ArticleJournal of cheminformatics2022
Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- From data to decisions: The role of machine learning in precision ecological restoration.Ecological applications : a publication of the Ecological Society of America · 2026Review
- DeepCYP: an integrated deep learning web server for the holistic "pathway-site product" prediction of CYP450 metabolism.Nucleic acids research · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugs.NPJ precision oncology · 2026Article
- A comprehensive evaluation of advanced methods for identifying structural alerts using extensive toxicity data.Journal of cheminformatics · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- ArcDFI: Attention regularization guided by CYP450 interactions for predicting drug-food interactions.PLoS computational biology · 2025Article
- Borylated 5-Membered Ring Iminosugars: Detailed Nuclear Magnetic Resonance Spectroscopic Characterisation, and Method for Analysis of Anomeric and Boron Equilibria.Molecules (Basel, Switzerland) · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Article
- GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors.Journal of cheminformatics · 2024Article
- New 1,3,4-Thiadiazole Derivatives as α-Glucosidase Inhibitors: Design, Synthesis, DFT, ADME, and In Vitro Enzymatic Studies.ACS omega · 2024Article
- Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application.Acta pharmaceutica Sinica. B · 2024Article
- Designing cytochrome P450 enzymes for use in cancer gene therapy.Frontiers in bioengineering and biotechnology · 2024Review
- Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features.BMC bioinformatics · 2023Article
- Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications.Metabolites · 2023Review
- Antifungal Drug-Drug Interactions with Commonly Used Pharmaceutics in European Pediatric Patients with Acute Lymphoblastic Leukemia.Journal of clinical medicine · 2023Review
- Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder.International journal of environmental research and public health · 2023Article
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
Abstract
Drug-drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.
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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.