RT Journal Article
SR Electronic
T1 A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.09.01.504602
DO 10.1101/2022.09.01.504602
A1 Lee, Brian K.
A1 Mayhew, Emily J.
A1 Sanchez-Lengeling, Benjamin
A1 Wei, Jennifer N.
A1 Qian, Wesley W.
A1 Little, Kelsie
A1 Andres, Matthew
A1 Nguyen, Britney B.
A1 Moloy, Theresa
A1 Parker, Jane K.
A1 Gerkin, Richard C.
A1 Mainland, Joel D.
A1 Wiltschko, Alexander B.
YR 2022
UL http://biorxiv.org/content/early/2022/09/06/2022.09.01.504602.abstract
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.