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