title: Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.
abstract: OBJECTIVE
  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 set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).
  
  
  RESULTS
  "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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA.
authors.forename: Alexander L
authors.initials: AL
authors.lastname: Perryman
authors.affiliation: Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA.
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Stratton
authors.affiliation: Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA.|Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, USA.
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
authors.affiliation: Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA. freundjs@rutgers.edu.|Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA. freundjs@rutgers.edu.
authors.forename: Joel S
authors.initials: JS
authors.lastname: Freundlich
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000379:methods*; D000069550:Machine Learning; D051379:Mice; D008862:Microsomes, Liver / Q000378:metabolism*; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism*; D025341:Principal Component Analysis; D054852:Small Molecule Libraries / Q000737:chemistry / Q000378:metabolism*
chemical_list: D004364:Pharmaceutical Preparations; D054852:Small Molecule Libraries
keywords: Bayesian model; machine learning; metabolic stability; mouse liver microsomal stability; translational research