Current methods and challenges for deep learning in drug discovery

Abstract

Driven by rapid advances in computer hardware and publicly available datasets over the past decade, deep learning has achieved tremendous success in the transformation of many computational disciplines. These novel technologies have had considerable impact on computer-aided drug design as well, throughout all stages of the development pipeline. A flexible toolbox of neural architectures has been developed that are well-suited to represent the sequential, topological, or geometrical concepts of chemistry and biology; and that are able to either discriminate existing molecules or to generate new ones from scratch. For some biochemical prediction tasks, the state of the art has been advanced; however, for complex and practically relevant projects, the outcomes are less clear-cut. Current deep learning methods rely on massive amounts of labeled examples, but drug discovery data is comparatively limited in quantity and quality. These problems need to be resolved and existing sources used more effectively to demonstrate that deep learning can revolutionize the field in general.