Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

ABSTRACT:

Purpose

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 stability.

Methods

Published assays on MLM half-life 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 cross-validation, external tests with a published se

SUBMITTER: Perryman AL 

PROVIDER: S-EPMC4712113 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature