In the extrapolative validation protocol, incorporating fluid density and Gibbs free energy of solvation had little impact on model performance (Figure ). However, adding other thermodynamic properties individually led to slight improvements in predictive accuracy. When all descriptors related to solute phase changes and solvent behavior were included, predictions improved by an average of 9% in terms of RMSE. A t-test comparing the prediction results for models with and without thermodynamic parameters revealed p-values below 0.05, indicating statistically significant differences. This finding reinforces the importance of taking into account thermodynamic parameters not only for random data splits but also when predicting the solubility of entirely new molecular structures that the model has not encountered before.