Citation: Fanding Xu, Zhiwei Yang, Sirui Wu, Wu Su, Lizhuo Wang, Deyu Meng, Jiangang Long. MolUNet++: Adaptive-grained explicit substructure and interaction aware molecular representation learning[J]. Acta Physico-Chimica Sinica, ;2026, 42(5): 100209. doi: 10.1016/j.actphy.2025.100209 shu

MolUNet++: Adaptive-grained explicit substructure and interaction aware molecular representation learning

Fanding Xu ¹ ,
Zhiwei Yang ^2,, ,
Sirui Wu ³ ,
Wu Su ¹ ,
Lizhuo Wang ¹ ,
Deyu Meng ^{4, 5,,} ,
Jiangang Long ^1,,

1.
School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China
2.
School of Physics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China
3.
China Mobile Communications Group Shaanxi Co., Ltd, Xi'an 710077, Shaanxi Province, China
4.
Research Institute for Mathematics and Mathematical Technology, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China
5.
School of Mathematics and Statistics, Henan University, Zhengzhou 475004, Henan Province, China

Corresponding author: Zhiwei Yang, yzws-123@xjtu.edu.cn Deyu Meng, dymeng@mail.xjtu.edu.cn Jiangang Long, jglong@xjtu.edu.cn
Received Date: 22 August 2025
Revised Date: 21 October 2025
Accepted Date: 22 October 2025

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.
- Molecular representation learning,
- GNN,
- Structure identification,
- Adaptive granularity
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