The transformation of input data into output predictions is often complex and challenging to interpret, particularly for vector representations derived from natural language processing methods and graph-based approaches. In this study, we conducted a feature importance analysis for the CatBoost:CDK with TP model using the complete dataset. Figure illustrates the contributions of ten most influential features on the prediction of solubility in scCO 2 . The analysis was performed using 5CV with a data splitting protocol based on unique SMILES identifiers. The bee swarm plot depicts the impact of each feature on the model's predictions, where each point represents an individual compound with its corresponding solubility, and the SHAP value is plotted on the abscissa axis. The interpretation of the results (Figure ) reveals that the added physicochemical descriptors play a pivotal role in the model's performance. Solubility is governed by the balance between the solute-solvent interaction energy and the energy required to disrupt the crystal lattice or intermolecular bonds within the solute. For compounds with high melting points and enthalpies of vaporization, the energy needed to break these bonds significantly exceeds the interaction energy with scCO 2 , which, as shown in the figure, reduces solubility. Notably, the Gibbs free energy of solvation ranked only in the thirtieth tier of importance. This may be due to high uncertainty associated with its theoretical estimation, where the input parameters (predicted critical parameters) could contain systematic errors. Additionally, its contribution may be implicitly captured by other descriptors in the model.