SEA-Driven OdorNet: A Large-Scale Standardized Molecular Olfactory Dataset Based on a Hybrid Semantic Taxonomy

Summary

The development of machine olfaction (digitization of odor) is currently hindered by data scarcity and the lack of standardization in descriptive labels. Datasets integration faces the challenges of descriptor-level conflicts, including cross-source labeling inconsistencies, synonym redundancy, skewed distributions, etc. To address the semantic confusion inherent in multi-source data, we developed a SEA (Statistical co-occurrence, Expert correction, and AI alignment) taxonomy framework that integrates statistical co-occurrence analysis, expert knowledge (perfumery taxonomy), and AI-assisted semantic alignment based on the definitions from both encyclopedia and odor-related scientific literatures. A unified-labeled olfactory dataset, namely OdorNet, is further presented, which is by far the largest molecule dataset with unified olfactory labels comprising over 20,000 entries and 9,000 unique molecules originated from academic literatures (1960–2021) and open databases. This process consolidates cluttered labels into a hierarchical system with the most extensively effective vocabulary coverage in the field to date. Technical validation demonstrates that a baseline model on OdorNet achieves a Macro F1 score of 0.42, substantially higher than the scores obtained on commonly used odor datasets and on OdorNet itself using the same model but without the SEA-based label taxonomy. This indicates that OdorNet allows to better capture learnable structure-odor relationships, laying a solid fundamental for digital odor editing and human-computer interaction.