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D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19

文献类型：期刊论文

作者Yang, Yanqing2,3,4; Zhou, Deshan1; Zhang, Xinben2,3; Shi, Yulong2,3,4; Han, Jiaxin5; Zhou, Liping2,3,4; Wu, Leyun2,3,4; Ma, Minfei2,3,4; Li, Jintian2,3,4; Peng, Shaoliang
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2022-04-21
页码16
关键词D3AI-CoV COVID-19 deep learning target prediction virtual screening
ISSN号1467-5463
DOI10.1093/bib/bbac147
通讯作者Peng, Shaoliang(slpeng@hnu.edu.cn) ; Xu, Zhijian(zjxu@simm.ac.cn) ; Zhu, Weiliang(wlzhu@simm.ac.cn)
英文摘要Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learningbased models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoWindex.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
WOS关键词SARS-COV-2
资助项目National Key Research and Development Program of China[2016YFA0502301] ; Natural Science Foundation of Shanghai[21ZR1475600] ; Natural Science Foundation of China[U19A2067]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000785786700001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/299713]  
专题新药研究国家重点实验室
通讯作者Peng, Shaoliang; Xu, Zhijian; Zhu, Weiliang
作者单位1.Hunan Univ, Dept Comp Sci, Changsha 410082, Hunan, Peoples R China
2.Chinese Acad Sci, Stake Key Lab Drug Res, Shanghai Inst Mat Med, CAS Key Lab Receptor Res, Shanghai 201203, Peoples R China
3.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, Shanghai 201203, Peoples R China
4.Univ Chinese Acad Sci, Sch Pharm, 19A Yuquan Rd, Beijing 100049, Peoples R China
5.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing 210046, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Yanqing,Zhou, Deshan,Zhang, Xinben,et al. D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19[J]. BRIEFINGS IN BIOINFORMATICS,2022:16.
APA Yang, Yanqing.,Zhou, Deshan.,Zhang, Xinben.,Shi, Yulong.,Han, Jiaxin.,...&Zhu, Weiliang.(2022).D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.BRIEFINGS IN BIOINFORMATICS,16.
MLA Yang, Yanqing,et al."D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19".BRIEFINGS IN BIOINFORMATICS (2022):16.

入库方式： OAI收割

来源：上海药物研究所

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