用几何对齐法提升质谱分子识别准确率
SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment
- 将质谱与分子表征通过隐式对齐映射到同一空间
- 在三大基准上相对强基线提升20%-25%准确率
- 仅微调少量参数，适合资源受限场景
非靶向小分子质谱（MS/MS）鉴定仍面临谱图库不全的瓶颈。现有深度学习方法多为两种极端：显式生成模型逐原子构建分子图，或从零学习跨模态子空间的对比模型。本文提出SpecBridge，一种新型隐式对齐框架，将结构识别视为几何对齐问题。该方法微调自监督谱图编码器DreaMS，直接投影至冻结的分子基础模型ChemBERTa的潜在空间，并通过余弦相似度检索预计算的分子嵌入库。在MassSpecGym、Spectraverse和MSnLib三大基准上，SpecBridge相较强基线实现约20%-25%的顶1检索准确率提升，同时保持可训练参数极少。结果表明，对齐冻结基础模型是一种稳定且实用的替代方案。代码已开源：https://github.com/HassounLab/SpecBridge。
原文摘要 · Abstract (English)
Small-molecule identification from tandem mass spectrometry (MS/MS) remains a bottleneck in untargeted settings where spectral libraries are incomplete. While deep learning offers a solution, current approaches typically fall into two extremes: explicit generative models that construct molecular graphs atom-by-atom, or joint contrastive models that learn cross-modal subspaces from scratch. We introduce SpecBridge, a novel implicit alignment framework that treats structure identification as a geometric alignment problem. SpecBridge fine-tunes a self-supervised spectral encoder (DreaMS) to project directly into the latent space of a frozen molecular foundation model (ChemBERTa), and then performs retrieval by cosine similarity to a fixed bank of precomputed molecular embeddings. Across MassSpecGym, Spectraverse, and MSnLib benchmarks, SpecBridge improves top-1 retrieval accuracy by roughly 20-25% relative to strong neural baselines, while keeping the number of trainable parameters small. These results suggest that aligning to frozen foundation models is a practical, stable alternative to designing new architectures from scratch. The code for SpecBridge is released at https://github.com/HassounLab/SpecBridge.
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