Novel therapeutic agents for H5N1 influenza virus through 3D-QSAR, molecular docking, ADMET prediction, and DFT based global reactivity descriptors

Abstract

Avian influenza is a severe respiratory disease that can cause catastrophic outbreaks in domestic poultry and wild birds as well as significant risks to people. This has motivated many researchers to develop new, effective neuraminidase (NA) inhibitors to treat this serious infection. In this context, this study aims to develop new potential NA inhibitors using five computational methods. A three-dimensional quantitative structure-activity relationship (3D-QSAR) comparative molecular similarity indices analysis (CoMSIA) was performed on a set of N-substituted Oseltamivir derivatives as anti-influenza agents. As a result, the best CoMSIA model was robust and predictive (R² = 0.966, Q² = 0.772, and $R_{\text{pred}}^2$ = 0.721). Based on the contour map analysis, 17 new NA inhibitors with high-predicted inhibitory activity were developed. Molecular docking was used to discover the binding modes and interactions between the 17 newly designed NA compounds and the corresponding NA protein. Based on the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, the compounds C10, C11, C12, C15, C16, and C17 have good drug-likeness and pharmacokinetics properties and could be new promising anti-influenza drugs. The six leading compounds further went through biological activity spectra prediction and quantum method density functional theory (DFT) study, which confirmed the trends and the utility of 3D-QSAR CoMSIA and molecular docking in developing new NA inhibitors.