ArticleNucleic acids research2015
ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- ToxiGuard: an AOP-guided mechanistically interpretable framework for multi-organ toxicity prediction.Archives of toxicology · 2026Article
- Informative relational learning for adverse reaction prediction with enhanced generalization to novel drugs.Bioinformatics (Oxford, England) · 2026Article
- KAN-PROSPECT: a Kolmogorov-Arnold Networks-integrated framework for predicting the effects and adverse reactions of natural products via transfer learning.Scientific reports · 2026Article
- PromptSE: drug side effect prediction with LLM-derived pharmacological representations.Scientific reports · 2026Article
- PersADE: a database of personalized adverse drug events and their underlying molecular mechanisms.Nucleic acids research · 2026Article
- MSAT: a FAERS-informed heterogeneous graph neural network for pharmacovigilance prediction of Chinese materia medica-associated adverse drug reactions.Frontiers in pharmacology · 2026Article
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- AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction.ACS omega · 2025Article
- InterPAD is a database of drug-drug interaction between phytochemicals and anticancer drugs.Scientific reports · 2025Article
- A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.Bioinformatics (Oxford, England) · 2025Article
- OPTUNA optimization for predicting chemical respiratory toxicity using ML models.Journal of computer-aided molecular design · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- Fusing multiplex heterogeneous networks using graph attention-aware fusion networks.Scientific reports · 2024Article
- HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.BMC bioinformatics · 2024Article
- Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.Journal of medical Internet research · 2024Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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9 authors.
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Abstract
Adverse drug reactions (ADRs) are noxious and unexpected effects during normal drug therapy. They have caused significant clinical burden and been responsible for a large portion of new drug development failure. Molecular understanding and in silico evaluation of drug (or candidate) safety in laboratory is thus so desired, and unfortunately has been largely hindered by misuse of ADR terms. The growing impact of bioinformatics and systems biology in toxicological research also requires a specialized ADR term system that works beyond a simple glossary. Adverse Drug Reaction Classification System (ADReCS; http://bioinf.xmu.edu.cn/ADReCS) is a comprehensive ADR ontology database that provides not only ADR standardization but also hierarchical classification of ADR terms. The ADR terms were pre-assigned with unique digital IDs and at the same time were well organized into a four-level ADR hierarchy tree for building an ADR-ADR relation. Currently, the database covers 6544 standard ADR terms and 34,796 synonyms. It also incorporates information of 1355 single active ingredient drugs and 134,022 drug-ADR pairs. In summary, ADReCS offers an opportunity for direct computation on ADR terms and also provides clues to mining common features underlying ADRs.
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