  Graph generation poses a significant challenge as it involves predicting a
complete graph with multiple nodes and edges based on simply a given label.
This task also carries fundamental importance to numerous real-world
applications, including de-novo drug and molecular design. In recent years,
several successful methods have emerged in the field of graph generation.
However, these approaches suffer from two significant shortcomings: (1) the
underlying Graph Neural Network (GNN) architectures used in these methods are
often underexplored; and (2) these methods are often evaluated on only a
limited number of metrics. To fill this gap, we investigate the expressiveness
of GNNs under the context of the molecular graph generation task, by replacing
the underlying GNNs of graph generative models with more expressive GNNs.
Specifically, we analyse the performance of six GNNs in two different
generative frameworks -- autoregressive generation models, such as GCPN and
GraphAF, and one-shot generation models, such as GraphEBM -- on six different
molecular generative objectives on the ZINC-250k dataset. Through our extensive
experiments, we demonstrate that advanced GNNs can indeed improve the
performance of GCPN, GraphAF, and GraphEBM on molecular generation tasks, but
GNN expressiveness is not a necessary condition for a good GNN-based generative
model. Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve
state-of-the-art results across 17 other non-GNN-based graph generative
approaches, such as variational autoencoders and Bayesian optimisation models,
on the proposed molecular generative objectives (DRD2, Median1, Median2), which
are important metrics for de-novo molecular design.
