In Silico Modeling of Small Molecule Carboxamides as Inhibitors of SARS-CoV 3CL Protease: An Approach Towards Combating COVID-19

Title:In Silico Modeling of Small Molecule Carboxamides as Inhibitors of SARS-CoV 3CL Protease: An Approach Towards Combating COVID-19

Volume: 24 Issue: 8

Author(s): Pathan Mohsin Khan, Vinay Kumar and Kunal Roy*

Affiliation:

• Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032,India

Keywords: QSAR, 3CLpro enzyme, Carboxamides, COVID-19, design, respiratory failure.

Abstract:

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 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.

Cite this article as:

Khan Mohsin Pathan , Kumar Vinay and Roy Kunal *, In Silico Modeling of Small Molecule Carboxamides as Inhibitors of SARS-CoV 3CL Protease: An Approach Towards Combating COVID-19, Combinatorial Chemistry & High Throughput Screening 2021; 24 (8) . https://dx.doi.org/10.2174/1386207323666200914094712