ArticleBMC bioinformatics2018
Predicting adverse drug reactions through interpretable deep learning framework.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models for adverse drug reaction prediction. A systematic review and three-level multilevel meta-analysis.Frontiers in pharmacology · 2026Pooled it
- Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective.Journal of medical Internet research · 2026Article
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Informative relational learning for adverse reaction prediction with enhanced generalization to novel drugs.Bioinformatics (Oxford, England) · 2026Article
- Review
- MGPT: A Multi-task Graph Prompt Learning Framework for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- ToxBERT: an explainable AI framework for enhancing prediction of adverse drug reactions and structural insights.Journal of pharmaceutical analysis · 2025Article
- An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results.Scientific data · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.Scientific reports · 2024Article
- Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- The temporal association between adverse drug reactions and antirheumatic drugs utilisation in Western Australia: a retrospective study from real-world data (1995-2015).Rheumatology international · 2024Article
- HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.BMC bioinformatics · 2024Article
- Artificial intelligence in the field of pharmacy practice: A literature review.Exploratory research in clinical and social pharmacy · 2023Review
- In vitro evaluation of 2-(1H-pyrazol-1-yl)-1,3,4-thiadiazole derivatives against replicative and infective stages of Trypanosoma cruzi.Journal of bioenergetics and biomembranes · 2023Article
- Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.Journal of chemical information and modeling · 2023Article
- Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs.Bioengineering (Basel, Switzerland) · 2023Article
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.
methodsIn this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.
resultsWe evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.
conclusionsThe deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.
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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.