Topological indices (TIs) can be used to forecast molecules' biological activity, physicochemical features, and toxicity. The use of topological indices and regression analysis can help us better understand drug behavior and contribute to the development of tailored drugs. This study investigates the predictive potential and efficacy of K-Banhatti and Zagreb type degree-based topological indices in quantitative structure–property relationship (QSPR) analysis of a comprehensive set of medications used for diabetes type-I and type-II disease. The K-Banhatti and Zagreb type degree-based topological indices are computed for 14 anti-diabetes drug molecules using edge/vertex partitioning techniques. By leveraging these topological indices, QSPR regression models are developed to predict the physicochemical properties of the understudy drugs. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the anti-diabetes drugs. Furthermore, the comparative analysis revealed that, for all the considered properties except enthalpy of vaporization, Zagreb type indices outperform K-Banhatti indices with high predictive ability. Hence, it can be concluded that the Zagreb type indices are the best alternatives to theoretically predict the properties of anti-diabetes drugs. This theoretical analysis can help chemists in their right choice of the topological indices to theoretically predict the properties of anti-diabetes drugs without going into laborious experimentation.

中文翻译：

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K-Banhatti 和 Zagreb 型分子描述符在一些新型药物分子结构-性质关系分析中的预测潜力

拓扑指数（TI）可用于预测分子的生物活性、理化特征和毒性。拓扑指数和回归分析的使用可以帮助我们更好地理解药物行为并有助于定制药物的开发。本研究探讨了 K-Banhatti 和 Zagreb 型基于程度的拓扑指数在用于治疗 I 型和 II 型糖尿病的一组综合药物的定量结构-性质关系 (QSPR) 分析中的预测潜力和功效。使用边/顶点划分技术计算 14 种抗糖尿病药物分子的 K-Banhatti 和 Zagreb 型基于度数的拓扑指数。通过利用这些拓扑指数，开发了 QSPR 回归模型来预测候选药物的理化性质。结果表明，这些拓扑指数的值与抗糖尿病药物的某些理化性质高度相关。此外，比较分析表明，对于除汽化焓之外的所有考虑的特性，Zagreb 型指数均优于 K-Banhatti 指数，具有较高的预测能力。因此，可以得出结论，萨格勒布型指数是理论上预测抗糖尿病药物特性的最佳替代方案。这种理论分析可以帮助化学家正确选择拓扑指数，从理论上预测抗糖尿病药物的特性，而无需进行繁琐的实验。