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Conditional Molecule Generator

This repository contains the source code and data sets for the graph based molecule generator discussed in the article "Multi-Objective De Novo Drug Design with Conditional Graph Generative Model" (https://arxiv.org/abs/1801.07299).

Briefly speaking, we used conditional graph convolution to structure the generative model. The properties of output molecules can then be controlled using the conditional code.

Requirements

This repo is built using Python 2.7, and utilizes the following packages:

To ease the installation process, please use the dockerfile environment defined in the Dockerfile.

Quick start

Project structure

Usage

To train the model, first unpackdatasets.tar.gz (download here) to the current directory, and call:

./train.py {molmp|molrnn|scaffold|prop|kinase} path/to/output

Where {molmp|molrnn|scaffold|prop|kinase} are model types, and path/to/output is the directory where you want to save the model's checkpoint file and log files. The following call:

./train.py {molmp|molrnn|scaffold|prop|kinase} -h

gives help for each model type.

For any questions | problems | criticisms | ...

Please contact me. Email: 1210307427@pku.edu.cn or kevinid4g@gmail.com