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Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new graph-attention-based approach, is proposed to improve the accuracy of molecular representation by simultaneously considering atom\u2013atom, bond\u2013bond and atom-bond interactions. In addition, we benchmark models extensively on 8 public and 680 proprietary industrial datasets spanning a wide variety of chemical end points. The results show that ComABAN has higher prediction accuracy compared with the classical machine learning method and the deep learning-based methods. Furthermore, the trained neural network was used to predict a library of 1.5 million molecules and picked out compounds with a classification result of grade I. Subsequently, these predicted molecules were scored and ranked using cascade docking, molecular dynamics simulations to generate five potential candidates. All five molecules showed high similarity to nanomolar bioactive inhibitors suppressing the expression of HIF-1\u03b1, and we synthesized three compounds (Y-1, Y-3, Y-4) and tested their inhibitory ability in vitro. Our results indicate that ComABAN is an effective tool for accelerating drug discovery.<\/jats:p>","DOI":"10.1093\/bib\/bbac350","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:37:43Z","timestamp":1661301463000},"source":"Crossref","is-referenced-by-count":2,"title":["ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6978-6350","authenticated-orcid":false,"given":"Huihui","family":"Yan","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals , Zhejiang University of Technology, Hangzhou, 310014, P. R. China"},{"name":"College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, 310058, P. R. China Fax\/Tel: 86-571-8820-845 E-mail: shengr@zju.edu.cn"}]},{"given":"Yuanyuan","family":"Xie","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals , Zhejiang University of Technology, Hangzhou, 310014, P. R. China"}]},{"given":"Yao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, 310058, P. R. China Fax\/Tel: 86-571-8820-845 E-mail: shengr@zju.edu.cn"}]},{"given":"Leer","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, 310058, P. R. China Fax\/Tel: 86-571-8820-845 E-mail: shengr@zju.edu.cn"}]},{"given":"Rong","family":"Sheng","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, 310058, P. R. 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