To better understand the influence of physicochemical molecular descriptors on the prediction of intrinsic solubility, we conducted feature importance analysis using SHAP (SHapley Additive exPlanations), an explainable AI tool . SHAP analysis provides insights into the From the SHAP value analysis, molecular properties that significantly influence solubility prediction include MW (molecular weight), Mol_MR (molecular polarizability), TPSA (total polar surface area), and Mol_LogP (partition coefficient ratio of substances in water to octanol solvent). On the other hand, the top molecular features slightly differ in order across datasets. This increases the molecule's hydrophilicity, facilitating dissolution. Molecules with a larger TPSA can form more hydrogen bonds with the solvent. These interactions can stabilize the solute in solution, promoting solubility. Furthermore, certain topological properties, such as numHeteroatom (number of heteroatoms), NOCount (number of nitrogen and oxygen atoms), numHAcceptor (number of hydrogen bond acceptors), and numHDonor (number of hydrogen bond donors), can positively impact solubility by enhancing interactions with the solvent, potentially resulting in higher solubility. Figure presents the correlations between specific chemical features and logS values.