Combining QSPR modeling and the VIKOR method to rank opioid analgesic drugs via topological indices

Opioid analgesic drugs are widely used in modern medicine for pain management, anesthesia, and palliative care, making the study of their physicochemical properties essential for drug design and optimization. This study aims to investigate the potential of domination degree-based topological indices (DTIs) in quantitative structure–property relationship (QSPR) modeling and to apply a decision-making approach for ranking opioid analgesic drugs. QSPR models were developed using DTIs of chemical graphs, with logarithmic and multilinear regression analyses employed to establish correlations between DTIs and key physicochemical properties. In addition, the VIKOR multi-criteria decision-making method was integrated with QSPR analysis to systematically rank twenty opioid analgesic drugs. The results revealed strong correlations between DTIs and physicochemical properties, confirming their predictive ability, while the VIKOR-based ranking was found to be highly consistent across properties. These findings highlight the reliability of DTIs in predicting drug characteristics and demonstrate the practical utility of combining QSPR modeling with decision-making tools for drug characterization, discovery, and prioritization.