Prediction of Molecular Physicochemical Properties Using Molecular Structure and Characteristics

The prediction of molecular physicochemical properties—such as solubility, hydration free energy, and lipophilicity—plays a crucial role in multiple fields, including drug discovery and materials science. Current prediction methods predominantly fall into two categories: machine learning methods based on molecular descriptors and deep learning methods that learn representations directly from molecular structures. However, when evaluated under rigorous scaffold splitting, these methods often exhibit limited generalization capability, resulting in unsatisfactory prediction accuracy. In this study, a method named PMPP is proposed to predict the physicochemical properties of molecules, which effectively utilizes enhanced graph representations that capture molecular motif structures and optimized selection of pharmacophore and physicochemical characteristics. The framework of PMPP contains three parts: molecular structure representation extraction, molecular characteristics extraction and optimized selection, and molecular physicochemical property prediction model construction. Experimental results demonstrate that PMPP achieves superior predictive accuracy, with lower Root Mean Square Error (RMSE) scores on three benchmark datasets: 0.787 (ESOL), 1.875 (FreeSolv), and 0.615 (Lipophilicity). These results outperform six state-of-the-art methods, including GROVER, MGSSL, MolCLR, KANO, HiMol and Mol-AE. Furthermore, our analysis reveals that robust prediction relies not only on molecular graph and motif representations but also on key pharmacophore and physicochemical characteristics—such as hydrophobic group count and LogP—which show strong correlations with the target molecular properties.