Pytorch implementation of MolGAN: An implicit generative model for small molecular graphs (https://arxiv.org/abs/1805.11973)
This library refers to the following two source code.

• nicola-decao/MolGAN
• yunjey/StarGAN

• python>=3.5
• pytroch>=0.4.1: https://pytorch.org
• rdkit: https://www.rdkit.org
• numpy

• 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 data/sparse_molecular_dataset.py to conver the dataset in a graph format used by MolGAN models).
• models: Class for Models.

python main.py

[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}
}

Work In Progress.