ArticleBioinformatics (Oxford, England)2022
LaGAT: link-aware graph attention network for drug-drug interaction prediction.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Beyond Structural Representations: A Semantic-Relational Graph Neural Network for Polypharmacy Side Effect Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Enabling Drug-Drug Interaction Event Prediction with Multi-view-enhanced Chemical Structural Information.Interdisciplinary sciences, computational life sciences · 2026Article
- BioMNEDR: mechanism-guided network embedding for drug repurposing.Briefings in bioinformatics · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction.PloS one · 2026Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.Biomedical engineering online · 2025Review
- LSA-DDI: Learning Stereochemistry-Aware Drug Interactions via 3D Feature Fusion and Contrastive Cross-Attention.International journal of molecular sciences · 2025Article
- Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.Neuroscience bulletin · 2025Article
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- Article
- DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.BMC biology · 2024Article
- A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2024Article
- Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning.Communications medicine · 2024Article
- Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion.Frontiers in pharmacology · 2024Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
motivationDrug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions.
resultsTo address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction. AVAILABILITYAND IMPLEMENTATION: The source code and data are available at https://github.com/Azra3lzz/LaGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.