ArticleBioinformatics (Oxford, England)2023
Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- DeepCYP: an integrated deep learning web server for the holistic "pathway-site product" prediction of CYP450 metabolism.Nucleic acids research · 2026Article
- FAME3R: an efficient, practical and reliable open-source tool for predicting phase 1 and phase 2 sites of metabolism.Journal of cheminformatics · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Reaction-Guided Metabolomics Accelerates High-Throughput Annotation of Xenobiotic Metabolites for Human Exposome.Environmental science & technology · 2025Article
- Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification.Journal of chemical information and modeling · 2025Article
- Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.Journal of pharmaceutical analysis · 2025Review
- Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.Biomedical engineering online · 2025Review
- Automated Annotation of Sites of Metabolism from Biotransformation Data.Journal of chemical information and modeling · 2025Article
- MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways.PLoS biology · 2025Article
- Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.RSC chemical biology · 2025Review
- Molecular Structure Discovery for Untargeted Metabolomics Using Biotransformation Rules and Global Molecular Networking.Analytical chemistry · 2025Article
- Article
- Structure-based metabolite function prediction using graph neural networks.Bioinformatics advances · 2025Article
- Computational analysis of the gut microbiota-mediated drug metabolism.Computational and structural biotechnology journal · 2025Article
- D-CyPre: a machine learning-based tool for accurate prediction of human CYP450 enzyme metabolic sites.PeerJ. Computer science · 2024Article
- SelenzymeRF: updated enzyme suggestion software for unbalanced biochemical reactions.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationWhile traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes.
resultsThis article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways. AVAILABILITY AND IMPLEMENTATION: A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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