Predictive Ability of Degree and Neighborhood Degree Sum-Based Topological Descriptors for Breast Cancer Drugs

Topological descriptors are numerical metrics of a molecular graph that do not change when the structure changes. They describe how the molecule’s bonds are arranged. The primary objective of examining topological descriptors is to encapsulate and convert chemical structural data while establishing a mathematical correlation between chemical networks and their physicochemical qualities, biological activities, and many experimental attributes. In chemical graph theory, the degree and neighborhood degree sum-based indices have been extensively investigated over the past decade. New indices are suggested and examined to enhance predictions of the target attributes of molecules, surpassing those derived from existing indices. In this research, we conduct a quantitative structure-property relationship (QSPR) analysis for 21 breast cancer therapy medicines. We create molecular graphs for these 21 breast cancer medications and then use them to calculate 10 reduced reverse degree-based indices and 28 neighborhood degree sum-based topological indices. The QSPR study for these medications employs Single Variate Regression and Multi-Variate Linear Regression methodologies, yielding findings from the analysis.