  Predicting the solubility of given molecules remains crucial in the
pharmaceutical industry. In this study, we revisited this extensively studied
topic, leveraging the capabilities of contemporary computing resources. We
employed two machine learning models: a linear regression model and a graph
convolutional neural network (GCNN) model, using various experimental datasets.
Both methods yielded reasonable predictions, with the GCNN model exhibiting the
highest level of performance. However, the present GCNN model has limited
interpretability while the linear regression model allows scientists for a
greater in-depth analysis of the underlying factors through feature importance
analysis, although more human inputs and evaluations on the overall dataset is
required. From the perspective of chemistry, using the linear regression model,
we elucidated the impact of individual atom species and functional groups on
overall solubility, highlighting the significance of comprehending how chemical
structure influences chemical properties in the drug development process. It is
learned that introducing oxygen atoms can increase the solubility of organic
molecules, while almost all other hetero atoms except oxygen and nitrogen tend
to decrease solubility.
