JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

This repository contains code for the paper: JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design. By: AkshatKumar Nigam, Robert Pollice, Alán Aspuru-Guzik

Package Requirements:

• SELFIES
• RDKit
• Pytorch
• Python 3.0 or up
• numpy

Using The Code:

The code can be run using:

python ./JANUS.py

Within params_init.py, a user has the option to provide:

1. A function for calculting property values (see function calc_prop).
2. Input parameters that are to be used by JANUS (see function generate_params). Initial parameters are provided. These are picked based on prior experience by the authors of the paper.

Output Generation:

All results from running JANUS will be stored here. The following files will be created:

1. fitness_explore.txt: Fitness values for all molecules from the exploration component of JANUS.
2. fitness_local_search.txt: Fitness values for all molecules from the exploitation component of JANUS.
3. generation_all_best.txt: Smiles and fitness value for the best molecule encountered in every generation (iteration).
4. init_mols.txt: List of molecules used to initialte JANUS.
5. population_explore.txt: SMILES for all molecules from the exploration component of JANUS.
6. population_local_search.txt: SMILES for all molecules from the exploitation component of JANUS.

Paper Results/Reproducibility:

Our code and results for each experiment in the paper can be found here:

• Experiment 4.1: https://drive.google.com/file/d/1rscIyzpTvtyiEkoP1WsF-XtSHJGQStUU/view?usp=sharing
• Experiment 4.3: https://drive.google.com/file/d/1tlIdfSWwzVeJ5kZ98l8G6osE9zf9wP1f/view?usp=sharing

Questions, problems?

Make a github issue 😄. Please be as clear and descriptive as possible. Please feel free to reach out in person: (akshat[DOT]nigam[AT]mail[DOT]utoronto[DOT]ca, rob[DOT]pollice[AT]utoronto[DOT]ca)

License

Apache License 2.0