The future of machine learning for small-molecule drug discovery will be driven by data

Abstract

Many studies have prophesied that the integration of machine learning techniques into small-molecule therapeutics development will help to deliver a true leap forward in drug discovery. However, increasingly advanced algorithms and novel architectures have not always yielded substantial improvements in results. In this Perspective, we propose that a greater focus on the data for training and benchmarking these models is more likely to drive future improvement, and explore avenues for future research and strategies to address these data challenges. The application of machine learning techniques to small-molecule drug discovery has not yet yielded a true leap forward in the field. This Perspective discusses how a renewed focus on data and validation could help unlock machine learning’s potential.