ArticleEBioMedicine2020
Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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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
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Metabolic Disturbances Associated with Beta-Blocker Therapy: New Insights from a Large-Scale Pharmacovigilance Study.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Multi-Output Probabilistic Prediction of Drug Side Effects Using Classical Machine Learning Algorithms.Pharmaceuticals (Basel, Switzerland) · 2026Article
- From Pharmacovigilance Signals to Mechanistic Phenotypes: Integrating ADMET, PK/PD, and Network Context to Interpret Antiviral Safety in Pregnancy.Pharmaceuticals (Basel, Switzerland) · 2026Article
- An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions.PloS one · 2026Article
- The Missing Enzymes: A Call to Update Pharmacological Profiling Practices for Better Drug Safety Assessment.Journal of medicinal chemistry · 2025Review
- Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Exploring chemical space for "druglike" small molecules in the age of AI.Frontiers in molecular biosciences · 2025Review
- Review
- Clinical Pharmacology of Asciminib: A Review.Clinical pharmacokinetics · 2024Review
- DOCK-PET: database of CNS kinetic parameters in the healthy human brain for existing PET tracers.Annals of nuclear medicine · 2024Article
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- The state of the art in secondary pharmacology and its impact on the safety of new medicines.Nature reviews. Drug discovery · 2024Review
- Article
- Predicting therapeutic and side effects from drug binding affinities to human proteome structures.iScience · 2024Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data.Drug safety · 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
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Evaluation of four machine learning models for signal detection.Therapeutic advances in drug safety · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 9 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.
methodsHere, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.
findingsBy evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs.
interpretationThese associations provide a comprehensive resource to support drug development and human biology studies.
fundingThis study was not supported by any formal funding bodies.
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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.