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.
- 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 rundata/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.