Influenza A and B viruses spread out worldwide, causing several global concerns. Discovering neuraminidase inhibitors to prevent influenza A and B viruses is thus of great interest. In this work, a machine learning model was trained and tested to evaluate the ligand-binding affinity to neuraminidase. The model was then used to predict the binding affinity of compounds from the CHEMBL database, which is a manually curated database of bioactive molecules with drug-like properties. The physical insights into the binding process of ligands to neuraminidase were clarified via molecular docking and molecular dynamics simulations. Experimental investigation on enzymatic activity validated our computational results and suggested that 2 compounds were potential inhibitors of neuraminidase of the influenza A and B viruses.
© 2024 The Authors. Published by American Chemical Society.