ArticleScientific reports2015
Comprehensive prediction of drug-protein interactions and side effects for the human proteome.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis 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.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research.Nature communications · 2023Pooled it
- Target-biology and interactome-derived signatures predict target-level associations with safety-related drug attrition.Scientific reports · 2026Article
- PHENOCAUZ: Linking Human Symptoms, Drug Side Effects and Efficacy to Their Molecular Causes Using Mendelian Disease Biology.bioRxiv : the preprint server for biology · 2026Article
- Characterization of rutin bindingto HRAS and MAPK3 and its clinical prognostic relevancein lung cancer: Using in silico and clinical prognostic experimental design.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Advanced Proteomics Approaches Hold Potential for the Risk Assessment of Metabolism-Disrupting Chemicals as Omics-Based NAM: A Case Study Using the Phthalate Substitute DINCH.Environmental science & technology · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Computational investigation unveils pathogenic LIG3 non-synonymous mutations and therapeutic targets in acute myeloid leukemia.PloS one · 2025Article
- Review
- A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.Biomolecules · 2024Review
- Preclinical side effect prediction through pathway engineering of protein interaction network models.CPT: pharmacometrics & systems pharmacology · 2024Article
- FOTF-CPI: A compound-protein interaction prediction transformer based on the fusion of optimal transport fragments.iScience · 2024Article
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.BMC pharmacology & toxicology · 2024Article
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.bioRxiv : the preprint server for biology · 2023Article
- Network-Based Prediction of Side Effects of Repurposed Antihypertensive Sartans against COVID-19 via Proteome and Drug-Target Interactomes.Proteomes · 2023Article
- Machine learning prediction of side effects for drugs in clinical trials.Cell reports methods · 2022Article
- Recycled Translation: Repurposing Drugs for Stroke.Translational stroke research · 2022Review
- De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference.International journal of molecular sciences · 2022Article
- Identifying potential natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach.PLoS neglected tropical diseases · 2022Article
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE(comb), whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.
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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.