SynFlowNet - Towards molecule design with garentueed synthesis pathways

Official implementation of SynFlowNet, a GFlowNet model with a synthesis action space, by Miruna Cretu, Charles Harris, Julien Roy, Emmanuel Bengio and Pietro Liò.

SynFlowNet learns how to generate molecules from chemical reactions and available building blocks. We implemented a new Environment, which defines an MDP which starts from an empty graph, followed by an Enamine building block. Stepping forward in the environment consists in running a reaction using RDKit. This repo contains a specific categorical distribution type for SynFlowNet actions, ActionCategorical, and a GraphTransfomerReactionsGFN class for a model which outputs an ActionCategorical. This project builds upon the code provided by recursionpharma/gflownet, available under the MIT license. For a primer and repo overview visit recursionpharma/gflownet.

Installation

PIP

This package is installable as a PIP package, but since it depends on some torch-geometric package wheels, the --find-links arguments must be specified as well:

pip install -e . --find-links https://data.pyg.org/whl/torch-2.1.2+cu121.html

Or for CPU use:

pip install -e . --find-links https://data.pyg.org/whl/torch-2.1.2+cpu.html

Reproducing results

Data

The training relies on two data sources: modified Hartenfeller-Button reaction templates and Enamine building blocks. The building blocks are not freely available and can be obtained upon request from enamine.net/building-blocks/building-blocks-catalog. We used the "Global stock" data and selected 6000 random molecules from here to train the model. After pre-processing the building blocks files using the scripts in src/gflownet/data/building_blocks, make sure that the file names match those in src/gflownet/tasks/config.py.

Training

The model can be trained using the sEH binding affinity proxy as reward by running src/gflownet/tasks/seh_reactions.py. You may want to change the default configuration in main(). The reward proxy is imported from src/gflownet/models/bengio2021flow.py.

Citation

If you use this code in your research, please cite the following paper:

@article{cretu2024synflownet,
      title={SynFlowNet: Towards Molecule Design with Guaranteed Synthesis Pathways},
      author={Miruna Cretu and Charles Harris and Julien Roy and Emmanuel Bengio and Pietro Liò},
      journal={arXiv preprint arXiv},
      year={2024}
}