Prediction of physicochemical properties of organic compounds using degree-based topological indices and machine learning models (opens in new tab)

This study presents a quantitative structure-property relationship (QSPR) framework that integrates graph theory with machine learning to predict key physicochemical properties of diverse organic compounds. A data set of 275 structurally diverse organic compounds, including alkanes, alkenes, alkynes, cyclic systems, and aromatic hydrocarbons–was represented as molecular graphs, and their topological features were encoded using two complementary degree-based indices: the Sombor index (SO) and ...