Predicting Mouse Liver Microsomal Stability with 'Pruned' Machine Learning Models and Public Data
| Autor: | Thomas P. Stratton, Joel S. Freundlich, Alexander L. Perryman, Sean Ekins |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
0301 basic medicine
Computer science Databases Pharmaceutical Pharmacology toxicology Stability (learning theory) Pharmaceutical Science Model system Machine learning computer.software_genre Models Biological Article Machine Learning Small Molecule Libraries 03 medical and health sciences Mice In vivo Drug Discovery Animals Pharmacology (medical) Pharmacology Principal Component Analysis business.industry Organic Chemistry Bayes Theorem Metabolic stability 030104 developmental biology Pharmaceutical Preparations Microsomes Liver Molecular Medicine Artificial intelligence business computer Biotechnology |
| Popis: | 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.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 set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism)."Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.Our 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. |
| Databáze: | OpenAIRE |
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