Predicting properties of antifungal drug molecules using neighborhood degree topological indices

Topological indices are an important part of chemical graph theory as a representation of the molecular structure and a predictor of the physicochemical properties. In this paper, nine neighborhood degree sum based topological indices are use as indicators of predictive potential in a QSPR analysis of twenty antifungal drug molecules. Linear, quadratic, and cubic regression models were used to test the relationships between the structural properties and important physicochemical properties, such as boiling point, density, enthalpy of vaporization, flash point, refractive index, molar refractivity, polarizability, surface tension, and molar volume. The findings also revealed that a cubic regression model provided the most optimal overall performance with the highest level of predictability in the form of molar refractivity and polarizability being the neighborhood inverse product index [Formula: see text] ([Formula: see text] = 0.98). These results assert that neighborhood based topological descriptors are effective, especially in modeling the refractivity related and electronic properties of drug molecules.