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Constrained Graph Variational Autoencoders for Molecule Design
This repository contains our implementation of Constrained Graph Variational Autoencoders for Molecule Design (CGVAE).
@article{liu2018constrained,
title={Constrained Graph Variational Autoencoders for Molecule Design},
author={Liu, Qi and Allamanis, Miltiadis and Brockschmidt, Marc and Gaunt, Alexander L.},
journal={The Thirty-second Conference on Neural Information Processing Systems},
year={2018}
}
Requirements
This code was tested in Python 3.5 with Tensorflow 1.3. conda, docopt and rdkit are also necessary. A Bash script is provided to install all these requirements.
source ./install.sh
To evaluate SAS scores, use get_sascorer.sh to download the SAS implementation from rdkit
Data Extraction
Three datasets (QM9, ZINC and CEPDB) are in use. For downloading CEPDB, please refer to CEPDB.
For downloading QM9 and ZINC, please go to data directory and run get_qm9.py and get_zinc.py, respectively.
python get_qm9.py
python get_zinc.py
Running CGVAE
We provide two settings of CGVAE. The first setting samples one breadth first search path for each molecule. The second setting samples transitions from multiple breadth first search paths for each molecule.
To train and generate molecules using the first setting, use
python CGVAE.py --dataset qm9|zinc|cep
To avoid training and generate molecules with a pretrained model, use
python CGVAE.py --dataset qm9|zinc|cep --restore pretrained_model --config '{"generation": true}'
To train and generate molecules using the second setting, use
python CGVAE.py --dataset qm9|zinc|cep --config '{"sample_transition": true, "multi_bfs_path": true, "path_random_order": true}'
To use optimization in the latent space, set optimization_step to a positive number
python CGVAE.py --dataset qm9|zinc|cep --restore pretrained_model --config '{"generation": true, "optimization_step": 50}'
More configurations can be found at function default_params in CGVAE.py
Evaluation
To evaluate the generated molecules, use
python evaluate.py --dataset qm9|zinc|cep
Pretrained Models and Generated Molecules
<!-- We provide pretrained models and generated molecules for both settings. The following files are pretrained models ``` pretrained/qm9_setting1 pretrained/qm9_setting2 pretrained/zinc_setting1 pretrained/zinc_setting2 pretrained/cep_setting1 pretrained/cep_setting2 ``` The following files are generated molecules ``` molecules/generated_smiles_qm9_setting1 molecules/generated_smiles_qm9_setting2 molecules/generated_smiles_zinc_setting1 molecules/generated_smiles_zinc_setting2 molecules/generated_smiles_cep_setting1 molecules/generated_smiles_cep_setting2 ``` -->Generated molecules can be obtained upon request.
A program in folder molecules is provided to read and visualize the molecules
python visualize.py molecule_file output_file
Questions/Bugs
Please submit a Github issue or contact qiliu@u.nus.edu.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.