Pharmaceutical Research

, Volume 33, Issue 2, pp 433–449 | Cite as

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

  • Alexander L. Perryman
  • Thomas P. Stratton
  • Sean Ekins
  • Joel S. Freundlich
Research Paper



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.

Graphical Abstract

Key Words

Bayesian model machine learning metabolic stability mouse liver microsomal stability translational research 



Absorption metabolism, distribution, excretion and toxicity


Collaborative Drug Discovery


Molecular function class fingerprints of maximum diameter 6


Human liver microsomal stability


High Throughput Screens


Mycobacterium tuberculosis


positive predictive value


Quantitative Structure-Activity Relationships


Receiver-operator characteristic


Structure Activity Relationship


Support Vector Machine



J.S.F., S.E., and A.L.P. were supported by Award Number 1U19AI109713 NIH/NIAID for the “Center to develop therapeutic countermeasures to high-threat bacterial agents,” from the National Institutes of Health: Centers of Excellence for Translational Research (CETR). S.E. and J.S.F. were also supported in part by Award Number 9R44TR000942-02 “Biocomputation across distributed private datasets to enhance drug discovery” from the National Center for Advancing Translational Sciences. We thank Dr. John Piwinski for suggesting that an MLM t1/2 of ≥60 min was ideal, but a t1/2 of ≥30 min was not significantly unfavorable. S.E. kindly acknowledges Alex Clark, Molecular Materials Informatics, Inc. and Krishna Dole and colleagues at Collaborative Drug Discovery, Inc., for their development of CDD Models. We thank Thomas Mayo at BIOVIA (formerly known as Accelrys, Inc.) for providing S.E. and J.S.F with Discovery Studio and Pipeline Pilot. We also thank Jodi Shaulsky at BIOVIA and Katalin Nadassy for assistance with setting up and maintaining the license server and Pipeline Pilot server. S.E. is a consultant for Collaborative Drug Discovery Inc.

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Alexander L. Perryman
    • 1
  • Thomas P. Stratton
    • 2
  • Sean Ekins
    • 3
    • 4
  • Joel S. Freundlich
    • 1
    • 2
  1. 1.Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging PathogensRutgers University–New Jersey Medical SchoolNewarkUSA
  2. 2.Department of Pharmacology & PhysiologyRutgers University-New Jersey Medical SchoolNewarkUSA
  3. 3.Collaborations in ChemistryFuquay-VarinaUSA
  4. 4.Collaborative Drug DiscoveryBurlingameUSA

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