MolUNet++:自适应粒度显式子结构与互作感知分子表示学习OA

Molecular representation learning is a critical task in AI-driven drug development.While graph neural networks(GNNs)have demonstrated strong performance and gained widespread adoption in this field,efficiently extracting and explicitly analyzing functional groups remains a challenge.To address this issue,we propose MolUNet++,a novel model that employs Molecular Edge Shrinkage Pooling(MESPool)for hierarchical substructure extraction,utilizes a Nested UNet framework for multi-granularity feature integration,and incorporates a substructure masking explainer for quantitative fragment analysis.We evaluated MolUNet++on tasks including molecular property prediction,drug-drug interaction(DDI)prediction,and drug-target interaction(DTI)prediction.Experimental results demonstrate that MolUNet++not only outperforms traditional GNN models in predictive performance but also exhibits explicit,intuitive,and chemically logical interpretability.This capability provides valuable insights and tools for researchers in drug design and optimization.