BACKGROUND: The quantitative structure-activity relationship (QSAR) approach is most widely used for 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 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 that of SARS-CoV-2, the causative organism of the recent pandemic, the former may be used for development of therapies against corona virus 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 cofficient (R20.748, adjusted R2(R2adj 0.700), cross-validated leave-one-out Q2(Q20.628 and external predicted variance (R2pred 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 docking analysis of the most active and least active compounds present in the data set for comparative analysis and to explain the features obtained from the 2D-QSAR model. 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.
In silico modeling of small molecule carboxamides as inhibitors of SARS-CoV 3CL protease: An approach towards combating COVID-19