From Spectra to Local Networks: Evaluating MS² Similarity Metrics

MS² spectral similarity is fundamental to interpreting LC-HRMS-NTA data. Beyond the commonly used cosine similarity, a wide range of alternative metrics, including distance-, probability-, and machine-learning-based approaches, provide different perspectives on spectral matching. In this study, we extracted 8290 sub-data sets from publicly and commercially available LC-HRMS/MS libraries (MassBank, MoNa, GNPS, and NIST), each containing spectra sharing a precursor m/z within a 5 mDa tolerance. We evaluated 20 similarity metrics by constructing single-generation local molecular networks. Most metrics failed to produce pure networks, achieving complete resolution in only ≈8% of cases at the recommended 0.7 threshold and ≈20% even when individually optimized. The number of fragment ions showed little influence on network resolution. Instead, performance was driven primarily by the similarity metric and thresholding behavior, indicating that library matching alone rarely supports identification confidence above level 3. These findings highlight the need for cumulative strategies that integrate multiple similarity perspectives and orthogonal information, such as retention time or index.