Predictive QSAR Models Followed by Toxicity, Molecular Docking, and Molecular Dynamics Simulation in Search of Azole Derivatives as AChE Inhibitors for the Treatment of Alzheimer's Disease

Abstract

The present study aims to find azole-containing acetylcholinesterase (AChE) inhibitors for the treatment of Alzheimer's disease (AD) through a mixed in silico approach. The first step involved the collection of azole derivatives and predictive quantitative structure–activity relationship (QSAR) model development for their AChE inhibition activity, using multiple linear regressions (MLRs) with the genetic algorithm (GA) for feature selection. The developed GA-MLR models were statistically robust enough internally (R²_adj = 0.643–0.640, Q²_LOO = 0.616–0.621) as well as externally (R²_pred = 0.626–0.658, R²M = 0.562–0.601). The prediction reliability of the models was assured through the leverage approach of the applicability domain. The most significant models were applied to azole-containing PubChem database compounds, which were classified as active and inactive based on theoretical predictions. The toxicity analysis was also performed for the active compounds by the online web server Protox-II. The less or nontoxic compounds were subjected to molecular docking, along with donepezil as a standard. Docking analysis revealed that the four compounds have better binding affinity (binding energy = −11.6 to −11.2 kcal/mol) as compared to donepezil (binding energy = −11 kcal/mol). Apart from binding energy, donepezil was observed to be toxic by the prediction from the Protox-II. Finally, molecular dynamics (MD) analysis of two compounds (Compound 5, having the lowest IC₅₀, and Compound 25, having the highest IC₅₀ among the top 4 docked compounds) not only differentiated them based on final interactions but also exhibited that the toxicity of donepezil might be due to hydrogen bonding with the active site.