1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Novel QSAR Model for Cyclic Sulfonamide Derivatives as Potent COVID-19 Inhibitors
Paper ID : 1060-ICB10
Authors:
Nathalie Moussa Mohamad Moussa *1, Hoda Mando2
1department of pharmacuetical chemistry and quality control, faculty of pharmacy, AlAndalus Univrsity, Tartus, Syria.
2Department of Pharmaceutical chemistry and quality control of medicaments, Faculty of Pharmacy, Damascus University, Damascus, Syria.
Abstract:
The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computational methods such as ligand-based drug design are promising approaches to discover novel inhibitors for coronavirus disease.
In this study, novel quantitative structure−activity relationship QSAR model for 28 cyclic sulfonamide derivatives that inhibit SARS-CoV-2 was built by multiple linear regression (MLR). To validate the proposed model, the studied compounds were divided into 23 compounds (training set) and 5 compounds (test set). The developed model was valid, robust, and predictive with correlation coefficient (R2) of 0.77 and 0.95 for training 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 role in SARS-CoV-2 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-2 inhibitors with better activity.
Keywords:
SARS-CoV-2; QSAR; descriptors; inhibitors; drug design.
Status : Paper Accepted (Poster Presentation)