ArticleJournal of biomolecular structure & dynamics2021
Virtual screening, molecular dynamics and structure-activity relationship studies to identify potent approved drugs for Covid-19 treatment.
Article in Journal of biomolecular structure & dynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 120 citations in OpenAlex.
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- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
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- In-silico approach to designing effective antiviral drugs against SARS-CoV-2 and SARS-CoV-1 from reported phytochemicals: a quality improvement study.Annals of medicine and surgery (2012) · 2023Article
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Corrections and comments
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Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural-activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi-sulfur and pi-pi interactions appear to be dominant in drug-Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug-Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural-activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.
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