Abstract
Background: Since the outbreak of the COVID-19 pandemic in 2019, the world has been racing to develop effective drugs for treating this deadly disease. Although there are now some vac-cines that have somewhat alleviated global panic, the lack of approved drugs remains a persistent challenge. Consequently, there is a pressing need to discover new therapeutic molecules. Methods: In this study, we explore the application of a quantitative structure−activity relationship (QSAR) model to predict the efficacy of 28 cyclic sulfonamide derivatives against SARS-CoV-2. The model was developed using multiple linear regression, and six molecular descriptors were identified as the most significant factors in determining the inhibitory activity. This proposed QSAR model holds the potential for aiding the virtual screening and drug design process in the development of new and more effective SARS-CoV-2 inhibitors. The model was also applied to seven natural products primary sulfonamides and sulfamates, demonstrating promising activity Results: The study results indicated that the atom count, as represented by the descriptor nCl, had the most significant impact on the inhibitory activity against SARS-CoV-2. The proposed model was val-idated using various statistical parameters, confirming its validity, robustness, and predictiveness, with a high correlation coefficient (R2) of 0.77 for the training group and 0.95 for the test group. Further-more, we predicted the activity of seven natural compounds, and among them, Dealanylascamycin exhibited the highest predicted activity. Subsequently, Dealanylascamycin was docked to SARS-CoV-2 and the results of the docking study further strengthened its potential as a promising candidate against COVID-19, suggesting that it should be considered for further optimization and validation. Conclusion: Our findings demonstrate promising predicted inhibitory activity against SARS-CoV-2 for seven natural products, primary sulfonamides, and primary sulfamates.
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