Computational discovery and dynamic profiling of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer's disease.

Alzheimer's disease is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction and neuroinflammation. Dual inhibition of acetylcholinesterase and monoacylglycerol lipase has emerged as a promising therapeutic approach. This study employed an integrative in silico workflow to identify potential dual acetylcholinesterase/monoacylglycerol lipase inhibitors from a molecular library derived from known inhibitors (rivastigmine, JZL-184, ABX-1431). A total of 365 compounds were screened via molecular docking, interaction-based filtering, ADME/toxicity prediction, and molecular dynamics simulations. Among them, compound H34 demonstrated a comparatively favorable overall computational profile, supported by MM/GBSA binding-energy estimates (ΔGbind = - 30.96 and - 37.34 kcal/mol) and comparatively favorable structural stability metrics (RMSD, RMSF, Rg, and SASA) in the MD simulations. Further ProLIF interaction mapping and free energy landscape analysis supported the persistent interaction profile and conformational behavior of the H34-protein complexes. Additionally, H34 displayed favorable pharmacokinetic properties and low predicted acute toxicity. These results highlight H34 as a promising dual-target candidate for Alzheimer's disease therapy and illustrate the effectiveness of integrated computational strategies in early-stage drug discovery.