Title: Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: molecule identification, mass spectrometry, loss functions, Bayes-optimal prediction

Abstract: One of the central challenges in the computational analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data is to identify the compounds underlying the output spectra.
In recent years, this problem is increasingly tackled using deep learning methods.
A common strategy involves predicting a molecular fingerprint vector from an input mass spectrum, which is then used to search for matches in a chemical compound database.
While various loss functions are employed in training these predictive models, their impact on model performance remains poorly understood. Facilitated by the recent establishment of standardized benchmarks, we investigate in this study commonly used loss functions through both an empirical and theoretical lens. Our results reveal a fundamental trade-off between the two objectives of (1) fingerprint similarity and (2) molecular retrieval. In conclusion, optimizing for more accurate fingerprint predictions typically translates into worse retrieval results, and vice versa.