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article · Lafia Journal of Scientific and industrial Research

Computational Investigation of 2,3-Dihydroquinazolin-4(1H)-one Derivatives as Multitarget Anti-Alzheimer Agents

Abstract

Alzheimer's disease (AD) remains one of the greatest unmet medical needs worldwide and requires a multitarget therapeutic approach as a result of its multifactorial pathogenesis. The 2,3-dihydroquinazolin-4(1H)-one scaffold is a pharmacologically privileged drug known to possess neuroprotective, antioxidant, and enzyme-inhibitory activities that are relevant to AD. Six structurally diverse derivatives (3a, 3k, 3m, 3n, 3r, and 3u) were computationally investigated against three important enzymes associated with AD (acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-secretase 1 (BACE1)). Molecular electrostatic potential maps, quantum chemical reactivity descriptors, and frontier molecular orbital analysis were calculated via the density functional theory (DFT) method at the M062X/def2TZVP level. The molecular docking was done using the Glide program of Maestro against co-crystallized protein structures (PDB: 7E3H, 7AIY, 5HU1) with Rivastigmine and Verubecestat as reference drugs. DFT analysis showed that compound 3n is the most chemically reactive compound with a HOMO–LUMO gap of 6.51 eV, and compound 3r is the most electrophilic with ω = 2.40 eV. The molecular docking revealed that 3k showed the highest binding affinity towards all three targets (AChE: −11.22 kcal/mol, BChE: −8.17 kcal/mol, BACE1: −7.78 kcal/mol) as compared to Rivastigmine (AChE: −6.66 kcal/mol, BChE: −5.21 kcal/mol). Analysis of interactions indicated that aromatic π–π stacking and hydrogen bonding are the predominant types of interactions involved in the binding of AChE, hydrophobic interactions are predominant in the binding of BChE, and polar interactions are predominant in the binding of BACE1. These results identify 3k as a potential multitarget agent that requires further experimental studies

Research topics

  • Cholinesterase and Neurodegenerative Diseases
  • Enzyme function and inhibition
  • Computational Drug Discovery Methods

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DOI: 10.62050/ljsir2026.v4n2.876

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