Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
arXiv:2602.20344v1 Announce Type: new Abstract: Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling costs such as molecular graph analysis. However, existing GSSL methods mostly focus on node- or edge-level information, often ignoring chemically relevant substructures which strongly influence molecular properties. In this work, we propose Graph Semantic Predictive Network (GraSPNet), a hierarchical self-supervised framework that explicitly models both atomic-level and fragment-level semantics. GraSPNet decomposes molecular graphs into chemically meaningful fragments without predefined vocabularies and learns node- and fragment-level representations through multi-level message passing with masked semantic prediction at both levels. This hierarchical semantic supervision enables GraSPNet to learn multi-resolution structural
arXiv:2602.20344v1 Announce Type: new Abstract: Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling costs such as molecular graph analysis. However, existing GSSL methods mostly focus on node- or edge-level information, often ignoring chemically relevant substructures which strongly influence molecular properties. In this work, we propose Graph Semantic Predictive Network (GraSPNet), a hierarchical self-supervised framework that explicitly models both atomic-level and fragment-level semantics. GraSPNet decomposes molecular graphs into chemically meaningful fragments without predefined vocabularies and learns node- and fragment-level representations through multi-level message passing with masked semantic prediction at both levels. This hierarchical semantic supervision enables GraSPNet to learn multi-resolution structural information that is both expressive and transferable. Extensive experiments on multiple molecular property prediction benchmarks demonstrate that GraSPNet learns chemically meaningful representations and consistently outperforms state-of-the-art GSSL methods in transfer learning settings.
Executive Summary
This article proposes GraSPNet, a hierarchical self-supervised framework for learning molecular representations. By modeling both atomic-level and fragment-level semantics, GraSPNet outperforms state-of-the-art graph self-supervised learning methods in transfer learning settings. The framework decomposes molecular graphs into chemically meaningful fragments and learns node- and fragment-level representations through multi-level message passing. Extensive experiments demonstrate the effectiveness of GraSPNet in learning chemically meaningful representations and predicting molecular properties.
Key Points
- ▸ GraSPNet is a hierarchical self-supervised framework for molecular representation learning
- ▸ The framework models both atomic-level and fragment-level semantics
- ▸ GraSPNet outperforms state-of-the-art graph self-supervised learning methods in transfer learning settings
Merits
Expressive Representations
GraSPNet learns multi-resolution structural information that is both expressive and transferable
Improved Performance
GraSPNet consistently outperforms state-of-the-art GSSL methods in transfer learning settings
Demerits
Computational Complexity
The multi-level message passing and masked semantic prediction may increase computational complexity
Expert Commentary
The proposed GraSPNet framework represents a significant advancement in molecular representation learning. By explicitly modeling both atomic-level and fragment-level semantics, GraSPNet is able to learn more expressive and transferable representations. The framework's ability to outperform state-of-the-art GSSL methods in transfer learning settings demonstrates its potential for real-world applications. However, further research is needed to address the computational complexity of the framework and to explore its applications in various domains.
Recommendations
- ✓ Further research is needed to optimize the computational complexity of GraSPNet
- ✓ GraSPNet should be applied to a wider range of molecular property prediction tasks to demonstrate its generalizability
