SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910260656078848 |
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| author | Zhang, Ziqi Jin, Eunyeong Vasco, Miguel Taleb, Farzaneh Rajabi, Nona Gutmann, Alexandra Williams, Jonathan Ribeiro, Antônio H. Kragic, Danica |
| author_facet | Zhang, Ziqi Jin, Eunyeong Vasco, Miguel Taleb, Farzaneh Rajabi, Nona Gutmann, Alexandra Williams, Jonathan Ribeiro, Antônio H. Kragic, Danica |
| contents | Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at inference, which is not available in practical sensing settings. We address this gap by exploring direct electron ionization mass spectrometry (EI-MS), a sensing technique that acquires chemically informative fragmentation fingerprints in seconds, as an alternative input modality for olfactory prediction. We contribute Spectrum-to-Chemical Embedding alignmeNT (SCENT), a multi-modal contrastive learning framework that aligns EI-MS representations with pretrained chemical structure embeddings, while requiring only mass spectra at inference. On the multi-label odor descriptor prediction task, SCENT significantly outperforms MS-only baselines and achieves performance comparable to structure-based models, despite requiring no explicit molecular structure at test time. The learned representations also better approximate continuous human perceptual ratings and generalize to real-world lab-measured spectra, suggesting that cross-modal alignment is an effective strategy for grounding analytical spectra in chemical semantics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27009 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception Zhang, Ziqi Jin, Eunyeong Vasco, Miguel Taleb, Farzaneh Rajabi, Nona Gutmann, Alexandra Williams, Jonathan Ribeiro, Antônio H. Kragic, Danica Machine Learning Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at inference, which is not available in practical sensing settings. We address this gap by exploring direct electron ionization mass spectrometry (EI-MS), a sensing technique that acquires chemically informative fragmentation fingerprints in seconds, as an alternative input modality for olfactory prediction. We contribute Spectrum-to-Chemical Embedding alignmeNT (SCENT), a multi-modal contrastive learning framework that aligns EI-MS representations with pretrained chemical structure embeddings, while requiring only mass spectra at inference. On the multi-label odor descriptor prediction task, SCENT significantly outperforms MS-only baselines and achieves performance comparable to structure-based models, despite requiring no explicit molecular structure at test time. The learned representations also better approximate continuous human perceptual ratings and generalize to real-world lab-measured spectra, suggesting that cross-modal alignment is an effective strategy for grounding analytical spectra in chemical semantics. |
| title | SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.27009 |