ArticleMolecular systems biology2013
Systematic identification of proteins that elicit drug side effects.
Article in Molecular systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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.
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Who cites it
58 citing papers in PubMed, 125 citations in OpenAlex.
- Phenotypic similarity of adverse drug reactions and disease phenotypes is a bridge to mechanistic discovery.npj drug discovery · 2025Article
- Exploring common mechanisms of adverse drug reactions and disease phenotypes through network-based analysis.Cell reports methods · 2025Article
- DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.bioRxiv : the preprint server for biology · 2024Article
- D'or: deep orienter of protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Polypharmacology: promises and new drugs in 2022.Pharmacological reports : PR · 2023Review
- A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs.Nature communications · 2023Article
- Large-scale prediction of adverse drug reactions-related proteins with network embedding.Bioinformatics (Oxford, England) · 2023Article
- Study on the Relationship Between the Number of Adverse Drug Reactions of Essential Drugs and Visits: Based on Vector Autoregressive Model.Risk management and healthcare policy · 2023Article
- Identification of New Toxicity Mechanisms in Drug-Induced Liver Injury through Systems Pharmacology.Genes · 2022Article
- Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports.CPT: pharmacometrics & systems pharmacology · 2022Review
- Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning.Frontiers in bioinformatics · 2022Article
- Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships.Database : the journal of biological databases and curation · 2021Article
- e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.Nucleic acids research · 2021Article
- Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.Nature biotechnology · 2020Article
- Biological representation of chemicals using latent target interaction profile.BMC bioinformatics · 2019Article
- Learning important features from multi-view data to predict drug side effects.Journal of cheminformatics · 2019Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Massive computational acceleration by using neural networks to emulate mechanism-based biological models.Nature communications · 2019Article
- Optimal control nodes in disease-perturbed networks as targets for combination therapy.Nature communications · 2019Article
- Network-based characterization of drug-protein interaction signatures with a space-efficient approach.BMC systems biology · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.
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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.