Design of New Anti-Influenza Structures Based on 3D-QSAR, Molecular Docking and Molecular Dynamics Studies

Abstract

This study aims to design novel inhibitors against influenza A virus by integrating 3D-quantitative structure–activity relationship (QSAR) modeling, molecular docking, and molecular dynamics (MD) simulations. The data set, consisting of 38 compounds, was divided into training and test sets using hierarchical clustering. The most active compound was used as a reference for molecular alignment. Comparative molecular field analysis (CoMFA), CoMFA-Focus, and comparative molecular similarity indices analysis (CoMSIA) models were developed and validated using the partial least squares method. Among them, the CoMSIA model, incorporating steric, hydrophobic, and hydrogen bond donor descriptors, demonstrated the highest predictive performance (q²_LOO = 0.681, r²_training = 0.847). Contour maps identified key regions for structural modifications to enhance inhibitory activity. Molecular docking confirmed these findings by highlighting crucial ligand–receptor interactions. Further validation through MD simulations revealed stable ligand binding with hemagglutinin, supported by root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses. The radius of gyration analysis indicated a compact ligand conformation, reinforcing its stability and strong binding affinity. Additionally, binding free energy calculations suggested favorable ligand–receptor interactions. On the basis of these insights, nine novel compounds were designed, showing promising potential for experimental validation and further development as anti-influenza A agents.