将质谱预测转化为分子图上的目标检测，提升准确率与速度。
GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
- 把分子碎片生成看作图上的目标检测任务，用单阶段Transformer模型统一建模。
- 在MassSpecGym上达70.0%准确率（前一SOTA为64.0%），NIST'20上达52.5%。
- 相比旧模型推理快近8倍，适合代谢组学与分子结构分析场景。
从分子结构预测串联质谱（MS/MS）是分析化学中的核心任务，直接关联临床代谢组学、系统生物学等领域。本文从目标检测视角重新审视该问题：分子碎裂可视为在分子图中检测子图（即碎片）及其光谱贡献。现有基于碎片的模型采用两阶段范式——先生成候选碎片再评分，类似计算机视觉中的两阶段R-CNN。为提升精度与推理速度，我们提出GLACIER，一种基于Transformer的单阶段分子图碎片检测神经网络。该统一框架无需枚举候选片段，实现可扩展且全局一致的分子碎裂建模。GLACIER显著优于现有最先进方法，在MassSpecGym数据集上不带对比微调时达到70.0%、带对比微调时达69.7%的Top-1检索准确率（前一SOTA为64.0%），在NIST'20数据集上分别为52.5%和38.5%（前一SOTA为33.2%）。此外，相比先前两阶段模型，推理速度提升近8倍。代码已开源。
原文摘要 · Abstract (English)
Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit the problem through the lens of object detection on molecular graphs. Molecular fragmentation, a central step in MS/MS prediction, can be approximated as detecting a set of subgraphs (i.e., fragments) and their associated spectral contributions. Existing fragment-based models follow a two-stage paradigm -- first generating candidate fragments and then scoring them -- analogous to two-stage R-CNNs in computer vision. Towards higher accuracy and faster inference, we introduce GLACIER, a single-stage transformer-based fragment detection neural network for molecular graphs. This unified formulation eliminates the need for candidate enumeration, enabling scalable and globally consistent modeling of molecular fragmentation. GLACIER is faster and more accurate than existing state-of-the-art by a significant margin, achieving 70.0% and 69.7% Top-1 retrieval accuracy with and without contrastive finetuning on the MassSpecGym dataset (from the previous SOTA of 64.0%) and 52.5% and 38.5% respectively on the NIST'20 dataset (from 33.2%). Furthermore, GLACIER provides nearly 8-fold inference speedup over our prior two-stage model. Code is available at https://github.com/coleygroup/ms-pred
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