BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning
Summary: arXiv:2604.06336v1 Announce Type: cross
Introduction
Graph Transformers have emerged as a promising approach for molecular property prediction by integrating the inductive biases of graph neural networks (GNNs) with the expansive receptive fields characteristic of Transformers. Despite their potential, many existing hybrid architectures predominantly rely on GNN frameworks, which can restrict the resulting representations to local message-passing influences. Furthermore, the majority of these methodologies function at a single structural granularity, thereby hindering their capacity to effectively capture molecular patterns that exist across various scales.
Overview of BiScale-GTR
To address these limitations, we introduce BiScale-GTR, a comprehensive framework designed for self-supervised molecular representation learning. This innovative method combines the principles of chemically grounded fragment tokenization with adaptive multi-scale reasoning, enhancing the model’s ability to interpret and predict molecular properties.
Key Features
- Enhanced Fragment Tokenization: BiScale-GTR improves upon traditional graph Byte Pair Encoding (BPE) tokenization methods to create consistent, chemically valid, and high-coverage fragment tokens.
- Parallel GNN-Transformer Architecture: The framework integrates fragment-level inputs into a hybrid architecture, allowing for comprehensive analysis and representation of molecular data.
- Multi-Scale Reasoning: By pooling atom-level representations into fragment-level embeddings, the model captures local chemical environments, substructure-level motifs, and long-range molecular dependencies effectively.
Experimental Results
BiScale-GTR was evaluated across multiple benchmark datasets, including MoleculeNet, PharmaBench, and the Long Range Graph Benchmark (LRGB). The results demonstrated state-of-the-art performance in both classification and regression tasks, illustrating the model’s robustness and adaptability in various predictive scenarios.
Interpretability and Attribution Analysis
One of the significant advantages of BiScale-GTR is its ability to provide interpretable insights into the relationships between molecular structures and their predicted properties. Attribution analysis reveals that the model effectively highlights chemically meaningful functional motifs, thereby enhancing understanding and transparency in molecular representation learning.
Conclusion and Future Work
In summary, BiScale-GTR represents a significant advancement in the field of molecular representation learning. By marrying the strengths of GNNs and Transformers while addressing the challenges of single-scale representation, this framework sets a new standard for performance and interpretability in molecular property prediction. Code for BiScale-GTR will be released following acceptance, offering researchers the opportunity to further explore and build upon this innovative approach.