  Deep learning based methods have been widely applied to predict various kinds
of molecular properties in the pharmaceutical industry with increasingly more
success. Solvation free energy is an important index in the field of organic
synthesis, medicinal chemistry, drug delivery, and biological processes.
However, accurate solvation free energy determination is a time-consuming
experimental process. Furthermore, it could be useful to assess solvation free
energy in the absence of a physical sample. In this study, we propose two novel
models for the problem of free solvation energy predictions, based on the Graph
Neural Network (GNN) architectures: Message Passing Neural Network (MPNN) and
Graph Attention Network (GAT). GNNs are capable of summarizing the predictive
information of a molecule as low-dimensional features directly from its graph
structure without relying on an extensive amount of intra-molecular
descriptors. As a result, these models are capable of making accurate
predictions of the molecular properties without the time consuming process of
running an experiment on each molecule. We show that our proposed models
outperform all quantum mechanical and molecular dynamics methods in addition to
existing alternative machine learning based approaches in the task of solvation
free energy prediction. We believe such promising predictive models will be
applicable to enhancing the efficiency of the screening of drug molecules and
be a useful tool to promote the development of molecular pharmaceutics.
