ArticleFrontiers in pharmacology2019
Deep Learning Based Drug Metabolites Prediction.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Advancing Wider Implementation of Multi-regional Clinical Trials in East Asia.Therapeutic innovation & regulatory science · 2026Review
- HazChemNet: A Deep Learning Model for Hazardous Chemical Prediction.International journal of molecular sciences · 2025Article
- BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs.Briefings in bioinformatics · 2025Article
- Machine learning: Python tools for studying biomolecules and drug design.Molecular diversity · 2025Review
- Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.Journal of pharmaceutical analysis · 2025Review
- Druggability Studies of Benzene Sulfonamide Substituted Diarylamide (E3) as a Novel Diuretic.Biomedicines · 2025Article
- Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals.Molecules (Basel, Switzerland) · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- The Combination of Electrochemistry and Microfluidic Technology in Drug Metabolism Studies.ChemistryOpen · 2022Review
- Review
- Applications of machine learning in computer-aided drug discovery.QRB discovery · 2022Review
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
- Structural Modification in Anesthetic Drug Development for Prodrugs and Soft Drugs.Frontiers in pharmacology · 2022Review
- Machine learning models in the prediction of drug metabolism: challenges and future perspectives.Expert opinion on drug metabolism & toxicology · 2021Article
- Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.Frontiers in artificial intelligence · 2020Review
- Deep metabolome: Applications of deep learning in metabolomics.Computational and structural biotechnology journal · 2020Review
- Network pharmacology and molecular docking investigation ofJournal of advanced pharmaceutical technology & researchArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.
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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.