Ranking Antidiabetic Drugs Using a Multi-criteria Decision-makingApproach Based on Domination Distance-Based Topological Indices andQSPR Modeling

Introduction: Diabetes is a rapidly increasing metabolic disorder influenced by lifestyle and diet. Therefore, identifying effective therapeutic agents is of great importance. Chemical graph theory, through topological indices, helps relate molecular structures to physicochemical and thermodynamic properties. However, the application of domination distance-based topological indices (DDTIs) for quantitative structure-property relationship (QSPR) modeling and the ranking of antidiabetic drugs remains largely unexplored. This study investigates the relationships between DDTIs and the physicochemical properties of antidiabetic drugs using a QSPR model and ranks the drugs based on these indices integrated with multi-criteria decision-making (MCDM) methods. Methods: A QSPR approach is employed using DDTIs. Cubic regression is applied to model the relationships between these indices and key physicochemical properties. To identify the most promising drug candidates, MCDM methods, namely, the technique for order preference by similarity to ideal solution (TOPSIS), weighted sum method (WSM), and weighted product method (WPM), are applied based on the calculated DDTIs. Results: Strong correlations are observed between the DDTIs and the selected physicochemical properties, enabling the development of effective predictive models. Eighteen antidiabetic drugs are ranked using TOPSIS, WSM, and WPM, integrated with DDTIs, with high consistency among the rankings, demonstrating the robustness of the approach. Discussion: The utility of domination distance-based indices in predicting drug properties and the effectiveness of MCDM methods in drug prioritization is highlighted. While the results align with previous QSPR studies, further validation with larger datasets is recommended. Conclusion: The findings demonstrate the predictive potential of DDTIs and the effectiveness of MCDM methods for drug prioritization. This framework enables the prediction and ranking of antidiabetic drugs, aiding the discovery of effective therapeutic candidates.