A principal odor map unifies diverse tasks in olfactory perception

Abstract

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors. Description Editor’s summary For vision and hearing, there are well-developed maps that relate physical properties such as frequency and wavelength to perceptual properties such as pitch and color. The sense of olfaction does not yet have such a map. Using a graph neural network, Lee et al. developed a principal odor map (POM) that faithfully represents known perceptual hierarchies and distances. This map outperforms previously published models to the point that replacing a trained human’s responses with the model output would improve overall panel description. The POM coordinates were able to predict odor intensity and perceptual similarity, even though these perceptual features were not explicitly part of the model training. These results were used to build a variety of olfactory predictions that outperformed previous feature sets even without fine-tuning. —Peter Stern An odor map achieved human-level odor description performance and generalized to diverse odor-prediction tasks.