MolGAN

by nicola-decao

nicola-decao / MolGAN

Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs

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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](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: 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: Example code for using the library within a Tensorflow project. NOTE: these are NOT the experiments on the paper!
  • models: Class for Models. Both VAE and (W)GAN are implemented.
  • optimizers: Class for Optimizers for both VAE, (W)GAN and RL.

Usage

Please have a look at the example.

Please cite [1] in your work when using this library in your experiments.

Feedback

For questions and comments, feel free to contact Nicola De Cao.

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} }

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