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arXiv · 2601.19561

AROMMA: Unifying Olfactory Embeddings for Single Molecules and Mixtures

Dayoung Kang·JongWon Kim·Jiho Park·Keonseock Lee·Ji-Woong Choi·Jinhyun So
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Abstract

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.

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Dayoung Kang, JongWon Kim, Jiho Park, Keonseock Lee, Ji-Woong Choi, Jinhyun So. 2026-01-27. AROMMA: Unifying Olfactory Embeddings for Single Molecules and Mixtures. https://arxiv.org/abs/2601.19561

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