Comparative QSAR and Q-RASAR modeling of benzimidazole derivatives as dual AChE/BuChE inhibitors and design of novel anti- Alzheimer drug candidates supported by molecular docking, ADMET and molecular dynamic simulation.

Alzheimer's disease is a progressive neurological disorder characterized by memory loss, cognitive decline, and behavioral changes, and is one of the most important causes of dementia. Benzimidazole derivatives, as bioactive compounds, are promising candidates for the design of new anti-Alzheimer drugs due to their diverse biological activities. In this study, a dataset of 193 benzimidazole derivatives, whose IC50 values were experimentally measured and previously reported, was used to investigate anti-Alzheimer activity. All modeling and computational analyses were performed based on these experimental data. First, a comparative study between Quantitative Structure-Activity Relationship (QSAR) and Quantitative Read-Across Structure-Activity Relationship (Q-RASAR) methods was conducted using Multiple Linear Regression (MLR) and Support Vector Regression (SVR). Model performance was evaluated using statistical parameters such as Q2, RMSE, and R, showing that Q-RASAR models had better predictive accuracy, stability, and interpretability. Molecular docking studies were then performed to investigate ligand-target interactions. Based on the results, a set of new compounds was designed and their biological activities were predicted using the developed models. Selected compounds were further validated through molecular docking and molecular dynamics simulations to assess complex stability. In addition, ADMET analyses were conducted to evaluate pharmacokinetic properties and toxicity. Among the designed compounds, compound 5 showed the best overall profile and was introduced as a lead candidate for further studies. This work provides valuable insights for the rational design of benzimidazole-based anti-Alzheimer agents and supports future research in this field.