article · PeerJ
Computational drug design offers a rapid alternative to conventional drug discovery routes, which typically take years to produce therapeutic candidates. Focusing on the SARS-CoV-2 main protease, an essential enzyme for viral replication and transcription, a computational workflow was constructed using recently identified inhibitor profiles. A pharmacophore model screened twenty million drug-like molecules from the open-access ZINC database. The resulting hits were prioritised through structure-based virtual screening, followed by molecular docking and binding energy calculations against the main protease target. The leading candidate, designated compound 1, alongside a potent reference standard, was subjected to one hundred nanosecond molecular dynamics simulations and computational absorption, distribution, metabolism, and excretion analyses. These simulations and pharmacokinetic evaluations identified compound 1 as a viable protease inhibitor suitable for subsequent development.
While vaccines help lower mortality, effective antiviral therapeutics remain vital for managing active coronavirus infections. Conventional drug development takes many years to yield clinical candidates. In silico screening rapidly pinpoints viable molecules that bind to critical viral replication machinery, significantly accelerating the early stages of antiviral discovery.
This work represents very early-stage computational discovery that could provide a starting point for developing targeted COVID-19 antiviral therapies. The potential users are pharmaceutical companies and medicinal chemistry research teams seeking lead molecules to synthesise and validate in biological assays. Because the findings rely entirely on computer simulations, the candidate compound remains distant from real-world medical use until experimental testing and preclinical trials take place.
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The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound ( 1 ), and the potent standard ( III ) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.
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DOI: 10.7717/peerj.14120
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