Quantitative Structure Property Relationship (QSPR) modeling is critical for predicting physicochemical properties of molecules, supporting drug discovery and material design. Traditional methods often use complex descriptors or opaque models, which limits interpretability.
There is a need for QSPR approaches that balance accuracy and interpretability to elucidate structural influences on molecular properties.
We introduce a QSPR method using degree-distance-based topological indices (TIs) derived from vertex edge weighted (VEW) molecular graphs, weighted by atomic number, mass, radius, density, electronegativity, and ionization energy. This approach captures detailed molecular connectivity and bonding while prioritizing interpretability.
Using a 166-molecule dataset, our models -Ridge Regression, Random Forest, XGBoost, and Neural Networks- achieved high prediction accuracy for six physicochemical properties. Regularization ensured robust predictions. The performance metrics tables and the TI correlations clarified the structure-property relationships. This efficient and interpretable framework accelerates drug discovery by enabling virtual screening and informed molecular design.