Tandem mass spectra capture fragmentation patterns that provide key structural information about molecules. Although mass spectrometry is applied in many areas, the vast majority of small molecules lack experimental reference spectra. For over 70 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 the MassFormer model 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 pretraining task, then fine-tuned on spectral data. MassFormer outperforms competing approaches for spectrum prediction on multiple datasets and accurately models the effects of collision energy. Gradient-based attribution methods reveal that MassFormer can identify compositional relationships between peaks in the spectrum. When applied to spectrum identification problems, MassFormer generally surpasses the performance of existing prediction-based methods.

串联质谱捕获提供分子关键结构信息的碎片模式。尽管质谱法在许多领域都有应用，但绝大多数小分子缺乏实验参考光谱。 70 多年来，频谱预测一直是该领域的一个关键挑战。现有的深度学习方法没有利用分子中的全局结构，这可能会导致推广到新数据时遇到困难。在这项工作中，我们提出了 MassFormer 模型来准确预测串联质谱。 MassFormer 使用图形转换器架构来模拟分子中原子之间的长距离关系。变压器模块使用通过化学预训练任务获得的参数进行初始化，然后根据光谱数据进行微调。 MassFormer 在多个数据集上的光谱预测优于竞争方法，并能准确地模拟碰撞能量的影响。基于梯度的归因方法表明 MassFormer 可以识别光谱中峰之间的成分关系。当应用于频谱识别问题时，MassFormer 的性能普遍超过了现有基于预测的方法。