TOPOLOGICAL INDICES AND REGRESSION MODELS FOR QSPR ANALYSIS OF CERTAIN ALZHEIMER'S DRUGS

Abstract

In order to anticipate the physicochemical and biological properties of pharmacological compounds using mathematical descriptors, Quantitative Structure–Property Relationship (QSPR) analysis is essential. In this research, a QSPR analysis is performed on selected compounds related to Alzheimer’s disease (AD) utilizing degree-based topological indices derived from their molecular graphs. Various well-known topological indices, including the Wiener index, Zagreb indices, Randić index, ABC index, GA index, and associated descriptors, are computed. These indices are correlated with significant physicochemical characteristics such molecular weight, polar surface area, and molar refractivity using linear and multiple regression models. The statistical performance of the models is evaluated using correlation coefficient (R), coefficient of determination (R²), and standard error. The findings demonstrate that topological indices are effective molecular descriptors for predicting properties of Alzheimer’s drugs and provide insights into structure–property relationships.