ArticlePharmaceutical research2016
Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.
Article in Pharmaceutical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Modifications of NU-9, a potent protein aggregation inhibitor. Properties and activity in a cellular model of amyotrophic lateral sclerosis.Bioorganic chemistry · 2025Article
- HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning.Bioinformatics (Oxford, England) · 2025Article
- MetaboGNN: predicting liver metabolic stability with graph neural networks and cross-species data.Journal of cheminformatics · 2025Article
- Employing Automated Machine Learning (AutoML) Methods to Facilitate theJournal of chemical information and modeling · 2025Article
- MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning.Briefings in bioinformatics · 2024Article
- Extended study on atomic featurization in graph neural networks for molecular property prediction.Journal of cheminformatics · 2023Article
- CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.Bioinformatics (Oxford, England) · 2023Article
- The Inhibitory Properties of a Novel, Selective LMTK3 Kinase Inhibitor.International journal of molecular sciences · 2023Article
- Generation of new inhibitors of selected cytochrome P450 subtypes-Computational and structural biotechnology journal · 2022Article
- Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.Journal of chemical information and modeling · 2021Article
- Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-ResistantACS infectious diseases · 2021Article
- Drug-Like Small Molecule HSP27 Functional Inhibitor Sensitizes Lung Cancer Cells to Gefitinib or Cisplatin by Inducing Altered Cross-Linked Hsp27 Dimers.Pharmaceutics · 2021Article
- Random Forest Model Prediction of Compound Oral Exposure in the Mouse.ACS pharmacology & translational science · 2021Article
- A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.ACS infectious diseases · 2021Article
- Pruned Machine Learning Models to Predict Aqueous Solubility.ACS omega · 2020Article
- Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.Pharmaceutical research · 2020Article
- Artificial intelligence in dermatology: past, present, and future.Chinese medical journal · 2019Article
- Exploiting machine learning for end-to-end drug discovery and development.Nature materials · 2019Article
- Naïve Bayesian Models for Vero Cell Cytotoxicity.Pharmaceutical research · 2018Article
- Novel Pyrimidines as Antitubercular Agents.Antimicrobial agents and chemotherapy · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeMouse 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.
methodsPublished 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.
conclusionsOur 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.