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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19561 |
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| _version_ | 1866912852366852096 |
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| author | Kang, Dayoung Kim, JongWon Park, Jiho Lee, Keonseock Choi, Ji-Woong So, Jinhyun |
| author_facet | Kang, Dayoung Kim, JongWon Park, Jiho Lee, Keonseock Choi, Ji-Woong So, Jinhyun |
| contents | Public olfaction datasets are small and fragmented across single molecules and mixtures, limiting learning of generalizable odor representations. Recent works either learn single-molecule embeddings or address mixtures via similarity or pairwise label prediction, leaving representations separate and unaligned. In this work, we propose AROMMA, a framework that learns a unified embedding space for single molecules and two-molecule mixtures. Each molecule is encoded by a chemical foundation model and the mixtures are composed by an attention-based aggregator, ensuring both permutation invariance and asymmetric molecular interactions. We further align odor descriptor sets using knowledge distillation and class-aware pseudo-labeling to enrich missing mixture annotations. AROMMA achieves state-of-the-art performance in both single-molecule and molecule-pair datasets, with up to 19.1% AUROC improvement, demonstrating a robust generalization in two domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19561 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AROMMA: Unifying Olfactory Embeddings for Single Molecules and Mixtures Kang, Dayoung Kim, JongWon Park, Jiho Lee, Keonseock Choi, Ji-Woong So, Jinhyun Machine Learning Artificial Intelligence Public olfaction datasets are small and fragmented across single molecules and mixtures, limiting learning of generalizable odor representations. Recent works either learn single-molecule embeddings or address mixtures via similarity or pairwise label prediction, leaving representations separate and unaligned. In this work, we propose AROMMA, a framework that learns a unified embedding space for single molecules and two-molecule mixtures. Each molecule is encoded by a chemical foundation model and the mixtures are composed by an attention-based aggregator, ensuring both permutation invariance and asymmetric molecular interactions. We further align odor descriptor sets using knowledge distillation and class-aware pseudo-labeling to enrich missing mixture annotations. AROMMA achieves state-of-the-art performance in both single-molecule and molecule-pair datasets, with up to 19.1% AUROC improvement, demonstrating a robust generalization in two domains. |
| title | AROMMA: Unifying Olfactory Embeddings for Single Molecules and Mixtures |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.19561 |