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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400373/ http://dx.doi.org/10.1016/j.chphi.2022.100105 |
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author | Eno, Ededet A. Louis, Hitler Unimuke, Tomsmith O. Gber, Terkumbur E. Akpanke, Josephat A. Amodu, Ismail O. Manicum, Amanda-Lee E. Offiong, Offiong E. |
author_facet | Eno, Ededet A. Louis, Hitler Unimuke, Tomsmith O. Gber, Terkumbur E. Akpanke, Josephat A. Amodu, Ismail O. Manicum, Amanda-Lee E. Offiong, Offiong E. |
author_sort | Eno, Ededet A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9400373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94003732022-08-25 Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR Eno, Ededet A. Louis, Hitler Unimuke, Tomsmith O. Gber, Terkumbur E. Akpanke, Josephat A. Amodu, Ismail O. Manicum, Amanda-Lee E. Offiong, Offiong E. Chemical Physics Impact Article 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. The Authors. Published by Elsevier B.V. 2022-12 2022-08-24 /pmc/articles/PMC9400373/ http://dx.doi.org/10.1016/j.chphi.2022.100105 Text en © 2022 The Authors. Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Eno, Ededet A. Louis, Hitler Unimuke, Tomsmith O. Gber, Terkumbur E. Akpanke, Josephat A. Amodu, Ismail O. Manicum, Amanda-Lee E. Offiong, Offiong E. Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR |
title | Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR |
title_full | Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR |
title_fullStr | Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR |
title_full_unstemmed | Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR |
title_short | Modeling of Re(I) tricarbonyl complexes against SARS-CoV-2 receptor via DFT, in-silico molecular docking, and QSAR |
title_sort | modeling of re(i) tricarbonyl complexes against sars-cov-2 receptor via dft, in-silico molecular docking, and qsar |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400373/ http://dx.doi.org/10.1016/j.chphi.2022.100105 |
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