  Molecular representation learning is crucial for the problem of molecular
property prediction, where graph neural networks (GNNs) serve as an effective
solution due to their structure modeling capabilities. Since labeled data is
often scarce and expensive to obtain, it is a great challenge for GNNs to
generalize in the extensive molecular space. Recently, the training paradigm of
"pre-train, fine-tune" has been leveraged to improve the generalization
capabilities of GNNs. It uses self-supervised information to pre-train the GNN,
and then performs fine-tuning to optimize the downstream task with just a few
labels. However, pre-training does not always yield statistically significant
improvement, especially for self-supervised learning with random structural
masking. In fact, the molecular structure is characterized by motif subgraphs,
which are frequently occurring and influence molecular properties. To leverage
the task-related motifs, we propose a novel paradigm of "pre-train, prompt,
fine-tune" for molecular representation learning, named molecule continuous
prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the
pre-trained model to project the standalone input into an expressive prompt.
The prompt effectively augments the molecular graph with meaningful motifs in
the continuous representation space; this provides more structural patterns to
aid the downstream classifier in identifying molecular properties. Extensive
experiments on several benchmark datasets show that MolCPT efficiently
generalizes pre-trained GNNs for molecular property prediction, with or without
a few fine-tuning steps.
