ArticlePloS one2013
Detection of drug-drug interactions by modeling interaction profile fingerprints.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 137 citations in OpenAlex.
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach.Journal of integrative bioinformatics · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Article
- MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction.Computational and structural biotechnology journal · 2025Article
- MSDAFL: molecular substructure-based dual attention feature learning framework for predicting drug-drug interactions.Bioinformatics (Oxford, England) · 2024Article
- Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion.Frontiers in pharmacology · 2024Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks.BMC bioinformatics · 2023Article
- PRID: Prediction Model Using RWR for Interactions between Drugs.Pharmaceutics · 2023Article
- MSResG: Using GAE and Residual GCN to Predict Drug-Drug Interactions Based on Multi-source Drug Features.Interdisciplinary sciences, computational life sciences · 2023Article
- Analysis and identification of drug similarity through drug side effects and indications data.BMC medical informatics and decision making · 2023Article
- Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.Journal of cheminformatics · 2022Article
- Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships.PloS one · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- Article
- Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
- A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2020Article
- DPDDI: a deep predictor for drug-drug interactions.BMC bioinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4-0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.
Indexed as
Identifiers
What OpenQuestion holds
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.