Flavonoids, a diverse class of polyphenolic phytochemicals, exhibit multifaceted biological activities critical to human health. This study leverages degree-based topological indices (TIs) to predict six physicochemical properties of sixty flavonoids using linear, quadratic, and logarithmic regression models. Statistical validation via correlation coefficients ( \(r^2\) ), Root Means Square Error (RMSE), and Mean Absolute Error (MAE) revealed robust predictive power, particularly for molar refractivity ( \(r^2=0.875\) , RMSE \(=8.12\, {\rm{cm}}^3\) , MAE \(=4.43 \,{\rm{cm}}^3\) ), molar volume ( \(r^2=0.814\) , RMSE \(=25.61\, {\rm{cm}}^3\) , MAE \(=18.67 \,{\rm{cm}}^3\) ), and enthalpy of vaporization ( \(r^2=0.568\) , RMSE \(= 15.89\, {\rm {kJ/mol}}\) , MAE \(= 10.47\, {\rm {kJ/mol}}\) ). Quadratic models consistently outperformed linear/logarithmic approaches, indicating nonlinear relationships between TIs and properties. The methodology offers a cost-effective tool for prioritizing bioactive flavonoids in drug discovery, validated by strong agreement between predicted and experimental values for external compounds (e.g., Procyanidin B2: molar refractivity RMSE \(= 10.56\, \rm{cm}^3\) ). This work bridges cheminformatics and QSPR, enabling rapid property estimation for polyphenolic systems.
