In silico identification and molecular dynamic simulations of derivatives of 6,6-dimethyl-3-azabicyclo[3.1.0]hexane-2-carboxamide against main protease 3CLpro of SARS-CoV-2 viral infection

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Abstract

Context: The unavailability of target-specific antiviral drugs for SARS-CoV-2 viral infection kindled the motivation to virtually design derivatives of 6,6-dimethyl-3-azabicyclo[3.1.0]hexane-2-carboxamide as potential antiviral inhibitors against the concerned virus. The molecular docking and molecular dynamic results revealed that the reported derivatives have a potential to act as antiviral drug against SARS-CoV-2. The reported hit compounds can be considered for in vitro and in vivo analyses. Methods: Fragment-based drug designing was used to model the derivatives. Furthermore, DFT simulations were carried out using B3LYP/6-311G** basis set. Docking simulations were performed by using a combination of empirical free energy force field with a Lamarckian genetic algorithm under AutoDock 4.2. By the application of AMBER14 force field and SPCE water model, molecular dynamic simulations and MM-PBSA were calculated for 100 ns.

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APA

Sinha, P., & Yadav, A. K. (2023). In silico identification and molecular dynamic simulations of derivatives of 6,6-dimethyl-3-azabicyclo[3.1.0]hexane-2-carboxamide against main protease 3CLpro of SARS-CoV-2 viral infection. Journal of Molecular Modeling, 29(5). https://doi.org/10.1007/s00894-023-05535-2

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