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

Shafiqahmad Yellur; Prashant Patil

doi:10.5269/bspm.78300

Shafiqahmad Yellur Jain College of Engineering, Belagavi https://orcid.org/0009-0001-3929-7601
Prashant Patil Jain College of Engineering, Belagavi

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.

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Author Biography

Shafiqahmad Yellur, Jain College of Engineering, Belagavi

Experienced Lecturer with a demonstrated history of working in the research industry. Skilled in Python (For Mathematics), Matlab, Microsoft Excel, Microsoft Word, Public Speaking, and Microsoft Office. Strong education professional with a Master's degree focused in Mathematics from Karnatak University.

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