Improvement on Generative Adversarial Network for Targeted Drug Design

Abstract

This paper provides a generative network framework that can replicate the molecular space distribution to satisfy a set of desirable features. The approach incorporates two effective machine learning techniques: an Encoder-Decoder architecture that converts the string notations of molecules into latent space and a generative adversarial network to learn the data distribution and generate new compounds. We train this joint model on a dataset that includes stereo-chemical information. The results show an improvement in the Encoder-Decoder performance, reaching 89% of correctly reconstructed molecules. The framework can generate a wide variety of compounds biased towards specific molecular properties using Transfer Learning.