  Determining the aqueous solubility of molecules is a vital step in many
pharmaceutical, environmental, and energy storage applications. Despite efforts
made over decades, there are still challenges associated with developing a
solubility prediction model with satisfactory accuracy for many of these
applications. The goal of this study is to develop a general model capable of
predicting the solubility of a broad range of organic molecules. Using the
largest currently available solubility dataset, we implement deep
learning-based models to predict solubility from molecular structure and
explore several different molecular representations including molecular
descriptors, simplified molecular-input line-entry system (SMILES) strings,
molecular graphs, and three-dimensional (3D) atomic coordinates using four
different neural network architectures - fully connected neural networks
(FCNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and
SchNet. We find that models using molecular descriptors achieve the best
performance, with GNN models also achieving good performance. We perform
extensive error analysis to understand the molecular properties that influence
model performance, perform feature analysis to understand which information
about molecular structure is most valuable for prediction, and perform a
transfer learning and data size study to understand the impact of data
availability on model performance.
