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MolGym: Reinforcement Learning for 3D Molecular Design

This repository allows to train reinforcement learning policies for designing molecules directly in Cartesian coordinates. The agent builds molecules by repeatedly taking atoms from a given bag and placing them onto a 3D canvas.

Check out our blog post for a gentle introduction. For more details, see our papers:

Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor N. C. Simm*, Robert Pinsler* and José Miguel Hernández-Lobato
Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 108, 2020.
http://proceedings.mlr.press/v119/simm20b.html

Symmetry-Aware Actor-Critic for 3D Molecular Design
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi and José Miguel Hernández-Lobato
International Conference on Learning Representations, 2021.
https://openreview.net/forum?id=jEYKjPE1xYN

Setup

Dependencies:

Install required packages and library itself:

pip install -r requirements.txt
pip install -e .

Note: Make sure that the CUDA versions associated with torch and torch-scatter match. Check the documentation if you run into any errors when installing torch-scatter.

Sparrow Setup

Sparrow can be installed using the conda package manager and is available on the conda-forge channel. To install the conda package manager we recommend the miniforge installer. If the conda-forge channel is not yet enabled, add it to your channels with

conda config --add channels conda-forge
conda config --set channel_priority strict

Once the conda-forge channel has been enabled, scine-sparrow-python can be installed with conda:

conda install scine-sparrow-python

Usage

You can use this code to train and evaluate reinforcement learning agents for 3D molecular design. We currently support running experiments given a specific bag (single-bag), a stochastic bag, or multiple bags (multi-bag).

Training

To perform the single-bag experiment with SF6, run

python3 scripts/run.py \
    --name=SF6 \
    --symbols=X,F,S \
    --formulas=SF6 \
    --min_mean_distance=1.10 \
    --max_mean_distance=2.10 \
    --bag_scale=5 \
    --beta=-10 \
    --model=covariant \
    --canvas_size=7 \
    --num_envs=10 \
    --num_steps=15000 \
    --num_steps_per_iter=140 \
    --mini_batch_size=140 \
    --save_rollouts=eval \
    --device=cuda \
    --seed=1

Hyper-parameters for the other experiments can be found in the papers.

Evaluation

To generate learning curves, run the following command:

python3 scripts/plot.py --dir=results

Running this script will automatically generate a figure of the learning curve.

To write out the generated structures, run the following command:

python3 scripts/structures.py --dir=data --symbols=X,F,S

You can visualize the structures in the generated XYZ file using, for example, PyMOL.

Citation

If you use this code, please cite our papers:

@inproceedings{Simm2020Reinforcement,
  title = {Reinforcement Learning for Molecular Design Guided by Quantum Mechanics},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  author = {Simm, Gregor N. C. and Pinsler, Robert and {Hern{\'a}ndez-Lobato}, Jos{\'e} Miguel},
  editor = {III, Hal Daum{\'e} and Singh, Aarti},
  year = {2020},
  volume = {119},
  pages = {8959--8969},
  publisher = {{PMLR}},
  series = {Proceedings of Machine Learning Research}
  url = {http://proceedings.mlr.press/v119/simm20b.html}
}

@inproceedings{Simm2021SymmetryAware,
  title = {Symmetry-Aware Actor-Critic for 3D Molecular Design},
  author = {Gregor N. C. Simm and Robert Pinsler and G{\'a}bor Cs{\'a}nyi and Jos{\'e} Miguel Hern{\'a}ndez-Lobato},
  booktitle = {International Conference on Learning Representations},
  year = {2021},
  url = {https://openreview.net/forum?id=jEYKjPE1xYN}
}