Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery?

INTRODUCTION

Deep learning approaches have become popular in recent years in de novo drug design. Generative models for molecule generation and optimization have shown promising results. Molecules trained on different chemical data could regenerate molecules that were similar to the query molecule, thus supporting lead optimization. Recurrent neural network-based generative models have demonstrated application in low-data drug discovery, fragment-based drug design and in lead optimization.

AREAS COVERED

In this review, we have provided an overview of recurrent neural network models and their variants for molecule generation with recent examples. The input representation of molecules as SMILES and molecular graphs have been discussed. The evaluation benchmarks and metrics used in generative neural network models are also highlighted. For this, ScienceDirect, Web of Science, and Google Scholar databases were searched with the article's keywords and their combinations to retrieve the most relevant and up-to-date information.

EXPERT OPINION

The simplicity of SMILES notation makes it suitable for training a sequence-based model such as a recurrent neural network. However, models that could be trained on molecular graphs to generate molecular structures which could be synthesized could open new possibility for valid molecule generation and synthetic feasibility.