Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)based drug discovery companies have attempted to build datadriven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.

Automated summary

Drug discovery aims to address unmet clinical needs by selecting appropriate targets and drug candidates. Recent developments in artificial intelligence and machine learning have led to the creation of data-driven platforms covering the entire drug discovery process. Identifying elusive targets increases the diversity of discovery pipelines and enhances the ability to meet these needs. Modern machine learning technologies complement traditional computer-aided drug discovery by accelerating candidate optimization. This review summarizes the latest advancements in AI/ML methods from target identification to molecule optimization and provides an overview of current trends in end-to-end AI/ML platforms.