ArticleJournal of the American Medical Informatics Association : JAMIA2014
Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties.
Article in Journal of the American Medical Informatics Association : JAMIA, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 2 of them syntheses that pooled it.
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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
122 citing papers in PubMed, 2 syntheses or guidelines pooled it, 371 citations in OpenAlex.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Prospective adverse event risk evaluation in clinical trials.Health care management science · 2022Trial
- DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- Comparing Neural Networks and Naive Bayes in the Prediction of Drug Gene Interactions of Type 4 Collagenase for Gingival Epithelialization.International journal of dentistry · 2026Article
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- PrevCardioOncAI: Machine Learning Algorithms for Predicting Cardiovascular Disease in Cancer Survivors.Journal of the American Heart Association · 2025Observational
- A Domain Adaptive Interpretable Substructure-Aware Graph Attention Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks.Scientific reports · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Computational frameworks transform antagonism to synergy in optimizing combination therapies.NPJ digital medicine · 2025Review
62 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
2 authors at 1 institution in 1 country.
Funding
Abstract
objectiveDrug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials.
methodsHere we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively.
resultsThe area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources.
conclusionsThrough machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance.
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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.