MolWt is one of the major factors affecting aqueous solubility. Generally, compounds with lower molecular weights are found to be more soluble in water . In this work, partitioning the entire dataset based on median molecular weight leads to a noticeable difference in solubility densities, shown in Figure which aligns with . Aqueous solubility shows a strong linear dependence on MolLogP in addition to MolWt . In the following analyses, we first adjust for these effects using a linear regression. Subsequently, we run our nonlinear regression analysis and evaluate the importance of chemical and graphical descriptors in the dataset. This methodology aims to capture the nonlinear association of solubility with chemical and graphical descriptors in the dataset. We also exclude MolMR due to its strong linear correlation with the partitioning variable MolWt (Figure ), which could introduce bias into our analysis. Also, NumHeteroatoms is omitted since HeavyAtomCount is already included in the model. Balaban J is excluded as it, being a graphical index, is already taken into account implicitly in the graphical descriptors used in this work. Figure outlines our overall analysis steps, where the blue highlighted arrows indicate the preprocessing of the data set, and the green dotted arrows indicate the downstream analyses using non-linear regressors.