ArticleNature protocols2014
Similarity-based modeling in large-scale prediction of drug-drug interactions.
Article in Nature protocols, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 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
87 citing papers in PubMed, 277 citations in OpenAlex.
- Tail-aware heterogeneous graph neural networks for multi-class drug-drug interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Qualitative and quantitative in silico toxicity profiling of "angel dust": phencyclidine (PCP) analogues as new psychoactive substances (3-HO-PCP, 3-MeO-PCP, 4-MeO-PCP, 3-HO-PCE, 3-MeO-PCE, 4-MeO-PCE).Archives of toxicology · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Characterizing clinical toxicity in cancer combination therapies.Bioinformatics (Oxford, England) · 2026Article
- Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction.PloS one · 2026Article
- ZSCAN-DDIE: an interpretable zero-shot learning method for the prediction of drug-drug interaction events using biomedical text.Bioinformatics advances · 2026Article
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- 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
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- Are We Considering All the Potential Drug-Drug Interactions in Women's Reproductive Health? A Predictive Model Approach.Pharmaceutics · 2025Article
- Measurement and prediction of small molecule retention by Gram-negative bacteria based on a large-scale LC/MS screen.Scientific reports · 2025Article
- Identify drug-drug interactions via deep learning: A real world study.Journal of pharmaceutical analysis · 2025Article
- Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction.Computational and structural biotechnology journal · 2025Article
- Self-Supervised Based Multi-View Graph Presentation Learning for Drug-Drug Interaction Prediction.Transactions on artificial intelligence · 2025Article
- Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges.Frontiers in pharmacology · 2025Review
- Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability.Journal of cheminformatics · 2024Article
27 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
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.
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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.