Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/microsoft/constrained-graph-variational-autoencoder
Sample code for Constrained Graph Variational Autoencoders
https://github.com/microsoft/constrained-graph-variational-autoencoder
Last synced: 1 day ago
JSON representation
Sample code for Constrained Graph Variational Autoencoders
- Host: GitHub
- URL: https://github.com/microsoft/constrained-graph-variational-autoencoder
- Owner: microsoft
- License: mit
- Created: 2018-07-19T10:57:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-03T02:50:57.000Z (over 1 year ago)
- Last Synced: 2024-08-06T20:07:59.353Z (3 months ago)
- Language: Python
- Size: 143 KB
- Stars: 227
- Watchers: 11
- Forks: 56
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
README
# Constrained Graph Variational Autoencoders for Molecule Design
This repository contains our implementation of [Constrained Graph Variational Autoencoders for Molecule Design](https://arxiv.org/abs/1805.09076) (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](https://github.com/rdkit/rdkit/tree/master/Contrib/SA_Score)
# Data Extraction
Three datasets (QM9, ZINC and CEPDB) are in use. For downloading CEPDB, please refer to [CEPDB](http://cleanenergy.molecularspace.org/).
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
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 [[email protected]](mailto:[email protected]).
# 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](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.