SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception

Predicting human olfactory perception from molecular structure has seen remarkable progress in recent years. However, existing approaches typically require access to the explicit chemical structure of an unknown substance at inference time, which is not directly observed. We address this gap by exploring electron ionization mass spectrometry (EI-MS) as a direct input modality for olfactory prediction, leveraging chemically informative fragmentation patterns acquired within seconds. We introduce 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, focusing here on single-compound odorants. On the multi-label odor descriptor prediction task, SCENT outperforms MS-only baselines and approaches the performance of structure-based models without access to explicit molecular structure at test time. On a prospective perceptual panel of structurally or perceptually distinct odorants, SCENT maintains its advantage over MS-only baselines. Its learned representations also provide more informative features for predicting continuous human perceptual ratings and generalize to independently acquired laboratory spectra. Together, these results suggest that cross-modal alignment can effectively transfer structure-derived chemical information into EI-MS representations for olfactory prediction.