ArticleiScience2024
DRGATAN: Directed relation graph attention aware network for asymmetric drug-drug interaction prediction.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
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4 authors.
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Abstract
In scenarios involving the treatment of complex or coexisting diseases with multiple drugs, the potential for severe adverse drug reactions in patients necessitates the identification of potential drug-drug interactions (DDIs). Most existing computational methods have not taken into account the asymmetry and relation types of drug interactions caused by the relation information between drugs, which may lead to missing information in embedded learning. Therefore, this paper proposes a directed relation graph attention aware network (DRGATAN) to predict asymmetric drug interactions. DRGATAN leverages an encoder to learn multi-relational role embeddings of drugs across different types of relations. The experimental results show that DRGATAN's performance is superior to recognized advanced methods. The visualization demonstrates the effect of utilizing asymmetric information, and the case analysis validates the reliability of the proposed method. This study provides guidance for predicting asymmetric drug interactions.
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