ArticleJournal of chemical information and modeling2012
RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes.
Article in Journal of chemical information and modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.Pharmaceutics · 2023Review
- Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism.Drug discovery today · 2023Review
- Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.Bioinformatics (Oxford, England) · 2023Article
- Pharmacokinetic Study and Metabolite Identification of 1-(3'-bromophenyl)-heliamine in Rats.Pharmaceuticals (Basel, Switzerland) · 2022Article
- CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.Molecules (Basel, Switzerland) · 2021Article
- Molecular docking analysis of modified gedunin from neem with snake venom enzymes.Bioinformation · 2021Article
- Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry.The journal of physical chemistry. A · 2020Article
- Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.Journal of chemical information and modeling · 2020Article
- XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.Journal of chemical information and modeling · 2020Article
- The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors.Journal of chemical information and modeling · 2020Article
- Towards the Development of an In vivo Chemical Probe for Cyclin G Associated Kinase (GAK).Molecules (Basel, Switzerland) · 2019Article
- Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database.ACS medicinal chemistry letters · 2019Article
- Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.Chemical research in toxicology · 2018Article
- RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules.Chemistry Central journal · 2017Article
- Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism.Chemical research in toxicology · 2017Article
- Article
- Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System.International journal of molecular sciences · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
RS-Predictor is a tool for creating pathway-independent, isozyme-specific, site of metabolism (SOM) prediction models using any set of known cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study, we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly accessible collection of P450 ligands and metabolites released to date. A comprehensive investigation into the importance of different descriptor classes for identifying the regioselectivity mediated by each isozyme is made through the generation of multiple independent RS-Predictor models for each set of isozyme substrates. Two of these models include a density functional theory (DFT) reactivity descriptor derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp are shown to have stronger performance than either method alone, while also exceeding the accuracy of the commercial regioselectivity prediction methods distributed by Optibrium and Schrödinger, correctly identifying a large proportion of the metabolites in each substrate set within the top two rank-positions: 1A2 (83.0%), 2A6 (85.7%), 2B6 (82.1%), 2C19 (86.2%), 2C8 (83.8%), 2C9 (84.5%), 2D6 (85.9%), 2E1 (82.8%), 3A4 (82.3%), and merged (86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs.
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