Abstract:

The recent outbreak of coronavirus disease 2019 (COVID-19) is posing a global threat to human population. The pandemic caused by novel coronavirus (2019-nCoV), also called as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2); first emerged in Wuhan city, Hubei province of China in December 2019. The rapid human to human transmission has caused the contagion to spread world-wide affecting 244,385,444 (244.4 million) people globally causing 4,961,489 (5 million) fatalities dated by 27 October 2021. At present, 6,697,607,393 (6.7 billion) vaccine doses have been administered dated by 27 October 2021, for the prevention of COVID-19 infections. Even so, this critical and threatening situation of pandemic and due to various variants’ emergence, the pandemic control has become challenging; this calls for gigantic efforts to find new potent drug candidates and effective therapeutic approaches against the virulent respiratory disease of COVID-19. In the respiratory morbidities of COVID-19, the functionally crucial drug target for the antiviral treatment could be the main protease/3-chymotrypsin protease (Mpro/3CLpro) enzyme that is primarily involved in viral maturation and replication. In view of this, in the current study I have designed a library of small molecules against the main protease (Mpro) of coronavirus SARS-CoV-2 (2019-nCoV) by using multimodal generative neural-networks. The scaffold-based molecular docking of the series of compounds at the active site of the protein was performed; binding poses of the molecules were evaluated and protein-ligand interaction studies followed by the binding affinity calculations validated the findings. I have identified a number of small promising lead compounds that could serve as potential inhibitors of the main protease (Mpro) enzyme of coronavirus SARS-CoV-2 (2019-nCoV). This study would serve as a step forward in the development of effective antiviral therapeutic agents against the COVID-19.

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