Estimating the prediction ability of reverse degree-based entropy indices for the physicochemical properties of lymes disease drugs

Lyme disease, caused by the bacterium Borrelia burgdorferi and transmitted through infected black-legged ticks, remains a significant health concern due to its potential for severe complications, including arthritis, neurological disorders, and cardiac issues. Early diagnosis and treatment are essential to prevent these outcomes. This study explores the predictive potential of reverse degree-based entropy indices for analyzing the molecular structures of therapeutic compounds used in Lyme disease treatment. While the use of topological indices for predicting physicochemical properties is well-established, our research uniquely integrates reverse entropy indices with a computational framework to refine the prediction process. We focus specifically on antibiotic drugs such as doxycycline, ceftriaxone, Doxy 100, cefotaxime, Ceftin, Cefuroxime, Erythromycin, EryPed, Erythrocin Lactobionate, Ofloxacin, Moxifloxacin, amoxicillin, and penicillin G potassium—commonly used to treat Lyme disease—and leverage a novel Maple-based algorithm for calculating reverse degree-based entropy indices. SPSS software was employed to assess correlations between these indices and critical physicochemical properties, such as molecular weight (MW), complexity (C), molar volume (MV), and XLog P. Unlike traditional experimental methods mandated by regulatory authorities for Chemistry, Manufacturing, and Controls (CMC) processes, our approach provides a supplementary predictive framework to streamline early-stage drug property estimation. The results reveal that first reverse Zagreb entropy effectively predicts molecular weight, reverse atom bond connectivity entropy effectively predicts complexity, reverse augmented Zagreb entropy effectively predicts molar volume and reverse geometric arithmetic entropy effectively predicts molecular XLog P. This study not only advances the computational methodology by employing novel combinations of entropy indices but also builds on existing work by focusing on a specific subset of Lyme disease drugs. While this framework offers a cost-effective preliminary tool for predicting physicochemical properties, it complements rather than replaces rigorous experimental validation required for regulatory reporting. These findings lay the groundwork for integrating computational and experimental methods, potentially accelerating drug development and enhancing therapeutic precision for Lyme disease.