A unified framework for molecular property prediction based on hierarchical multi-granularity molecular...

Molecular property prediction is a fundamental task in drug discovery and plays a key role in accelerating the identification of promising candidates. Existing methods have primarily focused on atom-bond information for molecular representation learning. However, atoms and bonds are often not explicitly organized as coupled learnable entities, and prior knowledge such as functional motifs and global molecular attributes remains largely underutilized. In addition, they lack effective mechanisms to enable interaction and fusion across different structural granularities. Consequently, the resulting representations often capture only partial chemical semantics.

To address these issues, we propose HMG-MRL, a unified framework for hierarchical multi-granularity molecular representation learning. This framework systematically integrates domain knowledge across three granularities: fine-grained atom-bond information, medium-grained functional motifs, and coarse-grained molecular attributes. Specifically, we introduce an atom-bond bipartite graph modeling approach that treats atoms and bonds as explicit learnable node types and jointly models atom-atom, atom-bond, and bond-bond local interactions within a unified propagation framework. In parallel, we integrate multiple substructure decomposition strategies to construct a diverse motif vocabulary and design a Motif Transformer that employs self-attention to capture global interactions among motifs. Moreover, we design a cross-granularity communication module to facilitate information exchange across granularities. Extensive experiments on nine public benchmark datasets show that HMG-MRL achieves competitive predictive performance. Case studies further indicate that the model can reveal key molecular components across different granularities, with its diverse substructure decomposition strategies offering complementary structural patterns.

The data and code of HMG-MRL are available at https://github.com/AKZstar/HMG-MRL.