Research Article | Volume 13, Supplement 1, September, 2023

Computational design for the development of natural molecules as compelling inhibitors against the target SARS-CoV-2: An in-silico attempt

Nagarjuna Palathoti Kalirajan Rajagopal Gowramma Byran Kannan Raman Edwin Jose Manikandan Gurunathan   

Open Access   

Published:  Sep 20, 2023

DOI: 10.7324/JAPS.2023.134301

A threat to the global human population has been established by the COVID-19 pandemic in 2020 and it is quite challenging to identify innovative medications in this epidemic. These are zoonotic and can potentially create massive outbreaks of illnesses that can result in morbidity and death. As a consequence, natural therapies for the anticipation and dealing of COVID-19 are widely acknowledged as a quick means to find successful therapeutic choices that can be found through in-silico drug screening tests. RNA-dependent RNA polymerase (RdRp), a vital precursor involved in the virus’s life cycle, is present in SARS-CoV-2. Blocking the formation of the RdRp–RNA complex inhibits viral replica and boosts the immune response of the host. In our present research, with the use of a SuperNatural Database, we started the high-throughput virtual screening method to recognize inhibitors aiming for SARS-CoV-2 RdRp. According to extra-precision docking data, two compounds, SN00293542 and SN00391842 had −14.79 and −14.65 kcal/mol docking scores, respectively. In addition, Prime molecular mechanics generalized bond surface area research has identified hydrophobic energy and Van der Waal energy footings as significant contributions towards total binding free energy. Additionally, a hundred nanosecond Molecular dynamics simulation of the SN00391842/7D4F complex was run to determine its dynamic behavior.


Palathoti N, Rajagopal K, Byran G, Raman K, Jose E, Gurunathan M. Computational design for the development of natural molecules as compelling inhibitors against the target SARS-CoV-2: An in-silico attempt. J Appl Pharm Sci, 2023; 13(Suppl 1):034–040.

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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