  Predicting molecular properties with data-driven methods has drawn much
attention in recent years. Particularly, Graph Neural Networks (GNNs) have
demonstrated remarkable success in various molecular generation and prediction
tasks. In cases where labeled data is scarce, GNNs can be pre-trained on
unlabeled molecular data to first learn the general semantic and structural
information before being fine-tuned for specific tasks. However, most existing
self-supervised pre-training frameworks for GNNs only focus on node-level or
graph-level tasks. These approaches cannot capture the rich information in
subgraphs or graph motifs. For example, functional groups (frequently-occurred
subgraphs in molecular graphs) often carry indicative information about the
molecular properties. To bridge this gap, we propose Motif-based Graph
Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif
generation framework for GNNs. First, for motif extraction from molecular
graphs, we design a molecule fragmentation method that leverages a
retrosynthesis-based algorithm BRICS and additional rules for controlling the
size of motif vocabulary. Second, we design a general motif-based generative
pre-training framework in which GNNs are asked to make topological and label
predictions. This generative framework can be implemented in two different
ways, i.e., breadth-first or depth-first. Finally, to take the multi-scale
information in molecular graphs into consideration, we introduce a multi-level
self-supervised pre-training. Extensive experiments on various downstream
benchmark tasks show that our methods outperform all state-of-the-art
baselines.
