Various approaches have been suggested to enhance the understanding of quantitative structureactivity relationships (QSAR) in computational modeling for predicting chemical and biological processes. These relationships can be determined by encoding structural descriptors using information at the chemical or molecular level. Historically, numerous efforts have been made to link molecular graphs with molecular representation. Metrics like Wiener's indices , Balaban indices , Hosoya indices , and Randic's indices , as well as other graphical properties such as eccentricity index and centrality, have been popularized in chemical graph theory in the past. Tailored descriptors are crafted using molecular graph theory, which serves as a key tool for converting molecular structures into numerical topological indexes . With recent developments, molecular representation is related to 0D,1D, 2D, 3D, and 4D descriptors. Geometrical 3D/4D descriptors indeed possess more information than simpler descriptors such as counting descriptors or topological descriptors, which usually display significant degeneracy. Developing an interpretable machine-learning method for understanding aquoeous solubility is a challenging but important task. While considering molecular graphs as descriptors, one may consider models based on graph neural nets or apply other traditional machine learning models based on the graph-theoretic attributes as features, similar to the application areas such as neuroscience , social network analysis , etc. Unlike the former, the latter approach offers a direct way to assess how molecular structural features influence solubility. In this paper, we adopt the latter approach and also incorporate the chemical features, enabling a straightforward comparison of the importance of both chemical and structural attributes. In this work, we primarily focus on 2D descriptors and the corresponding topological descriptors using graph attributes. We specifically examine the interpretability of the effect of molecular weight on aqueous solubility via graphs and physicochemical descriptors. While the relationship between molecular weight and solubility influences other factors such as lipophilicity , we anticipate differences in molecular weight leading to differences in how structural and chemical features affect solubility.