MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers
This work addresses a long-standing problem in mass spectrometry for chemists and biologists, offering an incremental improvement over prior deep learning approaches by incorporating global molecular structure.
The paper tackles the challenge of predicting tandem mass spectra for small molecules, which lack experimental reference spectra, by introducing MassFormer, a graph transformer model that outperforms existing methods on multiple datasets and demonstrates utility in spectrum identification tasks.
Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule. Although mass spectrometry is applied in many areas, the vast majority of small molecules lack experimental reference spectra. For over seventy years, spectrum prediction has remained a key challenge in the field. Existing deep learning methods do not leverage global structure in the molecule, potentially resulting in difficulties when generalizing to new data. In this work we propose a new model, MassFormer, for accurately predicting tandem mass spectra. MassFormer uses a graph transformer architecture to model long-distance relationships between atoms in the molecule. The transformer module is initialized with parameters obtained through a chemical pre-training task, then fine-tuned on spectral data. MassFormer outperforms competing approaches for spectrum prediction on multiple datasets, and is able to recover prior knowledge about the effect of collision energy on the spectrum. By employing gradient-based attribution methods, we demonstrate that the model can identify relationships between fragment peaks. To further highlight MassFormer's utility, we show that it can match or exceed existing prediction-based methods on two spectrum identification tasks. We provide open-source implementations of our model and baseline approaches, with the goal of encouraging future research in this area.