In silico prediction of the inhibition of new molecules on 3CLpro SARS-CoV-2 enzyme by using QSAR-SVRPSO approach

Continuous effort is dedicated to the discovery of potential drugs for the novel coronavirus-2 both clinically and computationally. Computer-Aided Drug Design CADD is the backbone of drug discovery, and shifting to computational approaches has become a need. Quantitative Structure-Activity Relationship QSAR is a widely used approach in predicting the activity of potential molecules and is an early step in drug discovery. 3CLpro is a highly conserved enzyme in the coronaviruses characterized by its role in the viral replication cycle. Despite the existence of various vaccines, the development of a new drug for SARS-CoV-2 is a necessity to provide cures to patients. In the pursuit of exploring new potential 3CLpro SARS-CoV-2 inhibitors and contributing to the existing literature, this work opted to build and compare three models of QSAR to correlate between the molecules’ structure and their activity: IC50, through the application of MLR, SVR, and SVR-PSO algorithms. The database was selected based on its novelty and proven activity, and its representative descriptors were obtained by the GA algorithm. The built models were plotted and compared following various internal and external validation criteria, and applicability domains for each model were determined. The results demonstrated that the SVR-PSO model performed best in terms of predictive ability and robustness, followed by SVR, and finally MLR. These outcomes prove that the SVR-PSO model is robust and concrete and paves the way for its prediction abilities for future screening of larger inhibitors’ datasets.