Predicting flavonoid physicochemical properties using topological indices and regression modeling - Huili Li; Shamaila Yousaf; Komal Shahzadi; Sobhy M. M. Ibrahim; Adnan Aslam; Guoping Zhang; Keneni Abera Tola (Scientific Reports)
Abstract 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.