Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study

• Published in: ACS medicinal chemistry letters 2024-08, Vol.15 (8), p.1169-1173
• Main Authors: 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.
• Format: Article
• Language: English
• Subjects: Viewpoint
• ISSN: 1948-5875; 1948-5875
• DOI: 10.1021/acsmedchemlett.4c00290

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.