ReLMole

Supplement of code for "ReLMole: Molecular Representation Learning based on Two-Level Graph Similarities"

Requirements

We implement our model on Python 3.7. These packages are mainly used:

torch                     1.7.1
torch-cluster             1.5.8              
torch-geometric           1.6.3
torch-scatter             2.0.5 
torch-sparse              0.6.8
torch-spline-conv         1.2.0
numpy                     1.19.5
scikit-learn              0.24.1
rdkit                     2020.09.1.0
deepchem                  2.5.0

Dataset

1. Pre-training dataset

We use 250k "lead-like" compounds from ZINC15, which is available in DeepChem package. You get get it by calling deepchem.molnet.load_zinc15() function. We convert the dataset into text format and saved it into data/ZINC15/zinc15_250k.

2. Molecular property datasets

We load MoleculeNet datasets using DeepChem package.

3. DDI datasets

We download the DDI datasets from CASTER. The splitting results used in ReLMole are available in directory data/DDI

Experiments

1. FG corpus generating

Run gen_fg_corpus.py to generate FG corpus and the corpus file will be saved into data/ZINC15/fg_corpus.txt.

2. CDF of similarities of two-level graphs

Run sim_cdf.py to sample molecule pairs from the pre-training dataset and plot the CDF curve of two-level similarities. The figures will be saved in directory data/ZINC15.

3. Pre-training

Run pretrain_cl.py to pre-train ReLMole and the pre-trained model will be saved in directory pretrained_model_cl_zinc15_250k.

4. Fine-tuning

For the molecular property prediction task, run task_property/run_${dataset}.py to fine-tune the pre-trained model.
For the DDI prediction task, run task_ddi/run_ddi.py to fine-tune the pre-trained model.
We apply fine-tuned models for each dataset in directory finetuned.