2024/08/27 by Changjian Zhou, Xin Zhang, Zhou, Changjian +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Pharmacology, Toxicology and Pharmaceutics · #Biomedical Text Mining and Ontologies #Computational Drug Discovery Methods #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Molecular Networks (q-bio.MN) #Pharmacogenetics and Drug Metabolism #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2408.15310
openalex publication_date 2024/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent studies suggest that drug-drug interaction (DDI) prediction via computational approaches has significant importance for understanding the functions and co-prescriptions of multiple drugs. However, the existing silico DDI prediction methods either ignore the potential interactions among drug-drug pairs (DDPs), or fail to explicitly model and fuse the multi-scale drug feature representations for better prediction. In this study, we propose RGDA-DDI, a residual graph attention network (residual-GAT) and dual-attention based framework for drug-drug interaction prediction. A residual-GAT module is introduced to simultaneously learn multi-scale feature representations from drugs and DDPs. In addition, a dual-attention based feature fusion block is constructed to learn local joint interaction representations. A series of evaluation metrics demonstrate that the RGDA-DDI significantly improved DDI prediction performance on two public benchmark datasets, which provides a new insight into drug development.