Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data
Open accessAbstract
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 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.
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The Bayesian approach has been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability, Caco-2 cell permeability, cytotoxicity and interactions with transporters.
“This Bayesian approach has also been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability42, Caco-2 cell permeability43, cytotoxicity44 and interactions with transporters45.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [42]
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Self-citation
- Authors
- Perryman, A. L., Stratton, T. P., Ekins, S., & Freundlich, J. S.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Collaborative Drug Discovery (United States) (US)
- Corresponding author
- Freundlich, Joel S.
- Venue
- Pharmaceutical Research
- Published
- 2015-09-28
- Volume
- 33
- Issue
- 2
- Pages
- 433-449
- ISSN
- 0724-8741 (ISSN-L)
- 1573-904X
- PMC
- PMC4712113
- OpenAlex
- W2179414561
- Cited by
- 50
- References
- 92
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 11 · 2020: 6 · 2019: 4 · 2018: 4 · 2017: 4 · 2016: 6
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Pharmaceutical Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R44TR000942)
- National Institute of Allergy and Infectious Diseases (U19AI109713)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- drug metabolism prediction, half-life prediction, small molecules, data pruning, Bayesian model, antitubercular agents, PubChem, metabolic stability
- MeSH terms
- Machine Learning, Animals, Bayes Theorem, Pharmaceutical Preparations, Microsomes, Liver, Models, Biological, Principal Component Analysis, Mice, Small Molecule Libraries, Drug Discovery, Databases, Pharmaceutical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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