Miru Tang , Chang Wen , Jie Lin , Hongming Chen , Ting Ran
{"title":"Discovery of novel A2AR antagonists through deep learning-based virtual screening","authors":"Miru Tang , Chang Wen , Jie Lin , Hongming Chen , Ting Ran","doi":"10.1016/j.ailsci.2023.100058","DOIUrl":null,"url":null,"abstract":"<div><p>The A<sub>2A</sub> adenosine receptor (A<sub>2A</sub>R) is emerging as a promising drug target for cancer immunotherapy. Novel A<sub>2A</sub>R antagonists are highly demanded due to few candidates entering clinic trials specific for cancer treatment. Structure-based virtual screening has made a great contribution to discover novel A<sub>2A</sub>R antagonists, but most depended on inefficient molecular docking on relatively small molecular databases. In this work, a deep learning strategy was applied to accelerate docking-based virtual screening, through which new structural types of A<sub>2A</sub>R antagonists for an extremely large molecular library were found successfully.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100058"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence in the life sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667318523000028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The A2A adenosine receptor (A2AR) is emerging as a promising drug target for cancer immunotherapy. Novel A2AR antagonists are highly demanded due to few candidates entering clinic trials specific for cancer treatment. Structure-based virtual screening has made a great contribution to discover novel A2AR antagonists, but most depended on inefficient molecular docking on relatively small molecular databases. In this work, a deep learning strategy was applied to accelerate docking-based virtual screening, through which new structural types of A2AR antagonists for an extremely large molecular library were found successfully.
Artificial intelligence in the life sciencesPharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
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