ArticleACS medicinal chemistry letters2019
Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database.
Article in ACS medicinal chemistry letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- MetabolEx: Platform for Small-Molecule Metabolites Generation and Metabolic Pathway Prediction.ACS measurement science au · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Prediction of UGT-mediated phase II metabolism via ligand- and structure-based predictive models.Journal of cheminformatics · 2025Article
- MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.International journal of molecular sciences · 2023Article
- Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives.Pharmaceutics · 2023Review
- Considerations for Improving Metabolism Predictions forFrontiers in toxicology · 2022Review
- MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database.Molecules (Basel, Switzerland) · 2021Article
- MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation.Molecules (Basel, Switzerland) · 2021Article
- Predicting reactivity to drug metabolism: beyond P450s-modelling FMOs and UGTs.Journal of computer-aided molecular design · 2021Article
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even though glucuronidations are the most frequent metabolic reactions of conjugation, both in quantitative and qualitative terms, they have rather seldom been investigated using computational approaches. To fill this gap, we have used the manually collected MetaQSAR metabolic reaction database to generate two models for the prediction of UGT-mediated metabolism, both based on molecular descriptors and implementing the Random Forest algorithm. The first model predicts the occurrence of the reaction and was internally validated with a Matthew correlation coefficient (MCC) of 0.76 and an area under the ROC curve (AUC) of 0.94, and further externally validated using a test set composed of 120 additional xenobiotics (MCC of 0.70 and AUC of 0.90). The second model distinguishes between O- and N-glucuronidations and was optimized by the random undersampling procedure to improve the predictive accuracy during the internal validation, with the recall measure of the minority class increasing from 0.55 to 0.78.
Identifiers
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Registered trials
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