Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds. Issue 8 (23rd March 2020)

• Record Type: Journal Article
• Authors: Ton, Anh‐Tien; Gentile, Francesco; Hsing, Michael; Ban, Fuqiang; Cherkasov, Artem
• Is Part Of: Molecular informatics. Volume 39:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-23
• Subjects: SARS-CoV-2 -- COVID-19 -- deep learning -- virtual screening -- protease inhibitors; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202000028
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: 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 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. Recently, our group has developed a novel deep learning platform – Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure‐based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1, 000 potential ligands for SARS‐CoV‐2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community. Abstract :