Recent years have seen a rapid growth of utilizing graph neural networks
(GNNs) in the biomedical domain for tackling drug-related problems. However,
like any other deep architectures, GNNs are data hungry. While requiring labels
in real world is often expensive, pretraining GNNs in an unsupervised manner
has been actively explored. Among them, graph contrastive learning, by
maximizing the mutual information between paired graph augmentations, has been
shown to be effective on various downstream tasks. However, the current graph
contrastive learning framework has two limitations. First, the augmentations
are designed for general graphs and thus may not be suitable or powerful enough
for certain domains. Second, the contrastive scheme only learns representations
that are invariant to local perturbations and thus does not consider the global
structure of the dataset, which may also be useful for downstream tasks.
Therefore, in this paper, we study graph contrastive learning in the context of
biomedical domain, where molecular graphs are present. We propose a novel
framework called MoCL, which utilizes domain knowledge at both local- and
global-level to assist representation learning. The local-level domain
knowledge guides the augmentation process such that variation is introduced
without changing graph semantics. The global-level knowledge encodes the
similarity information between graphs in the entire dataset and helps to learn
representations with richer semantics. The entire model is learned through a
double contrast objective. We evaluate MoCL on various molecular datasets under
both linear and semi-supervised settings and results show that MoCL achieves
state-of-the-art performance.

本文研究了图对比学习在生物医药领域中的应用，提出了一种利用本地和全局领域知识来辅助表示学习的框架 MoCL。使用双重对比目标学习整个模型。在不同的分子数据集上进行了评估，结果表明 MoCL 达到了最先进的性能水平。