ArticleiScience2022
Machine learning and structure-based modeling for the prediction of UDP-glucuronosyltransferase inhibition.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Fraxetin Inhibits UGT1A1 and UGT1A9 Activities In Vitro: Inhibition Kinetics, Molecular Dynamics Simulation, and Prediction of Herb-Drug Interaction Risk.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.Journal of pharmaceutical analysis · 2025Review
- Herb-drug interactions in oncology: pharmacodynamic/pharmacokinetic mechanisms and risk prediction.Chinese medicine · 2025Review
- Machine Learning and Deep Learning Models for Predicting Noncovalent Inhibitors of AmpC β-Lactamase.ACS omega · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
UDP-glucuronosyltransferases (UGTs) are responsible for 35% of the phase II drug metabolism. In this study, we focused on UGT1A1, which is a key UGT isoform. Strong inhibition of UGT1A1 may trigger adverse drug/herb-drug interactions, or result in disorders of endobiotic metabolism. Most of the current machine learning methods predicting the inhibition of drug metabolizing enzymes neglect protein structure and dynamics, both being essential for the recognition of various substrates and inhibitors. We performed molecular dynamics simulations on a homology model of the human UGT1A1 structure containing both the cofactor- (UDP-glucuronic acid) and substrate-binding domains to explore UGT conformational changes. Then, we created models for the prediction of UGT1A1 inhibitors by integrating information on UGT1A1 structure and dynamics, interactions with diverse ligands, and machine learning. These models can be helpful for further prediction of drug-drug interactions of drug candidates and safety treatments.
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Registered trials
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