In this section, we first describe our proposed model to characterize the solubility of small molecule drug systems based on their chemical and structural features as predictors. Although the fully nonparametric regression models considered in the previous section are flexible, they do not explicitly take into account the natural grouping among the predictors, and thus difficult to interpret the interaction between these two types of features on solubility. We thus propose a novel two-layer machine-learning model that enjoys flexibility and also offers transparent characterizations of the effects of chemical attributes and structural properties. It builds on the framework of the linear regression model with interaction. Specifically, an easy-to-interpret linear model for the solubility (y i ) in case of one unique summary chemical descriptor, u i and one unique structural descriptor, v i of the i-th molecule, is