6th Edition of World Nursing Science Conference 2026

Abstract

The impact of Alzheimer’s disease is enormous. Currently, more than 900,000 people live with the diagnosis, with their loved ones, who assume the care and the enormous economic and psychological costs that accompany the disease. If no cure is found, by 2050 this figure could triple globally. Approved anti-Alzheimer's medications do not cure Alzheimer's disease but aim to delay the onset of clinical symptoms. The current study relies on several molecular modeling approaches to develop novel potent AChE inhibitors. We conducted a 2D QSAR study using multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogues, which were recently synthesized and demonstrated their activities inhibitors against acetylcholinesterase (AChE). Molecular descriptors, such as polar surface area, dipole moment, and molecular weight, are identified as the key structural properties governing the AChE inhibitory activity of the analogs in the database. The multiple linear regression (MLR) model is chosen according to its statistical parameters: R²=0.86, R² _test =0.79, Q²_CV =0.70, RMSE=0.336, demonstrating its predictive reliability. Validation tests were adopted to verify the robustness of the model. As a result, 11 new molecules were designed with higher anti-Alzheimer activities. Their improved pharmacokinetic properties were demonstrated by an in silico ADMET study. Molecular docking was performed to explore the mechanisms of AChE inhibition and binding affinities in the active pocket. The binding scores of compounds M1, M2, and M6 were (-11.90 kcal/mol), (-12.52 kcal/mol), and (-13 kcal/mol), respectively, which are higher than the inhibitor standard Rivastigmine with a binding score of (-8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the