Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules-Reference-Cited by-同舟云学术

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

Published:2021 Issue:48 Volume:12 Page:15960-15974
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Gentile Francesco¹^ORCID,Fernandez Michael¹,Ban Fuqiang¹,Ton Anh-Tien¹,Mslati Hazem¹,Perez Carl F.¹,Leblanc Eric¹,Yaacoub Jean Charle¹,Gleave James¹,Stern Abraham²,Wong Bill³,Jean François⁴,Strynadka Natalie⁵,Cherkasov Artem¹^ORCID

Affiliation:

1. Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H3Z6, Canada

2. NVIDIA Corporation, Santa Clara, CA, USA

3. Dell Canada, North York, ON, Canada

4. Department of Microbiology and Immunology, The University of British Columbia, Vancouver, BC, Canada

5. Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada

Abstract

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.

Funder

Canadian Institutes of Health Research

Michael Smith Foundation for Health Research

Vancouver Coastal Health Research Institute

VGH and UBC Hospital Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D1SC05579H

Reference70 articles.