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原文摘要

Identifying molecular structures from LC-MS/MS spectra is a central problem in computational metabolomics, often approached by predicting molecular fingerprints and retrieving candidates via similarity search. Despite widespread use, the relationship between training objectives for fingerprint prediction and downstream retrieval performance remains poorly understood. In this work, we investigate the alignment between these objectives and show that they can be misaligned under uncertainty. Adopting a decision-theoretic perspective, we derive novel regret bounds that characterize when Bayes-optimal predictors for fingerprint similarity diverge from those for molecular retrieval. Our analysis reveals that optimizing standard similarity-based losses can degrade retrieval performance, and that the extent of this mismatch depends on the similarity structure of candidate sets. Empirically, we validate our theory on the MassSpecGym and Enveda-180 datasets, observing a Pareto frontier between fingerprint accuracy and retrieval metrics across commonly used loss functions. These findings highlight potential trade-offs in fingerprint-based molecular identification and provide principled guidance for the design of learning objectives in small molecule mass spectrometry.