Author: Ton, Anhâ€Tien; Gentile, Francesco; Hsing, Michael; Ban, Fuqiang; Cherkasov, Artem
Title: Rapid Identification of Potential Inhibitors of SARSâ€CoVâ€2 Main Protease by Deep Docking of 1.3 Billion Compounds Cord-id: vm4721yd Document date: 2020_3_23
ID: vm4721yd
Snippet: The recently emerged 2019 Novel Coronavirus (SARSâ€CoVâ€2) and associated COVIDâ€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 SARSâ€CoVâ€2. Along these efforts, the structure of SARSâ€CoVâ€2 main protease (Mpro) has been rapid
Document: The recently emerged 2019 Novel Coronavirus (SARSâ€CoVâ€2) and associated COVIDâ€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 SARSâ€CoVâ€2. Along these efforts, the structure of SARSâ€CoVâ€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 structureâ€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 SARSâ€CoVâ€2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.
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