A computational ranking of thyroid drugs using topological indices, graph entropy, and multi-criteria decision-making techniques

This work employs chemical graph theory to systematically analyze the structural properties of molecules through topological indices, addressing critical challenges in drug development, including time limitations, inadequate experimental data, safety profiling, high costs, and solubility concerns. Targeting thyroid-related medications such as Corgard, Propranolol, Metoprolol, Methimazole, Levothyroxine, Thalidomide, Thyrolar, Hydrocortisone, Dexamethasone, Carbimazole, Sorafenib, and Amiodarone, we calculate degree-based topological parameters and associated descriptors to explore molecular architecture. To provide a robust evaluation of drug efficacy, this study integrates graph entropy measurements with advanced multi-criteria decision-making (MCDM) techniques, including ENTROPY, VIKOR, TOPSIS, and MABAC methods. The interplay between entropy, topological indices, and molecular features is further elucidated through logarithmic regression models, revealing intricate patterns of energetic and structural characteristics. This work combines numerical computations with graphical characterizations, offering valuable insights for drug design and performance evaluation. Its primary contribution lies in the integrated application of topological indices and MCDM techniques to a specific set of thyroid drugs, establishing a novel framework for their comparative analysis. This theoretical framework provides a foundation for the pre-screening and prioritization of drug candidates based on structural attributes. To the best of our knowledge, this is the first study to employ such an integrated approach, enabling the identification of key structural descriptors for predicting molecular complexity and determining drug rankings. • Combines chemical graph theory with multi-criteria decision-making to assess thyroid drugs. • Computes degree-based topological indices and graph entropy to quantify molecular structure. • Uses logarithmic regression to reveal strong correlations between entropy and topological indices. • Evaluates 12 thyroid-related medications, providing detailed rankings with methods like VIKOR, TOPSIS, and MABAC. • Offers insights that could guide more effective drug design and performance evaluation.