A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2. Issue 10 (28th July 2020)

• Record Type: Journal Article
• Authors: Battisti, Verena; Wieder, Oliver; Garon, Arthur; Seidel, Thomas; Urban, Ernst; Langer, Thierry
• Is Part Of: Molecular informatics. Volume 39:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-28
• Subjects: COVID-19 -- coronavirus -- virtual screening -- in silico -- molecular docking -- common hits approach -- docking consensus approach; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202000090
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: The current pandemic threat of COVID‐19, caused by the novel coronavirus SARS‐CoV‐2, not only gives rise to a high number of deaths around the world but also has immense consequences for the worldwide health systems and global economy. Given the fact that this pandemic is still ongoing and there are currently no drugs or vaccines against this novel coronavirus available, this in silico study was conducted to identify a potential novel SARS‐CoV‐2‐inhibitor. Two different approaches were pursued: 1) The Docking Consensus Approach (DCA) is a novel approach, which combines molecular dynamics simulations with molecular docking. 2) The Common Hits Approach (CHA) in contrast focuses on the combination of the feature information of pharmacophore modeling and the flexibility of molecular dynamics simulations. The application of both methods resulted in the identification of 10 compounds with high coronavirus inhibition potential. Abstract :