Inverse Degree-Based Topological Indices for QSPR Modeling of Anti-Babesiosis Drugs: A Computational Approach

Abstract In this study, we investigate the utility of inverse degree-based topological indices in modeling the physicochemical properties of anti-babesiosis drugs via quantitative structure–property relationship (QSPR) analysis. Thirteen drug molecules were analyzed using indices such as M2-1, KCD2-1, GO2-1, and ISD-1. Linear regression was applied to correlate these indices with experimentally known properties, including molar refractivity, polarizability, boiling point, polar surface area, and molar volume. Statistical validation was conducted using the Shapiro–Wilk test and Q–Q plots to assess the normality of residuals, while cross-validation (CV R2) was used to evaluate generalization performance. The model predicting molar refractivity from M2-1 achieved the highest fit (R2 = 0.980), though some CV R2$ values suggested potential overfitting. This research contributes to computational drug discovery by demonstrating the predictive power of inverse topological indices, offering a promising framework for screening and evaluating new compounds for the treatment of babesiosis. Classification of Mathematics Subjects: 05C92, 05C09, 92E10