Computational identification of novel acetylcholinesterase inhibitors as potential treatments for Alzheimer's disease

Abstract

Background Alzheimer's disease represents a major public health issue that affects millions of people worldwide. Although symptomatic treatments are available, they neither prevent nor halt disease progression; therefore, it is necessary to develop new therapeutic alternatives. Objective To identify acetylcholinesterase inhibitors with potential biological activity through a computational protocol. Methods In this study, a computational approach based on virtual screening, molecular docking, and molecular dynamics simulations was applied to identify new potential acetylcholinesterase inhibitors. Results The results allowed the identification of three compounds with higher binding affinities than donepezil, which was used as a reference. Among them, ligand code 24771824 stood out for establishing hydrophobic and aromatic interactions that maximize dispersive contributions and promote a rigid and stable conformation within the active site. In contrast, ligand codes 151171 and 21081761 were favored by more directional polar contacts, which increased specificity but limited the overall affinity toward the enzyme. Conclusions Altogether, the free energy, structural fluctuation, hydrogen bond occupancy, and molecular clustering analyses suggest that 24771824 exhibits the most favorable energetic and dynamic behavior, consolidating it as the best candidate for future experimental validation.