Predictive potential of K-Banhatti and Zagreb type molecular descriptors in structure-property relationship analysis of some novel drug molecules

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.