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This framework not only integrates the topological adaptive graph convolutional network and multi-head attention mechanism, but also introduces a self-masked attention mechanism to ensure that each protein binding site can focus on its own unique features and its interaction with the ligand. Experimental results on multiple public datasets show that our method significantly outperforms traditional machine learning and graph neural network methods in predictive performance. In addition, our method can effectively identify and explain key molecular interactions, providing new insights into understanding the complex relationship between drugs and targets. <\/jats:p>","DOI":"10.1142\/s0219720024500240","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T04:42:28Z","timestamp":1726116148000},"source":"Crossref","is-referenced-by-count":0,"title":["Improving drug\u2013target interaction prediction through dual-modality fusion with InteractNet"],"prefix":"10.1142","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6137-9066","authenticated-orcid":false,"given":"Baozhong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Su Zhou 215009, P. R. 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