2D-QSAR, 3D-QSAR, molecular docking and ADMET prediction studies of some novel 2-((1H-indol-3-yl)thio)-N-phenyl-acetamide derivatives as anti-influenza A virus

Abstract

ABSTRACT Due to the emergence of drug-resistant strains of influenza A virus (IAV) in recent times, the need to search and discover more potent anti-IAV inhibitors is of great interest, especially with the devastating COVID-19 pandemic. The present research applied 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions on some novel analogs of 2-((1 H-indol-3-yl)thio)-N-phenyl-acetamide as IAV inhibitors. The 2D-QSAR modeling results revealed GFA-MLR ( =0.8861, q2 = 0.7864) and GFA-ANN ( =0.8980, q2 = 0.8884) models with the most relevant descriptors for predicting the anti-IAV responses of the compounds, which have passed the benchmarks for accepting QSAR models. The 3D-QSAR modeling results suggested CoMFA_SE ( =0.925, q2 = 0.59) and CoMSIA_EAD ( =0.929, q2 = 0.767) models for good and reliable activity predictions. The molecular docking of the compounds with the active site of neuraminidase (NA) receptor theoretically confirms their resilient potency. The compounds mostly formed H-bond and hydrophobic interactions with key residues, such as ARG118, ASP151, GLU119, TRP179, ARG293 and PRO431 that triggered the catalytic reaction for the NA inhibition. However, compounds 16 and 21 were identified as lead compounds in the in-silico search for more potent candidates. The outcome of this study set a course for the in-silico design and search of potential candidates for influenza therapy.