Property prediction on molecular graphs is an important application of Graph
Neural Networks (GNNs). Recently, unlabeled molecular data has become abundant,
which facilitates the rapid development of self-supervised learning for GNNs in
the chemical domain. In this work, we propose pretraining GNNs at the fragment
level, which serves as a promising middle ground to overcome the limitations of
node-level and graph-level pretraining. Borrowing techniques from recent work
on principle subgraph mining, we obtain a compact vocabulary of prevalent
fragments that span a large pretraining dataset. From the extracted vocabulary,
we introduce several fragment-based contrastive and predictive pretraining
tasks. The contrastive learning task jointly pretrains two different GNNs: one
based on molecular graphs and one based on fragment graphs, which represents
high-order connectivity within molecules. By enforcing the consistency between
the fragment embedding and the aggregated embedding of the corresponding atoms
from the molecular graphs, we ensure that both embeddings capture structural
information at multiple resolutions. The structural information of the fragment
graphs is further exploited to extract auxiliary labels for the graph-level
predictive pretraining. We employ both the pretrained molecular-based and
fragment-based GNNs for downstream prediction, thus utilizing the fragment
information during finetuning. Our models advance the performances on 5 out of
8 common molecular benchmarks and improve the performances on long-range
biological benchmarks by at least 11.5%.

利用基于片段的对比学习方法，在分子图预测性质方面取得了良好表现并提高了长程生物标准的预测效果。