MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning

Understand

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.