Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19

Background: Coronavirus disease 2019 (COVID-19) as a serious global health crisis leads to high mortality and morbidity. However, currently, there are no effective vaccines and treatments for COVID-19. Main protease (Mpro) and angiotensin-converting enzyme 2 (ACE2) are the best therapeutic targets o...

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Autores principales:	Saeid Afshar, Asrin Bahmani, Massoud Saidijam
Formato:	article
Lenguaje:	EN
Publicado:	Hamadan University of Medical Sciences 2020
Materias:	covid-19 main protease ace2 drug repurposing fragment-qsar Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/720d98b63dde427fb2d5a5cf9d284f0a
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spelling	oai:doaj.org-article:720d98b63dde427fb2d5a5cf9d284f0a2021-11-21T10:39:25ZMolecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-1910.34172/ajmb.2020.122345-4113https://doaj.org/article/720d98b63dde427fb2d5a5cf9d284f0a2020-12-01T00:00:00Zhttp://ajmb.umsha.ac.ir/PDF/ajmb-8-83.pdfhttps://doaj.org/toc/2345-4113Background: Coronavirus disease 2019 (COVID-19) as a serious global health crisis leads to high mortality and morbidity. However, currently, there are no effective vaccines and treatments for COVID-19. Main protease (Mpro) and angiotensin-converting enzyme 2 (ACE2) are the best therapeutic targets of COVID-19. Objectives: The main purpose of this study is to investigate the most appropriate drug and candidate compound for proper interaction with Mpro and ACE2 to inhibit the activity of COVID-19. Methods: In this study, repurposing of approved drugs and screening of candidate compounds using molecular docking and fragment-based QSAR method were performed to discover the potential inhibitors of Mpro and ACE2. QSAR and docking calculations were performed based on the prediction of the inhibitory activities of 5-hydroxy indanone derivatives. Based on the results, an optimal structure was proposed to inhibit the activity of COVID-19. Results: Among 2629 DrugBank approved drugs, 118 were selected considering the LibDock score and absolute energy for possible drug-Mpro interactions. Furthermore, the top 40 drugs were selected based on screening the results for possible drug- Mpro interactions with AutoDock Vina. Conclusion: Finally, evaluation of the top 40 selected drugs for possible drug-ACE2 interactions with AutoDock Vina indicated that deslanoside (DB01078) can interact effectively with both Mpro and ACE2. However, prior to conducting clinical trials, further experimental validation is needed.Saeid AfsharAsrin BahmaniMassoud SaidijamHamadan University of Medical Sciencesarticlecovid-19main proteaseace2drug repurposingfragment-qsarMedical technologyR855-855.5ENAvicenna Journal of Medical Biochemistry, Vol 8, Iss 2, Pp 83-88 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 main protease ace2 drug repurposing fragment-qsar Medical technology R855-855.5
spellingShingle	covid-19 main protease ace2 drug repurposing fragment-qsar Medical technology R855-855.5 Saeid Afshar Asrin Bahmani Massoud Saidijam Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19
description	Background: Coronavirus disease 2019 (COVID-19) as a serious global health crisis leads to high mortality and morbidity. However, currently, there are no effective vaccines and treatments for COVID-19. Main protease (Mpro) and angiotensin-converting enzyme 2 (ACE2) are the best therapeutic targets of COVID-19. Objectives: The main purpose of this study is to investigate the most appropriate drug and candidate compound for proper interaction with Mpro and ACE2 to inhibit the activity of COVID-19. Methods: In this study, repurposing of approved drugs and screening of candidate compounds using molecular docking and fragment-based QSAR method were performed to discover the potential inhibitors of Mpro and ACE2. QSAR and docking calculations were performed based on the prediction of the inhibitory activities of 5-hydroxy indanone derivatives. Based on the results, an optimal structure was proposed to inhibit the activity of COVID-19. Results: Among 2629 DrugBank approved drugs, 118 were selected considering the LibDock score and absolute energy for possible drug-Mpro interactions. Furthermore, the top 40 drugs were selected based on screening the results for possible drug- Mpro interactions with AutoDock Vina. Conclusion: Finally, evaluation of the top 40 selected drugs for possible drug-ACE2 interactions with AutoDock Vina indicated that deslanoside (DB01078) can interact effectively with both Mpro and ACE2. However, prior to conducting clinical trials, further experimental validation is needed.
format	article
author	Saeid Afshar Asrin Bahmani Massoud Saidijam
author_facet	Saeid Afshar Asrin Bahmani Massoud Saidijam
author_sort	Saeid Afshar
title	Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19
title_short	Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19
title_full	Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19
title_fullStr	Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19
title_full_unstemmed	Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19
title_sort	molecular docking and fragment-based qsar modeling for in silico screening of approved drugs and candidate compounds against covid-19
publisher	Hamadan University of Medical Sciences
publishDate	2020
url	https://doaj.org/article/720d98b63dde427fb2d5a5cf9d284f0a
work_keys_str_mv	AT saeidafshar moleculardockingandfragmentbasedqsarmodelingforinsilicoscreeningofapproveddrugsandcandidatecompoundsagainstcovid19 AT asrinbahmani moleculardockingandfragmentbasedqsarmodelingforinsilicoscreeningofapproveddrugsandcandidatecompoundsagainstcovid19 AT massoudsaidijam moleculardockingandfragmentbasedqsarmodelingforinsilicoscreeningofapproveddrugsandcandidatecompoundsagainstcovid19
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Molecular Docking and Fragment-Based QSAR Modeling for In Silico Screening of Approved Drugs and Candidate Compounds Against COVID-19

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