Data-Efficient Graph Grammar Learning for Molecular Generation

This repository contains the implementation code for paper Data-Efficient Graph Grammar Learning for Molecular Generation (ICLR 2022 oral).

In this work, we propose a data-efficient generative model (DEG) that can be learned from datasets with orders of magnitude smaller sizes than common benchmarks. At the heart of this method is a learnable graph grammar that generates molecules from a sequence of production rules. Our learned graph grammar yields state-of-the-art results on generating high-quality molecules for three monomer datasets that contain only ∼20 samples each.

Installation

Prerequisites

• Retro*: The training of our DEG relies on Retro* to calculate the metric. Follow the instruction here to install.

• Pretrained GNN: We use this codebase for the pretrained GNN used in our paper. The necessary code & pretrained models are built in the current repo.

Conda

You can use conda to install the dependencies for DEG from the provided environment.yml file, which can give you the exact python environment we run the code for the paper:

git clone git@github.com:gmh14/data_efficient_grammar.git
cd data_efficient_grammar
conda env create -f environment.yml
conda activate DEG
pip install -e retro_star/packages/mlp_retrosyn
pip install -e retro_star/packages/rdchiral

Note: it may take a decent amount of time to build necessary wheels using conda.

Install Retro*:

• Download and unzip the files from this link, and put all the folders (dataset/, one_step_model/ and saved_models/) under the retro_star directory.

• Install dependencies:

conda deactivate
conda env create -f retro_star/environment.yml
conda activate retro_star_env
pip install -e retro_star/packages/mlp_retrosyn
pip install -e retro_star/packages/rdchiral
pip install setproctitle

Train

For Acrylates, Chain Extenders, and Isocyanates,

conda activate DEG
python main.py --training_data=./datasets/**dataset_path**

where **dataset_path** can be acrylates.txt, chain_extenders.txt, or isocyanates.txt.

For Polymer dataset,

conda activate DEG
python main.py --training_data=./datasets/polymers_117.txt --motif

Since Retro* is a major bottleneck of the training speed, we separate it from the main process, run multiple Retro* processes, and use file communication to evaluate the generated grammar during training. This is a compromise on the inefficiency of the built-in python multiprocessing package. We need to run the following command in another terminal window,

conda activate retro_star_env
bash retro_star_listener.sh **num_processes**

Note: opening multiple Retro* is EXTREMELY memory consuming (~5G each). We suggest to start from using only one process by bash retro_star_listener.sh 1 and monitor the memory usage, then accordingly increase the number to maximize the efficiency. We use 35 in the paper.

After finishing the training, to kill all the generated processes related to Retro*, run

killall retro_star_listener

Use DEG

Download and unzip the log & checkpoint files from this link. See visualization.ipynb for more details.

Acknowledgements

The implementation of DEG is partly based on Molecular Optimization Using Molecular Hypergraph Grammar and Hierarchical Generation of Molecular Graphs using Structural Motifs .

Citation

If you find the idea or code useful for your research, please cite our paper:

@inproceedings{guo2021data,
  title={Data-Efficient Graph Grammar Learning for Molecular Generation},
  author={Guo, Minghao and Thost, Veronika and Li, Beichen and Das, Payel and Chen, Jie and Matusik, Wojciech},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

Contact

Please contact guomh2014@gmail.com if you have any questions. Enjoy!