These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds
preprintsubmitted on 16.02.2020, 20:23 and posted on 19.02.2020, 06:04 by Anh-Tien Ton, Francesco Gentile, Michael Hsing, Fuqiang Ban, Artem Cherkasov
The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no FDA-approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates.
In recent month, our group has developed a novel deep learning platform – Deep Docking (DD) which enables very fast docking of billions of molecular structures and provides up to 6,000X enrichment on the top-predicted ligands compared to conventional docking workflow (without notable loss of information on potential hits). In the current work we applied DD to entire 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro. The compounds are made publicly available for further characterization and development by scientific community.
Email Address of Submitting Authorfgentile@prostatecentre.com
InstitutionVancouver Prostate Centre, University of British Columbia
ORCID For Submitting Author0000-0001-8299-1976
Declaration of Conflict of InterestThe authors declare no conflict of interest.
Read the published paper
in Molecular Informatics