Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

By Anh-Tien Ton, Francesco Gentile, Michael Hsing, Fuqiang Ban, Artem Cherkasov. Molecular Informatics, 2020

442 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/759891bb3d28e821ab03b2e7570947a85eea7a4e

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Computational approaches streamlining drug discovery (2023, background): “In comparison, several studies screening ultra-large libraries were able to identify de novo non-covalent M pro inhibitors in the 10–100-μM range 24,62,63,143 , while experimentally testing only a few hundred synthesized on-demand compounds.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “[34] develop a Deep Docking (DD) platform which trains a neural network to predict the outcomes of docking simulations, which they use to identify a set of 3 million candidate 3C-like protease inhibitors from a set of over 1 billion compounds extracted from the ZINC database.”
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, background): “Besides drug repur-posing, many have been devoted to drug discovery [89], [91]– [93] and protein structure prediction [94]–[96] for combating against the COVID-19 pandemic.”