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

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) R ² = 0.9314, Q ² = 0.9271; (test set) R ² = 0.9243, Q ² = 0.8986. Molecular fragments, defi

SUBMITTER: Nickcovic VP 

PROVIDER: S-EPMC8977062 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature