Improvement on Generative Adversarial Network for Targeted Drug Design
Beatriz P. Santos, Maryam Abbasi, Tiago C. Pereira, Bernardete Ribeiro, Joel Perdiz Arrais · ESANN 2021 proceedings · 2021
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.