In-silico approach to design effective antiviral drugs against SARS- CoV-2 and SARS-CoV-1 from reported phytochemicals

Computer-aided drug design by molecular docking, statistical analysis like multiple linear regression (MLR), principal component analysis (PCA), and molecular dynamics studies can emerge as an efficient approach to design promising core scaffolds for coronavirus medication. The main protease (3CLpro) of SARS-CoV-2 and SARS-CoV-1 is one of the critical targets for designing and developing broad-antiviral therapeutic drugs. In this evaluation, we have selected 40 reported phytochemicals to design the efficient core scaffolds, which can act as a potent inhibitor against the main protease of SARS-CoV-2 and SARS-CoV-1. We categorized the selected phytochemicals into a more bioavailable and less bioavailable set considering phytochemical drug likeliness properties. All the selected phytochemicals vigorously interact with the catalytic dyad His41 and Cys145. Statistical analysis MLR has confirmed their contribution to structural features on binding affinities and PCA analysis for structural activity relationship (SAR) for their structural pattern recognition to determine the core scaffolds inhibitors. We have confirmed that 4'-Hydroxyisolonchocarpin, and BrussochalconeA, are safe and exhibit good pharmacological properties. Our comprehensive computational and statistical analysis reveals that these selected phytochemicals (4'-Hydroxyisolonchocarpin, BrussochalconeA) can be used to design potential broad-antiviral inhibitors against the SARS-CoV-2 and SARS-CoV-1.