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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

QSAR of SARS-CoV-2 Main Protease Inhibitors Utilizing Theoretical Molecular Descriptors

Author(s): Sisir Nandi*, Mohit Kumar and Anil Kumar Saxena*

Volume 21, Issue 1, 2024

Published on: 29 December, 2022

Page: [116 - 132] Pages: 17

DOI: 10.2174/1570180820666221214151614

Price: $65

Abstract

Background: COVID-19 is caused by a novel strain of severe acute respiratory syndrome coronaviruses (SARS-CoV-2). It has claimed casualties around the world since the end of 2019 due to its high virulence and quick multiplicity in the human body. Hence, there has been a requirement to develop effective remedial measures to mitigate the mortality. Scientists have been able to develop corona vaccines to provide immunity, but there are no specific small-molecule chemotherapeutics to combat the novel coronavirus which has spread to the whole world due to its contagiousness. In the viral genome exploration, it has been found that the main protease, also known as chymotrypsin-like cysteine protease ([Mpro] or 3C-like protease [3CLpro]) is responsible for the novel coronavirus replication, transcription, and host immunity destruction.

Objectives: Therefore, the main protease has been selected as one of the major targets for the design of new inhibitors. The protein crystallographic and molecular docking studies on SARS-CoV-2 Mpro inhibitors and some quantitative structure-activity relationship (QSAR) studies have been carried out on SARSCoV main protease inhibitors to get some lead molecules for SARS-CoV-2 inhibition. However, there is hardly any QSAR done on the diverse data of SARS-CoV-2 main protease inhibitors. In view of it, QSAR studies have been attempted on SARS-CoV-2 Mpro inhibitors utilizing theoretical molecular descriptors solely computed from the structures of novel corona viral main protease inhibitors.

Methods: As the number of structural descriptors is more than the observations, a genetic algorithm coupled with multiple linear methods has been applied for the development of QSAR models taking diverse SARS-CoV-2 Mpro inhibitors.

Results: The developed best QSAR model showing R2, Q2 Loo, and R2 pred values of 0.7389, 0.6666, and 0.6453 respectively has been further validated on an external data set where a good correlation (r = 0.787) has been found.

Conclusion: Therefore, this model may be useful for the design of new SARS-CoV-2 main protease inhibitors.

Keywords: SARS-CoV-2, Mpro or 3CLpro main protease inhibitors, computed structural descriptors, QSAR, Anti-COVID-19 drug design, molecular descriptors.

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