Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

ABSTRACT:

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ( Rtrain2  = 0.9192, Q ² = 0.8767, R ² _adj = 0.8991, RMSE = 0.09

SUBMITTER: Abdullahi M 

PROVIDER: S-EPMC9389500 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature