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arXiv · 2610.37979

Separability Is Not Alignability: Representations for Spectrum-to-Molecule Retrieval

Karol Musielinski·Fanurs C. E. Teh·Takahiko Koyama·Artur Dubrawski
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Abstract

Identifying a small molecule from its tandem mass spectrum is limited by paired data. Foundation models offer a way around this scarcity: pretrain a spectrum encoder on unlabeled spectra and a molecule encoder on a large corpus of structures, then align the two frozen representations, as in vision-language and as the Platonic Representation Hypothesis suggests. We audit this route on MassSpecGym with spectrum encoders retrained free of test-fold contamination. Alignment needs a metric relating distinct molecules, but the objectives that produce spectrum encoders never see molecular structure: we prove that some predictive objectives fix molecular identity without fixing that metric. Moreover, we find that a candidate-based objective rewards a spectrum-independent prior over the benchmark's decoys, which we isolate. What ultimately addresses data scarcity is not architectural: simulated spectra surpass frozen geometry.

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Karol Musielinski, Fanurs C. E. Teh, Takahiko Koyama, Artur Dubrawski. 2026-10-27. Separability Is Not Alignability: Representations for Spectrum-to-Molecule Retrieval. https://arxiv.org/abs/2610.37979

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