Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA)

Christian Kramer, Attilla Ting, Hao Zheng, Jerome Hert, Torsten Schindler, Martin Stahl, Graeme Robb, James J. Crawford, Jeff Blaney, Shane Montague, Andrew G. Leach, Al G. Dossetter, Ed J. Griffen

Research output: Contribution to journalArticlepeer-review

Abstract

The first large scale analysis of in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) data shared across multiple major pharma has been performed. Using advanced matched molecular pair analysis (MMPA), we combined data from three pharmaceutical companies and generated ADMET rules, avoiding the need to disclose the full chemical structures. On top of the very large exchange of knowledge, all companies involved synergistically gained approximately 20% more rules from the shared transformations. There is good quantitative agreement between the rules based on shared data compared to both individual companies’ rules and rules published in the literature. Known correlations between log D, solubility, in vitro clearance, and plasma protein binding also hold in transformation space, but there are also interesting exceptions. Data pools such as this allow focusing on particular functional groups and characterizing their ADMET profile. Finally the role of a corpus of robustly tested medicinal chemistry knowledge in the training of medicinal chemistry is discussed.
Original languageEnglish
Pages (from-to)3277-3292
JournalJournal of Medicinal Chemistry
Volume61
Issue number8
Early online date28 Sept 2017
DOIs
Publication statusPublished - 26 Apr 2018

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