Novel QSAR Model for Cyclic Sulfonamide Derivatives as Potent COVID-۱۹ Inhibitors

The recent outbreak of the deadly coronavirus disease ۱۹ (COVID-۱۹) pandemic poses serious healthconcerns around the world. The lack of approved drugs continues to be a challenge and further necessitatesthe discovery of new therapeutic molecules. Computational methods such as ligand-based drug design arepromising approaches to discover novel inhibitors for coronavirus disease.In this study, novel quantitative structure−activity relationship QSAR model for ۲۸ cyclic sulfonamidederivatives that inhibit SARS-CoV-۲ was built by multiple linear regression (MLR). To validate the proposedmodel, the studied compounds were divided into ۲۳ compounds (training set) and ۵ compounds (test set).The developed model was valid, robust, and predictive with correlation coefficient (R۲) of ۰.۷۷ and ۰.۹۵ fortraining and test groups, respectively.The model obtained six descriptors which best describe the activity. The six descriptors encode barysz matrix,atom count, and autocorrelation. The descriptors nCl that related to the atom count play more significant rolein SARS-CoV-۲ inhibitory activity as sensitivity analysis has shown.The model is expected to be useful in virtual screening, providing important tools in the field of drug design,and orienting the direction of designing new SARS-CoV-۲ inhibitors with better activity.

کلیدواژه ها:

SARS-CoV-۲ ، QSAR ، descriptors ، inhibitors ، drug design