ArticleFrontiers in pharmacology2023
DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction.
Article in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 41 citations in OpenAlex.
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- AI-enabled fungal biomanufacturing: from genome mining to the artificial intelligence virtual cell.Mycology · 2026Review
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- 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
- MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors.Scientific reports · 2025Article
- ADCNet: a unified framework for predicting the activity of antibody-drug conjugates.Briefings in bioinformatics · 2025Article
- 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
- R2eGIN: Residual Reconstruction Enhanced Graph Isomorphism Network for Accurate Prediction of Poly (ADP-Ribose) Polymerase Inhibitors.Bioinformatics and biology insights · 2025Article
- Investigation ofComputational and structural biotechnology journal · 2024Review
- 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
- Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties.Archives of toxicology · 2024Article
- Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models.Pharmaceutics · 2024Review
- Decoding the Role of CYP450 Enzymes in Metabolism and Disease: A Comprehensive Review.Biomedicines · 2024Review
- Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges.Clinical pharmacokinetics · 2024Review
- Article
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Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors.
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Registered trials
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.