A Mathematical Investigation of Reverse Sombor indices for Anti-Hypertensive drugs using Entropy and WASPAS method

Topological indices are numerical values obtained from molecular structures that help to describe and predict the properties of chemical compounds. In this work, we analyze the structural and physicochemical characteristics of ten anti-hypertensive drug molecules by reverse Sombor indices. This is the first work to systematically compute eight reverse Sombor degree-based topological indices for molecular graphs of anti-hypertensive drug compounds. The use of the reverse vertex degree concept allows the analysis to go beyond conventional degree-based descriptors, capturing complementary structural information not captured by standard degree-based indices. The relationship between the calculated indices and some physicochemical properties is studied using linear and quadratic regression models and the external predictive capability of the developed models is further validated through leave-one-out cross-validation (LOO-CV). The quadratic model has better predictive performance. Furthermore, the entropy weight approach is applied to assess the relative importance of the descriptors, and the weighted aggregated sum product assessment (WASPAS) approach is used to rank the drugs in a systematic way. The findings demonstrate that reverse Sombor indices effectively characterize and distinguish anti-hypertensive drug molecules. The proposed framework provides a novel computational approach for anti-hypertensive drug evaluation and ranking using the WASPAS method.