Molecular Property Prediction Based on Multi-Level Graph Self-Supervised Pretraining and Multi-Grain Finetuning.

Molecular property prediction is crucial for advancing medical research in areas like retrosynthesis analysis and drug discovery. The challenge of obtaining accurate molecular property labels has led to the use of pretrained Graph Neural Networks (GNNs) with self-supervised learning methods. However, traditional approaches often fail to capture detailed chemical structural and functional information, particularly within molecular functional groups, and do not adequately address relationships across molecular graph layers.