Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

This is the official implementation of junction tree encoder-decoder model in https://arxiv.org/abs/1812.01070

Requirements

The code has been tested under python 2.7 with pytorch 0.4.1.

The tutorial of training and testing our variational junction tree encoder-decoder is in diff_vae/README.md.

A quick summary of different folders:

data/ contains the training, validation and test set of logP, QED and DRD2 tasks described in the paper.
fast_jtnn/ contains the implementation of junction tree encoder-decoder.
diff_vae/ includes the training and decoding script of variational junction tree encoder-decoder (README).
diff_vae_gan/ includes the training and decoding script of adversarial training module (README).
props/ is the property evaluation module, including penalized logP, QED and DRD2 property calculation.
scripts/ provides evaluation and data preprocessing scripts.