ArticlePLoS computational biology2022
Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
- Artificial Intelligence-Based In Silico Evaluation of the Pharmacological Potential and In Vitro Anti-Malignancy Effect in the Human Glioblastoma Cell Line of the Hydrate of the Coumarin Compound Meranzin Originating From the Edible Macroalga (Food science & nutrition · 2026Article
- Small-Molecule Trk Agonists: Where Do We Go from Here?Journal of medicinal chemistry · 2025Review
- Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning.Journal of cheminformatics · 2025Article
- Employing Automated Machine Learning (AutoML) Methods to Facilitate theJournal of chemical information and modeling · 2025Article
- Insight into the Relationships Between Chemical, Protein and Functional Variables in the PBP/GOBP Family in Moths Based on Machine Learning.International journal of molecular sciences · 2025Article
- Repurposing COVID-19 Compounds (via MMV COVID Box): Almitrine and Bortezomib Induce Programmed Cell Death inPathogens (Basel, Switzerland) · 2025Article
- Machine Learning-BasedChemical research in toxicology · 2024Article
- GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors.Journal of cheminformatics · 2024Article
- Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase.ACS omega · 2024Article
- Marine Prostanoids with Cytotoxic Activity from OctocoralMarine drugs · 2024Article
- Anti-COVID Drugs (MMV COVID Box) as Leishmanicidal Agents: Unveiling New Therapeutic Horizons.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Designing cytochrome P450 enzymes for use in cancer gene therapy.Frontiers in bioengineering and biotechnology · 2024Review
- Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives.Pharmaceutics · 2023Review
- An Insight into the Metabolism of 2,5-Disubstituted Monotetrazole Bearing Bisphenol Structures: Emerging Bisphenol A Structural Congeners.Molecules (Basel, Switzerland) · 2023Article
- DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction.Frontiers in pharmacology · 2023Article
- Machine learning and structure-based modeling for the prediction of UDP-glucuronosyltransferase inhibition.iScience · 2022Article
- ABCG2/BCRP transport mechanism revealed through kinetically excited targeted molecular dynamics simulations.Computational and structural biotechnology journal · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.
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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.