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Degree-based topological co-indices for QSPR modelling of benzenoid hydrocarbons: a comparative computational study
Benzenoid hydrocarbons are structurally regular aromatic compounds, which makes them well suited for quantitative structure–property relationship (QSPR) studies. This study presents a QSPR analysis of nineteen benzenoid hydrocarbon species using degree-based topological co-indices. We developed a Python program to compute eleven degree-based topological co-indices from molecular graphs and evaluated their ability to explain variations in key physicochemical properties. Using SPSS, we evaluated linear, quadratic, and cubic regression models to correlate the computed co-indices with Henry’s law constant, molecular weight, -electron energy, and other physicochemical properties. The comparative analysis reveals that cubic regression models provide the strongest structure–property correlations, with the forgotten co-index emerging as a particularly robust descriptor for Henry’s law constant. Overall, this study demonstrates that degree-based co-indices are effective and computationally efficient descriptors for capturing structure–property trends in benzenoid hydrocarbons. The predictive stability of the proposed models is confirmed through leave-one-out cross-validation, Y-randomization, and applicability-domain analysis.