ArticleNature communications2020
Predicting the frequencies of drug side effects.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models for adverse drug reaction prediction. A systematic review and three-level multilevel meta-analysis.Frontiers in pharmacology · 2026Pooled it
- Safety and Tolerability of Natural and Synthetic Cannabinoids in Older Adults: A Systematic Review and Meta-Analysis of Open-Label Trials and Observational Studies.Drugs & aging · 2021Pooled it
- Robust prediction of drug combination side effects in realistic settings.PLoS computational biology · 2026Article
- A linear interpretable model for drug target prediction.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
- Generative pretraining for drug molecule design with bidirectional structure-property optimization.Communications chemistry · 2026Article
- Explainable drug side effect prediction in central neural system via biologically informed graph neural network.Translational psychiatry · 2026Article
- RAG-based architectures for drug side effect retrieval using compact LLMs.Scientific reports · 2026Article
- Structure-Property Associations: Breaking Paradigms for Linking Chemical Structures and Biological Properties in Drug Discovery.ChemMedChem · 2026Article
- Machine learning prediction of pediatric adverse drug reactions using consensus-derived scarce data.Communications chemistry · 2025Article
- DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.Briefings in bioinformatics · 2025Article
- Development of a genetic priority score to predict drug side effects using human genetic evidence.Nature communications · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- Computational drug repurposing: approaches, evaluation of in silico resources and case studies.Nature reviews. Drug discovery · 2025Review
- A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.Bioinformatics (Oxford, England) · 2025Article
- Host centric drug repurposing for viral diseases.PLoS computational biology · 2025Article
- Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- sChemNET: a deep learning framework for predicting small molecules targeting microRNA function.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computationally predicting frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug for which a small number of side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We show that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration.
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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.