Print ISSN : 0919-2719
Regular Article
In silico Design of Inhibitor Against SARS-CoV-2 Protease by Docking Simulation and ADMET Prediction
Madoka HoshiSouma ShiinoAkihiko GomiKyousuke SakataSho KonnoYoshio HayashiMasaki Kojima
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2021 Volume 29 Pages 11-21


Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has high genomic similarity to SARS-CoV-1, which was responsible for SARS in 2003. Since 3C-like protease (3CLpro) of these pathogenic coronaviruses processes functional proteins essential for the viral life cycle, it has been a viable target for drug development. YH-53 was originally developed as an anti-SARS agent, which would also inhibit 3CLpro of SARS-CoV-2. In order to lead optimal inhibitors as anti-COVID-19 agents, we analyzed the bound structures of SARS-CoV-2 3CLpro with YH-53 or related compounds by computational docking along with the predictions of their pharmacokinetic properties and toxicities (ADMET). Under a covalent bond with 3CLpro, YH-53 was found to bind to the enzyme in a correct docking pose so that each P subsite could bind to the corresponding S subsite of 3CLpro. The blind docking of related compounds suggested that the P3 subsite of YH-53 was required for correct docking, while P1' was not so necessary. Some of the predicted ADMET properties of YH-53 were unfavorable, where the main contributors were large molecular weight and low solubility. Since P1' acts as a reactive warhead and P3 affects the substrate specificity, there may be a trade-off between the modification of substructures of YH-53 and the improvement of its ADMET properties. Based on these results, we propose plausible inhibitory candidates against SARS-CoV-2 with better ADMET qualities.

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