Suggestion of active 3-chymotrypsin like protease (3CL^Pro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model

ABSTRACT

The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CL^pro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CL^pro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski’s rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.

KEYWORDS:

• 3CL^pro inhibitors
• coronavirus
• ALASSO
• artificial neural network
• molecular docking

Acknowledgements

The authors are thankful to the Shahrood University of Technology Research Council for supporting this work.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2021.1975167

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors