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Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study
Alexander S Rich1, Yvonne H Chan2, Benjamin Birnbaum1
1Inductive Bio, Inc., 550 Vanderbilt Ave, #730, Brooklyn, New York 11238, United States.
Abstract:
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.
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