Prediction of Physicochemical Properties of Flavonoids via Modified Reverse Degree Topological Indices

Flavonoids are naturally occurring compounds with diverse pharmacological properties, including antioxidant, anti-inflammatory, and anticancer activities. They are widely used to treat cardiovascular disorders, neurodegenerative diseases, diabetes, and certain cancers. Understanding their physicochemical properties is important for drug design and development. In this study, we employ modified reverse degree topological indices to predict the physicochemical properties of flavonoid-based drug molecules, such as boiling point, enthalpy of vaporization, flashpoint, polarization, and molar volume. In addition, we develop quantitative structure-property relationship (QSPR) models using linear, quadratic, and cubic regression analyses to estimate the predictive power of these indices. Our results demonstrate that modified reverse degree topological indices achieve high correlation coefficients, outperforming conventional indices in specific cases. This study strengthens their importance in QSPR modeling and their potential applications in drug discovery.