ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

Vanja P. Ničkčović, Gordana R. Nikolić, Biserka M. Nedeljković, Nebojša Mitić, Snežana Filipović Danić, Jadranka Mitić, Zoran Marčetić, Dušan Sokolović, and Aleksandar M. Veselinović

Clinical-Hospital Center, Priština, Serbia

E-mail: aveselinovic@medfak.ni.ac.rs

Received: 29 May 2021  Accepted: 5 March 2022

Abstract:

The COVID-19 pandemic emerged in 2019, bringing with it the need for greater stores of effective antiviral drugs. This paper deals with the conformation-independent, QSAR model, developed by employing the Monte Carlo optimization method, as well as molecular graphs and the SMILES notation-based descriptors for the purpose of modeling the SARS-CoV-3CLpro enzyme inhibition. The main purpose was developing a reproducible model involving easy interpretation, utilized for a quick prediction of the inhibitory activity of SAR-CoV-3CLpro. The following statistical parameters were present in the best-developed QSAR model: (training set) R² = 0.9314, Q² = 0.9271; (test set) R² = 0.9243, Q² = 0.8986. Molecular fragments, defined as SMILES notation descriptors, that have a positive and negative impact on 3CLpro inhibition were identified on the basis of the results obtained for structural indicators, and were applied to the computer-aided design of five new compounds with (4-methoxyphenyl)[2-(methylsulfanyl)-6,7-dihydro-1H-[1,4]dioxino[2,3-f]benzimidazol-1-yl]methanone as a template molecule. Molecular docking studies were used to examine the potential inhibition effect of designed molecules on SARS-CoV-3CLpro enzyme inhibition and obtained results have high correlation with the QSAR modeling results. In addition, the interactions between the designed molecules and amino acids from the 3CLpro active site were determined, and the energies they yield were calculated.

Keywords: 3CLpro inhibitors; COVID-19 therapy; QSAR; Molecular modeling; Drug design

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-022-02170-8

Chemical Papers 76 (7) 4393–4404 (2022)