PT  - JOURNAL ARTICLE
AU  - Brian K. Lee
AU  - Emily J. Mayhew
AU  - Benjamin Sanchez-Lengeling
AU  - Jennifer N. Wei
AU  - Wesley W. Qian
AU  - Kelsie Little
AU  - Matthew Andres
AU  - Britney B. Nguyen
AU  - Theresa Moloy
AU  - Jane K. Parker
AU  - Richard C. Gerkin
AU  - Joel D. Mainland
AU  - Alexander B. Wiltschko
TI  - A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception
AID  - 10.1101/2022.09.01.504602
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.09.01.504602
4099  - http://biorxiv.org/content/early/2022/09/06/2022.09.01.504602.short
4100  - http://biorxiv.org/content/early/2022/09/06/2022.09.01.504602.full
AB  - Mapping molecular structure to odor perception is a key challenge in olfaction. Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than did the median panelist. 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.One-Sentence Summary An odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.Competing Interest StatementThe authors have declared no competing interest.