We show the effectiveness of Graphormer, a graph transformer, for the classification of biological reaction graphs associated with different species. We compare the results with graph kernels, a more traditional network analyzer.
Reaction graphs derived from metabolic processes compiled by KEGG provide a compact and structured representation of the biochemical activity of an organism. These graphs often exhibit species-specific patterns, making them a valuable resource for
comparative analysis and classification tasks in bioinformatics.
Traditional graph-based machine learning approaches, such as graph kernels or graph neural networks, have been used to analyse these structures, but they may struggle to capture long-range dependencies and complex global patterns.
Recently, transformer-based architectures have emerged as powerful models capable of learning rich representations from structured data. Graph Transformers extend the self-attention mechanism to graph domains, enabling the integration of both local and global structural information.
In this context, graph transformers offer a promising approach for encoding reaction graphs into meaningful vector representations that can be used for downstream tasks such as classification. In this study, we use Graphormer as a fixed encoder and combined it with a species classification neural network head. The reaction graphs are subsampled when they exceed the usual input size for Graphormer.
This work seeks to provide insights into the suitability of graph transformers for bioinformatics applications involving structured biological data. The results demonstrate that these models can outperform or complement more traditional graph-based approaches, thereby contributing to the broader use of advanced AI techniques in biological network analysis.

Co-authors: Aniello Di Vaio, Mercè Llabrés