Exploring entropy measures with topological indices on eye disorder using curvilinear regression analysis

Abstract

Topological indices (TIs) of chemical graphs representing pharmaceutical compounds provide valuable computational tools for predicting essential properties and
biological activities, enabling more informed drug design strategies. In this investigation, we focus on medications used to treat various ocular disorders, including, Cataract, Glaucoma, Diabetic retinopathy and Macular degeneration. Our research integrates computational modeling with decision-making approaches to establish a cost-effective methodology for understanding molecular behavior. We employ linear, quadratic and cubic regression analysis to develop Quantitative Structure-Property Relationship (QSPR) models. Our selection criteria prioritize topological indices demonstrating significant correlation (r > 0.9) with key physicochemical properties. This approach facilitates the identification of robust structure-property relationships that can guide the development of novel ophthalmic therapeutics. The resulting models provide predictive capabilities that may reduce experimental costs and accelerate drug discovery timelines.