Background: The quantitative structure-activity relationship (QSAR) approach is most
widely used for the prediction of biological activity of potential medicinal compounds. A QSAR
model is developed by correlating the information obtained from chemical structures (numerical descriptors/
independent variables) with the experimental response values (the dependent variable).
Methods: In the current study, we have developed a QSAR model to predict the inhibitory activity
of small molecule carboxamides against severe acute respiratory syndrome coronavirus (SARS--
CoV) 3CLpro enzyme. Due to the structural similarity of this enzyme with SARS-CoV-2, the
causative organism of the recent pandemic, the former may be used for the development of therapies
against coronavirus disease 19 (COVID-19).
Results: The final multiple linear regression (MLR) model was based on four two-dimensional descriptors
with definite physicochemical meaning. The model was strictly validated using different
internal and external quality metrics. The model showed significant statistical quality in terms of
determination coefficient (R2=0.748, adjusted R2 or R2
adj = 0.700), cross-validated leave-one-out Q2
(Q2=0.628) and external predicted variance R2
pred = 0.723. The final validated model was used for
the prediction of external set compounds as well as to virtually design a new library of small
molecules. We have also performed a docking analysis of the most active and least active compounds
present in the dataset for comparative analysis and to explain the features obtained from the
Conclusion: The derived model may be useful to predict the inhibitory activity of small molecules
within the applicability domain of the model only based on the chemical structure information prior
to their synthesis and testing.