Rich Alexander S, Chan Yvonne H, Birnbaum Benjamin, Haider Kamran, Haimson Joshua, Hale Michael, Han Yongxin, Hickman William, Hoeflich Klaus P, Ortwine Daniel, Özen Ayşegül, Belanger David B
Inductive Bio, Inc., 550 Vanderbilt Ave, #730, Brooklyn, New York 11238, United States.
Nested Therapeutics, 1030 Mass Ave, Suite 410, Cambridge, Massachusetts 02138, United States.
ACS Med Chem Lett. 2024 Jul 25;15(8):1169-1173. doi: 10.1021/acsmedchemlett.4c00290. eCollection 2024 Aug 8.
Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.
优化化合物的吸收、分布、代谢和排泄（ADME）特性以及药代动力学（PK）概况是任何药物化学研究中发现未来临床候选药物的关键活动之一。找到加快解决ADME/PK缺陷的过程并减少待合成化合物数量的方法非常有价值。本文提供了关于使用机器学习ADME模型指导小分子先导化合物优化中化合物设计的实用指南和案例研究。这些指南强调，机器学习模型不能在真空中产生影响：当它们获得用户信任、根据项目需求进行调整并以补充和增强化学家专业知识的方式融入决策过程时，它们有助于推动项目进展。
