Curvilinear regression analysis and ranking of migraine treatment drugs using degree-based topological indices and the WASPAS method.

Topological indices, derived from molecular graphs, provide valuable numerical descriptors for the comprehensive analysis of pharmaceuticals. These indices are pivotal in the physicochemical characterization and predictive assessment of various drugs. In this study, we calculate several degree-based topological indices for a range of migraine treatment medications, including aspirin, caffeine, eletriptan, ergotamine, sumatriptan, rizatriptan, verapamil, diclofenac, frovatriptan, and droperidol. The process involves several steps: data collection on the molecular structures of migraine drugs and their corresponding biological activities, followed by the calculation of descriptors that represent key features of molecules. Calculating the values of these descriptors, we use vertex degree, edge division, and the counting degree technique. We employ curvilinear regression models, including linear, quadratic, and cubic regressions, to analyze each topological indicator. This research emphasizes the application of curvilinear regression techniques and performs extensive testing using these models to enhance the understanding of drug properties. The Weighted Aggregated Sum Product Assessment (WASPAS) method is applied to evaluate and rank these drugs based on various topological indices attributes, integrating both the weighted sum model and the weighted product model to provide a comprehensive assessment.