Understanding aqueous solubility is an essential step for drug discovery, impacting many downstream applications. In this work, we introduce an interpretable nonparametric additive model that combines graph-based features with global chemical descriptors of molecules. This model helps to provide a more interpretable framework for integrating chemical and graphical features, later quantified using a weighted adjacency matrix, to learn the aqueous solubility of drugs. The additive characterization is shown to perform better than the traditional machine learning methods, in addition to its ability to produce interesting insights into solubility.