Developing a QSPR model for Alzheimer's drugs using topological indices and M-polynomial: A computational study

Topological indices, which are numerical descriptors that encode molecular structure, are widely used in computational drug discovery due to their efficiency and interpretability. In this study, we developed a robust quantitative structure-property relationship (QSPR) framework to predict the core physicochemical properties of nine clinically relevant Alzheimer's disease drugs, including Donepezil, Galantamine, and Memantine.​ We employed a streamlined computational approach, using MATLAB and the M-polynomial method, to efficiently calculate a series of degree-based topological indices. Through comprehensive regression analyses, we identified strong correlations between degree-based topological indices and key physicochemical properties, including boiling point and molar refractivity. While linear models provided a reasonable baseline, nonlinear models, particularly cubic and power equations, delivered significantly improved predictive accuracy. The analysis highlighted the critical interplay between the choice of the index and the regression model. For instance, the cubic model was frequently the most effective for predicting properties such as boiling point and flash point, while the power model performed best for molar refractivity and polarizability. Notably, the redefined first Zagreb index and the modified first Zagreb index exhibited exceptional predictive capacity, reflecting their sensitivity to structural features that govern physicochemical behavior. The strong performance of these QSPR models underscores their potential to accelerate the rational design of Alzheimer's therapeutics. By enabling rapid, cost-effective, and reliable property prediction prior to synthesis, this framework offers a valuable tool for future drug development efforts.