Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.

药物发现和药物开发工作是费力、昂贵且耗时的。这些计划可能需要长达 12 年以上的时间，耗资 25 亿美元，失败率超过 90%。机器学习 (ML) 提供了改进药物发现过程的机会。事实上，随着公共和私人大规模生物和化学数据集的日益丰富，机器学习技术正成为增强传统药物开发过程的有用工具。在本视角中，我们讨论了药物发现的整个临床前阶段的算法方法的整合。具体来说，我们重点介绍了跨不同疾病领域的一系列基于机器学习的努力，以加速初始命中发现、作用机制 (MOA) 阐明和化学性质优化。随着机器学习在不同治疗领域的应用取得进展，我们认为完全集成机器学习的药物发现管道将定义药物开发项目的未来。