ArticleCell reports methods2022
Machine learning prediction of side effects for drugs in clinical trials.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed.
- RAG-based architectures for drug side effect retrieval using compact LLMs.Scientific reports · 2026Article
- Advances in cyclotide research: bioactivity to cyclotide-based therapeutics.Molecular diversity · 2025Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Development of a genetic priority score to predict drug side effects using human genetic evidence.Nature communications · 2025Article
- AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction.ACS omega · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Computational drug repurposing: approaches, evaluation of in silico resources and case studies.Nature reviews. Drug discovery · 2025Review
- An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results.Scientific data · 2025Article
- An Integrated Approach for Representing Knowledge on the Potential of Drugs to Cause Acute Kidney Injury.Drug safety · 2025Article
- Preclinical side effect prediction through pathway engineering of protein interaction network models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.Briefings in bioinformatics · 2024Review
- Digital Technology Applications in the Management of Adverse Drug Reactions: Bibliometric Analysis.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects.Frontiers in drug safety and regulation · 2023Article
- Machine learning prediction of side effects for drugs in clinical trials.Cell reports methods · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.
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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.