Topological modeling and QSPR based prediction of physicochemical properties of bioactive polyphenols

Molecular graph theory provides a powerful mathematical framework for representing chemical structures, where atoms and bonds are modeled as vertices and edges of a graph. Topological indices, derived from these graphs, serve as numerical descriptors capturing the structural features of molecules. These indices are widely applied in Quantitative Structure-Property Relationship (QSPR) analysis to predict the physicochemical behavior of chemical compounds. In this study, we investigate a novel class of bioactive polyphenols-namely ferulic acid, syringic acid, p-hydroxybenzoic acid, benzoic acid, vanillic acid, and sinapic acid-well known for their antioxidant, anti-inflammatory, antibacterial, anticancer, and antiviral properties. Using several widely recognized degree-based topological indices, we construct molecular graph models of these polyphenols and establish linear regression models correlating the computed indices with essential physicochemical properties. Our QSPR analysis demonstrates strong predictive correlations, highlighting the potential of graph-theoretical descriptors in rational drug design and bioactivity prediction. The results validate the utility of topological indices as efficient computational tools in cheminformatics, offering valuable insights for future applications in pharmaceutical chemistry and material sciences.