ArticleNature communications2025
Predicting rare drug-drug interaction events with dual-granular structure-adaptive and pair variational representation.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MEGPNM as a multiscale edge-aware GAT network with hybrid pooling predicting permeability of non-peptidic macrocycles.Communications chemistry · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- DDI-HierPred: An Artificial Intelligence-Based Hierarchical PK/PD Platform for Drug-Drug Interaction Prediction.Pharmaceutics · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.BMC biology · 2026Article
- Unveiling rare drug interactions via a BioGPT-enhanced dual graph framework for robust pharmacovigilance.iScience · 2026Article
- FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.PLoS computational biology · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Polypharmacy and Drug-Drug Interaction Architecture in Hospitalized Cardiovascular Patients: Insights from Real-World Analysis.Biomedicines · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- Semantic-enhanced heterogeneous graph learning for identifying ncRNAs associated with drug resistance.Bioinformatics (Oxford, England) · 2026Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- Mapping the Global Research on Drug-Drug Interactions: A Multidecadal Evolution Through AI-Driven Terminology Standardization.Bioengineering (Basel, Switzerland) · 2025Article
- Predicting rare drug-drug interaction events with dual-granular structure-adaptive and pair variational representation.Nature communications · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Adverse drug-drug interaction events (DDIEs) pose serious risks to patient safety, yet rare but severe interactions remain challenging to identify due to limited clinical data. Existing computational methods rely heavily on abundant samples, failing to identify rare DDIEs. Here we introduce RareDDIE, a metric-based meta-learning model that employs a dual-granular structure-driven pair variational representation to enhance rare DDIE prediction. To further address the challenge of zero-shot DDIE identification, we develop the Biological Semantic Transferring (BST) module, integrating large-scale sentence embeddings to form the ZetaDDIE variant. Our model outperforms existing methods in few-sample and zero-sample settings. Furthermore, we verify that knowledge transfer from DDIE can improve drug synergy predictions, surpassing existing models. Case studies on antiplatelet activity reduction and non-small cell lung cancer drug synergy further illustrate the practical value of RareDDIE. By analyzing the meta-knowledge construction process, we provide interpretability into the model's decision-making. This work establishes an effective computational framework for rare DDIE prediction, leveraging meta-learning and knowledge transfer to overcome key challenges in data-limited scenarios.
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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 Socratic graph.