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Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR

The quest for potential antiviral drug for the ongoing SARS-CoV-2 pandemic has posed a serious challenge to the scientific community. While several potential drugs have been proposed from organic molecular perspective, the uses of 3D metal complexes of bipyridine ligand have been recently proven to...

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Detalles Bibliográficos
Autores principales: Eno, Ededet A., Louis, Hitler, Unimuke, Tomsmith O., Gber, Terkumbur E., Akpanke, Josephat A., Amodu, Ismail O., Manicum, Amanda-Lee E., Offiong, Offiong E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400373/
http://dx.doi.org/10.1016/j.chphi.2022.100105
Descripción
Sumario:The quest for potential antiviral drug for the ongoing SARS-CoV-2 pandemic has posed a serious challenge to the scientific community. While several potential drugs have been proposed from organic molecular perspective, the uses of 3D metal complexes of bipyridine ligand have been recently proven to be potential coordinate covalent inhibitors for the SARS-CoV-2 main 3-Chymotrypsin-like protease (3CL(pro)). Herein, we present detailed DFT studies, in silico molecular docking, and multilinear regression analysis (MLRA) investigations of eight (8) selected biologically active Rhenium Tricarbonyl complexes designed and modeled based on the results of Karges and co-workers. The atomistic DFT modeling was conducted to investigate the reactivity, structural stability, and electronic properties based on frontier molecular orbitals (FMO), natural bond orbitals (NBO), interaction energies, density of states (DOS), charge distributions, and molecular thermochemical parameters. Molecular docking simulations were performed to study the binding interactions between the selected biologically active complexes and the target SARS-CoV-2 viral protein, 3CL(PRO). The best quantitative structure-activity relationship (QSAR) was established to demonstrate the correlations between the DFT calculated descriptors and the in vitro biological activities (IC(50)) of structures.