ArticleBMC biology2024
DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.
Article in BMC biology, 2024. 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.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Multimodal feature fusion for molecular property classification.Journal of cheminformatics · 2026Article
- Hgtsynergy: a transfer learning method for predicting anticancer synergistic drug combinations based on a drug-drug interaction heterogeneous graph.BMC bioinformatics · 2026Article
- Graph-based deep learning for drug-drug interaction prediction: a systematic review.Frontiers in chemistry · 2026Review
- Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes.Bioinformatics (Oxford, England) · 2025Article
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- Multimodal fusion with relational learning for molecular property prediction.Communications chemistry · 2025Article
- SCATrans: semantic cross-attention transformer for drug-drug interaction predication through multimodal biomedical data.BMC bioinformatics · 2025Article
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Computational discovery of natural medicines targeting adenosine receptors for metabolic diseases.Frontiers in pharmacology · 2025Article
- Advancing drug-drug interactions research: integrating AI-powered prediction, vulnerable populations, and regulatory insights.Frontiers in pharmacology · 2025Review
- Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery.Frontiers in genetics · 2025Article
- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges.
resultsHere, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results.
conclusionsDrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT .
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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.