Contrastive Domain Generalization for Cross-Instrument Molecular Identification in Mass Spectrometry

• URL: http://arxiv.org/abs/2602.00547v1
• Date: Sat, 31 Jan 2026 06:18:47 GMT
• Title: Contrastive Domain Generalization for Cross-Instrument Molecular Identification in Mass Spectrometry
• Authors: Seunghyun Yoo, Sanghong Kim, Namkyung Yoon, Hwangnam Kim,
• Abstract summary: We propose a cross-modal alignment framework that maps mass spectra into the chemically meaningful molecular structure embedding space of a pretrained chemical language model.<n>Our model achieves a Top-1 accuracy of 42.2% in fixed 256-way zero-shot retrieval and demonstrates strong generalization under a global retrieval setting.<n>These results suggest that explicitly integrating physical spectral resolution with molecular structure embedding is key to solving the generalization bottleneck in molecular identification from MS data.
• Score: 3.6398652091809987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying molecules from mass spectrometry (MS) data remains a fundamental challenge due to the semantic gap between physical spectral peaks and underlying chemical structures. Existing deep learning approaches often treat spectral matching as a closed-set recognition task, limiting their ability to generalize to unseen molecular scaffolds. To overcome this limitation, we propose a cross-modal alignment framework that directly maps mass spectra into the chemically meaningful molecular structure embedding space of a pretrained chemical language model. On a strict scaffold-disjoint benchmark, our model achieves a Top-1 accuracy of 42.2% in fixed 256-way zero-shot retrieval and demonstrates strong generalization under a global retrieval setting. Moreover, the learned embedding space demonstrates strong chemical coherence, reaching 95.4% accuracy in 5-way 5-shot molecular re-identification. These results suggest that explicitly integrating physical spectral resolution with molecular structure embedding is key to solving the generalization bottleneck in molecular identification from MS data.

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