QSAR-driven discovery and ranking of potential anti-H1N1 inhibitors

The Influenza A virus (IAV), a major respiratory threat to humans, owes its pandemic potential to a high rate and capacity for genetic reassortment. A pivotal factor in its propagation is neuraminidase (NA), a surface glycoprotein that mediates the release of newly synthesized viral particles. In this work, we undertook a comprehensive in silico investigation of 168 candidate molecules targeting NA, integrating quantitative structure-activity relationship (QSAR) modeling, molecular docking, and molecular dynamics (MD) simulations. The QSAR model, constructed using 2D descriptors selected for their mechanistic relevance and computational simplicity, showed strong predictive power (R² = 0.82; Q² = 0.81), in line with OECD validation standards. Top-ranking compounds based on docking scores underwent ADMET screening, followed by MD simulations to evaluate complex stability. Subsequently, free energy calculations using the MM/PBSA approach were performed to estimate the binding affinities of the most promising ligand–protein complexes, providing deeper thermodynamic insight into their interaction profiles. Among the molecules tested, several candidates stood out for their favorable binding behavior and pharmacokinetic properties, offering a promising basis for the future development of anti-influenza drugs.