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 on six different molecular generative objectives on the ZINC-250k dataset in two different generative frameworks: autoregressive generation models, such as GCPN and GraphAF, and one-shot generation models, such as GraphEBM. 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.

翻译：图生成是一项重大挑战，因为它涉及根据给定的标签预测包含多个节点和边的完整图。这一任务对众多现实应用也至关重要，包括从零开始设计药物和分子。近年来，图生成领域涌现出几种成功的方法。然而，这些方法存在两个显著不足：（1）这些方法中使用的底层图神经网络（GNN）架构往往未得到充分探索；（2）这些方法通常仅在有限的指标上被评估。为弥补这一空白，我们通过将图生成模型的底层GNN替换为更具表达力的GNN，研究了GNN在分子图生成任务背景下的表达力。具体而言，我们在两种不同的生成框架中分析了六种GNN在ZINC-250k数据集上针对六种不同的分子生成目标的性能：自回归生成模型（如GCPN和GraphAF）以及一次性生成模型（如GraphEBM）。通过大量实验，我们证明了先进的GNN确实能提升GCPN、GraphAF和GraphEBM在分子生成任务中的性能，但GNN的表达力并非构建优秀GNN生成模型的必要条件。此外，我们展示了采用先进GNN的GCPN和GraphAF能够在所提出的分子生成目标（DRD2、Median1、Median2）上，超越其他17种非基于GNN的图生成方法（如变分自编码器和贝叶斯优化模型）达到最先进的结果，而这些目标是从零开始设计分子的重要指标。