Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study
Year: 2024
Type: editorial
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.
Source: ACS Medicinal Chemistry Letters
Authors: Alexander S. Rich, Yvonne H. Chan, Benjamin Birnbaum, Kamran Haider, Joshua Haimson, Michael Hale, Yongxin Han, William Hickman, Klaus P. Hoeflich, Daniel Ortwine, Ayşegül Özen, David B. Belanger
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4400981370