ABSTRACT Alzheimer's disease (AD) involves multiple pathogenic pathways, yet current therapeutic strategies remain largely symptomatic and focused on single molecular targets, underscoring a critical existing gap, emphasizing the need for rational multitarget drug design. Addressing this limitation, the present study employed an integrated in silico framework to identify multitarget 1,3,4‐oxadiazole derivatives against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and glycogen synthase kinase‐3 β (GSK3β). A dataset of 273 reported compounds was used to develop robust QSAR models via Genetic Algorithm‐Multiple Linear Regression (GA‐MLR), exhibiting strong internal ( R 2 = 0.638–0.758, Q 2 LOO = 0.609–0.736) and satisfactory external predictivity ( Q 2 F1 − Q 2 F2 = 0.566–0.800). Subsequent virtual screening of 3404 1,3,4‐oxadiazoles from BindingDB identified 72 promising hits (IC₅₀ ≤ 300 nM), which underwent molecular dynamics (MD) docking. MD‐based CDOCKER analysis highlighted compound 2851 with superior binding energies and stable key interactions compared to donepezil, supported by binding free energy (Δ G ) calculations. ADMET evaluation indicated favorable pharmacokinetic and toxicity profiles, while density functional theory (DFT) analysis revealed enhanced reactivity and lower band gaps. This integrated computational workflow identified compound 2851 as a promising MT therapeutic agent for AD.
Chhabra et al. (Sun,) studied this question.