https://github.com/nicola-decao/MolGAN

Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs
https://github.com/nicola-decao/MolGAN

Last synced: 12 days ago
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Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs

• Host: GitHub
• URL: https://github.com/nicola-decao/MolGAN
• Owner: nicola-decao
• License: mit
• Created: 2018-09-24T10:54:43.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-10-25T15:08:32.000Z (about 1 year ago)
• Last Synced: 2024-08-01T17:25:09.722Z (3 months ago)
• Language: Python
• Homepage: https://arxiv.org/abs/1805.11973
• Size: 32.2 KB
• Stars: 252
• Watchers: 8
• Forks: 85
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-graph-classification - [Python Reference

README

        
# MolGAN

Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs (https://arxiv.org/abs/1805.11973)

## Overview

This library contains a Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs as presented in [[1]](#citation)(https://arxiv.org/abs/1805.11973).

## Dependencies

* **python>=3.6**

* **tensorflow>=1.7.0**: https://tensorflow.org

* **rdkit**: https://www.rdkit.org

* **numpy**

* **scikit-learn**

## Structure

* [data](https://github.com/nicola-decao/MolGAN/tree/master/data): should contain your datasets. If you run `download_dataset.sh` the script will download the dataset used for the paper (then you should run `utils/sparse_molecular_dataset.py` to convert the dataset in a graph format used by MolGAN models).

* [example](https://github.com/nicola-decao/MolGAN/blob/master/example.py): Example code for using the library within a Tensorflow project. **NOTE: these are NOT the experiments on the paper!**

* [models](https://github.com/nicola-decao/MolGAN/tree/master/models): Class for Models. Both VAE and (W)GAN are implemented.

* [optimizers](https://github.com/nicola-decao/MolGAN/tree/master/optimizers): Class for Optimizers for both VAE, (W)GAN and RL.

## Usage

Please have a look at the [example](https://github.com/nicola-decao/MolGAN/blob/master/example.py).

Please cite [[1](#citation)] in your work when using this library in your experiments.

## Feedback

For questions and comments, feel free to contact [Nicola De Cao](mailto:[email protected]).

## License

MIT

## Citation

```

[1] De Cao, N., and Kipf, T. (2018).MolGAN: An implicit generative 

model for small molecular graphs. ICML 2018 workshop on Theoretical

Foundations and Applications of Deep Generative Models.

```

BibTeX format:

```

@article{de2018molgan,

  title={{MolGAN: An implicit generative model for small

  molecular graphs}},

  author={De Cao, Nicola and Kipf, Thomas},

  journal={ICML 2018 workshop on Theoretical Foundations 

  and Applications of Deep Generative Models},

  year={2018}

}

```