  Recently, the study of graph neural network (GNN) has attracted much
attention and achieved promising performance in molecular property prediction.
Most GNNs for molecular property prediction are proposed based on the idea of
learning the representations for the nodes by aggregating the information of
their neighbor nodes (e.g. atoms). Then, the representations can be passed to
subsequent layers to deal with individual downstream tasks. Therefore, the
architectures of GNNs can be considered as being composed of two core parts:
graph-related layers and task-specific layers. Facing real-world molecular
problems, the hyperparameter optimization for those layers are vital.
Hyperparameter optimization (HPO) becomes expensive in this situation because
evaluating candidate solutions requires massive computational resources to
train and validate models. Furthermore, a larger search space often makes the
HPO problems more challenging. In this research, we focus on the impact of
selecting two types of GNN hyperparameters, those belonging to graph-related
layers and those of task-specific layers, on the performance of GNN for
molecular property prediction. In our experiments. we employed a
state-of-the-art evolutionary algorithm (i.e., CMA-ES) for HPO. The results
reveal that optimizing the two types of hyperparameters separately can gain the
improvements on GNNs' performance, but optimising both types of hyperparameters
simultaneously will lead to predominant improvements. Meanwhile, our study also
further confirms the importance of HPO for GNNs in molecular property
prediction problems.
