2D-QSAR-guided design of potent carbamate-based inhibitors of acetylcholinesterase

基于二维定量构效关系(2D-QSAR)的强效氨基甲酸酯类乙酰胆碱酯酶抑制剂设计

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Abstract

Alzheimer's disease (AD) causes a progressive decline in memory, along with impairments in other cognitive abilities. The main pharmacological target for Alzheimer's disease (AD) treatment is acetylcholinesterase (AChE), a biochemical enzyme belonging to the cholinesterase (ChE) family. In the search for novel hit compoundswith potential as future Alzheimer's therapies, a series of carbamates derivatives were designed and evaluated using computational approaches including QSAR modeling, molecular docking, ADMET profiling, and molecular dynamics simulations. The following study focused on the development of a QSAR model with satisfactory statistical properties. ADMET analysis on the designed ligands, demonstrated good pharmacokinetic properties. Molecular docking identified M6 as a promising AChE binder with a docking score of -11.200 kcal/mol, while the Donepezil control returned a docking score of -10.800 kcal/mol. The validity of the docked complex was confirmed using molecular dynamics simulations, where the trajectory plots of M6 were found to be stable and consistent over 100 ns intervals. The enclosed study highlights M6 as a novel chemical starting point (CSP) (i.e., hit compound) targeting AChE as a potential therapeutic strategy against AD.

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