Hierarchies of smell: structuring the molecular odor space using semantic taxonomies and machine learning

One of the key challenges to predict odor from the molecular structure is unarguably our limited understanding of the odor space and the complexity of the underlying structure-odor relationships. Here, we introduce an expert-curated taxonomy (ET) that captures the hierarchical relations between odor descriptors for molecular datasets. To quantify the usefulness and relevance of this expert taxonomy, we provide a systematic validation that leverages the predictive performance of machine learning models for structure-based odor predictions, as well as known structure-odor relationships. As a control next to the ET based on semantic and perceptual similarities, we provide a data-driven taxonomy (DT) based on clustering co-occurrence patterns of odor descriptors from our expert-curated molecular dataset. The latter is derived from available datasets in the Pyrfume repository. Both taxonomies (ET and DT) add value in the semantic organization of odor descriptors and provide an avenue for novel insights in molecular structure-odor prediction. Together with in-depth validation steps that highlight the value of odor taxonomies, the quality of the ET is quantitatively assessed. The DT further allows critical evaluation of the expert taxonomy, identification of potential inconsistencies, and a better understanding of the molecular odor space. Finally, we highlight the results of the expert-curated taxonomy by showcasing odor predictions for the case of pear odorants used in perfumery. Both taxonomies as well as a full molecular dataset are made available to the community, providing a stepping stone for a future community-driven exploration of the molecular basis of smell.