Authors: Alexander L Perryman Thomas P Stratton Sean Ekins Joel S Freundlich
Publish Date: 2015/09/28
Volume: 33, Issue: 2, Pages: 433-449
Abstract
Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases Although mouse liver microsomal MLM stability studies are not a perfect surrogate for in vivo studies of metabolic clearance they are the initial model system used to assess metabolic stability Consequently we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stabilityPublished assays on MLM halflife values were identified in PubChem reformatted and curated to create a training set with 894 unique small molecules These data were used to construct machine learning models assessed with internal crossvalidation external tests with a published set of antitubercular compounds and independent validation with an additional diverse set of 571 compounds PubChem data on percent metabolismOur results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources
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