Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

被引：41

作者：

Gentile, Francesco ^{[1
]}

Fernandez, Michael ^{[1
]}

Ban, Fuqiang ^{[1
]}

Ton, Anh-Tien ^{[1
]}

Mslati, Hazem ^{[1
]}

Perez, Carl F. ^{[1
]}

Leblanc, Eric ^{[1
]}

Yaacoub, Jean Charle ^{[1
]}

Gleave, James ^{[1
]}

Stern, Abraham ^{[2
]}

Wong, Bill ^{[3
]}

Jean, Francois ^{[4
]}

Strynadka, Natalie ^{[5
]}

Cherkasov, Artem ^{[1
]}

机构：

[1] Univ British Columbia, Vancouver Prostate Ctr, Dept Urol Sci, 2660 Oak St, Vancouver, BC V6H 3Z6, Canada

[2] NVIDIA Corp, Santa Clara, CA USA

[3] Dell Canada, N York, ON, Canada

[4] Univ British Columbia, Dept Microbiol & Immunol, Vancouver, BC, Canada

[5] Univ British Columbia, Dept Biochem & Mol Biol, Vancouver, BC, Canada

来源：

CHEMICAL SCIENCE | 2021年 / 12卷 / 48期

基金：

加拿大健康研究院;

关键词：

DRUG DISCOVERY; ALGORITHM; ACCURATE; LIGANDS; ICM;

D O I：

10.1039/d1sc05579h

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

引用

页码：15960 / 15974

页数：15

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