QSPR Analysis of Some Alzheimer’s Compounds via Topological Indices and Regression Models

Abstract

Neurodegenerative diseases (NDDs) have received considerable interest from scientists for a long time due to their multifactorial nature. Alzheimer’s disease (AD) is of particular importance among pathologies, and despite approved drugs for its treatment, there is no effective pharmacological therapy to stop, halt, or repair neurodegeneration. The U.S. Food and Drug Administration (FDA) has approved certain medications to treat AD, including galantamine, donepezil, rivastigmine, memantine, tacrine, suvorexant, brexpiprazole, butein, and Licochalcone A. Topological indices and quantitative structure-property relationships (QSPRs) are indispensable in drug discovery. They allow researchers to analyze, compare, and predict the properties of chemical compounds, thereby expediting the identification of promising drug candidates while minimizing experimental costs and efforts. Regression models are vital in QSPR analysis, especially when dealing with topological indices. They facilitate quantifying the relationship between chemical structure and properties, thereby facilitating drug design, material discovery, and other chemistry-related applications. The objective of this study is to examine the efficacy of descriptors in correlating the physicochemical features of molecules associated with Alzheimer’s disease. We will use the computational method to calculate degree-related, distance-related, and eccentricity-based topological indices for any chemical graph. QSPR models are developed utilizing degree-based, distance-based, and eccentricity-based topological indices to estimate some physicochemical properties of AD drugs, including molecular weight (MW), boiling point (BP), topological polar surface area (TPSA), complexity (C), polarizability (P), and refractive index (R). The QSPRS studies are obtained using the linear regression technique. The present study found that the topological indices Randic index R, first Zagreb index M1, and atom-bond connectivity index ABC provide valuable insights into the structure-activity relationships of different drugs and help in designing more effective combinations for treating Alzheimer’s disease.