On QSPR analysis for predicting efficacy of physicochemical properties of antibiotics drugs via topological indices and regression models

Quantitative structure property relationship(QSPR) has emerged as an indispensable tool for the estimation of physicochemical properties in drug molecules using mathematical and computational methods. Here, we introduce novel reverse degree based topological indices to see their applicability in case of selected antibiotic compounds property prediction. Reliable models to predict properties such as the boiling point, molar refractivity and enthalpy of vaporization exist to correlate molecular structure with experimentally reported physicochemical parameters. We have analyzed structurally different antibiotics with regression models developed in Python and SPSS in order to guarantee the robustness and reproducibility. We note here that predictive measures of cubic regression models seem to perform better, as observed through generally greater correlation coefficients. The results show that the reverse topological indices are efficient for recording structural differences in antibiotic molecules and they can be excellent descriptors for predicting their physical and chemical properties. It also stresses that, the use of reverse degree based descriptors on antibiotic compounds is new, providing a basis for further QSPR modeling for more general drug families. This work is part of a growing trend to study the interfaces between graph theory and cheminformatics where new indices help to improve our understanding over molecular properties with importance for drug design.