Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning.

Nature microbiology·2026
Same author

Loss of essential outer membrane functions causes drug hypersensitization in <i>Acinetobacter baumannii</i> overexpressing multidrug efflux pumps.

mBio·2026
Same author

Rifamycin Structural Modifications Attenuate PXR Binding and CYP3A4 Induction.

Journal of medicinal chemistry·2026
Same author

Design of a Targeted Covalent Probe to Interrogate the DNA Polymerase Activity of Polθ.

ACS medicinal chemistry letters·2026
Same author

The Quantification of Drug Accumulation within Gram-Negative Bacteria.

ACS infectious diseases·2025
Same author

The Discovery of RP-2119: A Potent, Selective, and Orally Bioavailable Polθ ATPase Inhibitor.

Journal of medicinal chemistry·2025

Related Experiment Video

Updated: Apr 2, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

5.5K

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

Alexander L Perryman1, Thomas P Stratton2, Sean Ekins3,4

  • 1Division 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.

Pharmaceutical Research
|September 30, 2015
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict mouse liver microsomal stability, aiding drug discovery. A pruning strategy improved model accuracy for identifying stable drug compounds.

Keywords:
Bayesian modelmachine learningmetabolic stabilitymouse liver microsomal stabilitytranslational research

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.7K
Author Spotlight: Advancing Liver Regeneration Research through ALPPS Mouse Model
06:45

Author Spotlight: Advancing Liver Regeneration Research through ALPPS Mouse Model

Published on: January 19, 2024

1.5K

Related Experiment Videos

Last Updated: Apr 2, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

5.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.7K
Author Spotlight: Advancing Liver Regeneration Research through ALPPS Mouse Model
06:45

Author Spotlight: Advancing Liver Regeneration Research through ALPPS Mouse Model

Published on: January 19, 2024

1.5K

Area of Science:

  • Pharmacology and Toxicology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Mouse efficacy studies are crucial for translational research but are preceded by metabolic stability assessments.
  • Mouse liver microsomal (MLM) stability studies are an initial, though imperfect, model for predicting metabolic clearance.
  • Identifying compounds with good MLM stability is essential for advancing potential therapeutics.

Purpose of the Study:

  • To develop machine learning (ML) models for predicting MLM stability.
  • To enhance the identification of compounds with favorable metabolic stability profiles.
  • To improve the efficiency of early-stage drug discovery pipelines.

Main Methods:

  • Compiled a training dataset of 894 unique small molecules with published MLM half-life values from PubChem.
  • Constructed ML models, including Bayesian approaches, using the curated dataset.
  • Assessed model performance through internal cross-validation, external testing with antitubercular compounds, and independent validation with 571 diverse compounds.

Main Results:

  • A "pruning" strategy, removing moderately stable compounds, significantly improved model predictive power.
  • Bayesian ML models demonstrated the highest accuracy in identifying compounds with a half-life of ≥1 hour.
  • The models showed enhanced predictive value for MLM stability.

Conclusions:

  • The pruning strategy offers a generalizable method to improve test set enrichment for MLM stability.
  • Machine learning models, particularly Bayesian, provide enhanced predictive value for the MLM stability of small organic molecules.
  • This study represents a comprehensive application of ML to publicly available MLM data for drug development.