Molecular Property Prediction Based on Motif-Centric Multi-Grain Pretaining and Finetuning Strategy

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 multi-level pretrained Graph Neural Networks (GNNs) with self-supervised learning methods. However, these multi-level approaches do not adequately address relationships across molecular graph levels particularly at the motif and atom levels, and neglect considering the fusion method of different grains. To overcome these limitations, we introduce the Motif-centric Multi-grain Graph Pretaining and Finetuning Strategy Framework (MMGSF). This framework consists of two components: Motif-centric Molecular Graph Pretraining Strategy(MMGS) which focuses on motif-centric contrastive learning on multi-level graph without disturbing molecular structure, and Multi-grain Finetuning (MGF) that refines node representations across grains, using a novel mol-adapter module with cross-attention for adaptive feature fusion. Our MGF captures complex feature interactions, ensuring structural and semantic information from different grains contributes effectively to molecular property predictions. Superior results in molecular property classification tasks demonstrate the effectiveness of MMGSF, and its visualization performance shows that the learned representations capture molecular multi-grain information and properties successfully. This study offers fresh insights into the design of more effective self-supervised learning frameworks for molecular property prediction.