Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

中文翻译：

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TransExION：基于变压器的可解释相似性度量，用于比较串联质谱中的 ION


小分子鉴定是分析化学和生命科学中的一项重要任务。阐明小分子结构最常用的技术之一是质谱法。产物离子谱 (MS/MS) 的谱库搜索是识别或寻找结构类似物的流行策略。该方法依赖于光谱相似性和结构相似性相关的假设。然而，流行的光谱相似性测量通常基于 MS/MS 光谱之间的相同片段匹配来计算，并不总是准确反映结构相似性。在本研究中，我们提出了 TransExION，一种基于 Transformer 的 IONS 可解释相似性度量。 TransExION 通过质量差异检测 MS/MS 谱图之间的相关片段，并使用这些片段来估计谱图相似性。这些相关片段可以几乎相同，但也可以共享子结构。 TransExION 还提供了对其估计的事后解释，可用于支持科学家评估光谱库搜索结果，从而支持未知分子的结构阐明。我们的模型具有基于 Transformer 的架构，并根据来自 GNPS MS/MS 库的数据进行训练。实验结果表明，它在搜索和解释结构类似物以及分子网络方面改进了现有的光谱相似性测量。我们提出了一种基于变压器的光谱相似性度量，可以改进小分子串联质谱的比较。我们提供了事后解释，可以作为基于数据库光谱的未知光谱注释的良好起点。