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Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https:\/\/github.com\/microsoft\/IGT-Intermolecular-Graph-Transformer.<\/jats:p>","DOI":"10.1093\/bib\/bbac162","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:16:25Z","timestamp":1649934985000},"source":"Crossref","is-referenced-by-count":28,"title":["Improved drug\u2013target interaction prediction with intermolecular graph transformer"],"prefix":"10.1093","volume":"23","author":[{"given":"Siyuan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou, 510006, China"},{"name":"Microsoft Research Asia , Beijing, 100080, China"}]},{"given":"Yusong","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing, 100080, China"},{"name":"Institute of Artificial Intelligence and Robotics , Xi\u2019an Jiaotong University, Xi\u2019an, 710049, China"}]},{"given":"Yifan","family":"Deng","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing, 100080, China"}]},{"given":"Liang","family":"He","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing, 100080, China"},{"name":"School of Computer Science , Fudan University, Shanghai, 200433, China"}]},{"given":"Bin","family":"Shao","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia , Beijing, 100080, China"}]},{"given":"Jian","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou, 510006, China"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics , Xi\u2019an Jiaotong University, Xi\u2019an, 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