A Graph-Theoretical Approach to Bond Length Prediction in Flavonoids Using a Molecular Graph Model

The accurate determination of bond lengths is fundamental to understanding molecular geometry and the physicochemical behavior of chemical compounds. However, obtaining these measurements is often challenging, as both experimental techniques and advanced quantum-chemical methods are complex, computationally demanding, and costly to apply across diverse molecular systems. In this work, we present a novel graph-theoretical model for predicting bond lengths in flavonoid molecules based on molecular descriptors derived from atomic and topological parameters. By integrating atomic electronegativity with graph-based descriptors, such as the weighted second-order neighborhood, the proposed model predicts the bond lengths of luteolin with a coefficient of determination of R2=0.990. This approach offers a computationally efficient and highly accurate alternative to conventional experimental and theoretical methods, providing a practical framework for bond length estimation when experimental data are unavailable.