Discovery of novel A2AR antagonists through deep learning-based virtual screening

被引：4

作者：

Tang, Miru ^{[1
,2
,3
]}

Wen, Chang ^{[1
]}

Lin, Jie ^{[1
]}

Chen, Hongming ^{[1
]}

Ran, Ting ^{[1
]}

机构：

[1] Guangzhou Lab, Div Drug & Vaccine Res, Guangzhou 510530, Peoples R China

[2] Guangzhou Regenerat Med & Hlth Guangdong Lab, Bioland Lab, Guangzhou 510530, Peoples R China

[3] Chinese Acad Sci, Guangzhou Inst Biomed & Hlth, State Key Lab Resp Dis, Guangzhou 510530, Peoples R China

来源：

ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES | 2023年 / 3卷

基金：

中国国家自然科学基金;

关键词：

Deep learning; Virtual screening; A 2A R antagonist; Drug discovery; Molecular docking; A(2A) RECEPTOR ANTAGONISTS; PROTEIN DATA-BANK; ADENOSINE; GENERATION; ACCURATE; BINDING; DOCKING;

D O I：

10.1016/j.ailsci.2023.100058

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The A2A 2A adenosine receptor (A2AR) 2A R) is emerging as a promising drug target for cancer immunotherapy. Novel A2AR 2A 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 A2AR 2A 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 A2AR 2A R antagonists for an extremely large molecular library were found successfully.

引用

页数：7

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