The results indicate that incorporating individual thermodynamic parameters -such as T m , ∆H v , ∆G solv and ρ CO 2 -into molecular CDK descriptors, as well as their various combinations, consistently reduced errors compared to the baseline model. For graph-based models, these thermodynamic characteristics further improved the internal graph representations, leading to even lower prediction errors. The GCNN:RDKit models outperformed traditional CatBoost:CDK models, reducing prediction error by an average of 12%. A scat-ter plot illustrating the performance of the GCNN model with all descriptors is provided in Figure .