Molecular Insights into Anti-Alzheimer's Drugs Through Eccentricity-Based Predictive Mathematical Modeling Using Regression and QSPR Analysis

In this study, we suggest an eccentricity-based linear regression model to predict the effectiveness of anti-Alzheimer's drugs such as Tacrine, Donepezil, Rivastigmine, Butein, Licochalcone-A and Flavokawain-A. The relationship between structural characteristics and therapeutic effectiveness by integrating eccentricity values is derived from these drugs. Our findings provide promising insights into the potential use of eccentricity-driven linear regression as a drug discovery tool in the field of Alzheimer's disease treatment. This method provides an improved comprehension of the structural factors influencing drug efficacy, establishing the way for more targeted and efficient anti-Alzheimer's drug development. Moreover, a quantitative structure-property relationship model is developed by using linear regression model to predict properties such as boiling point, flash point, molar volume, molecular weight, complexity and polarizability.