Predicting Human Olfactory Perception from Activities of Odorant Receptors

Odor perception in humans is initiated by activation of odorant receptors (ORs) in the nose. However, the ORs linked to specific olfactory percepts are unknown, unlike in vision or taste where receptors are linked to perception of different colors and tastes. The large family of ORs (~400) and multiple receptors activated by an odorant present serious challenges. Here, we first use machine learning to screen ~0.5 million compounds for new ligands and identify enriched structural motifs for ligands of 34 human ORs. We next demonstrate that the activity of ORs successfully predicts many of the 146 different perceptual qualities of chemicals. Although chemical features have been used to model odor percepts, we show that biologically relevant OR activity is often superior. Interestingly, each odor percept could be predicted with very few ORs, implying they contribute more to each olfactory percept. A similar model is observed in Drosophila where comprehensive OR-neuron data are available. [Display omitted] •Machine learning predicted activity of 34 human ORs for ~0.5 million chemicals•Activities of human ORs could predict odor character using machine learning•Few OR activities were needed to optimize predictions of each odor percept•Behavior predictions in Drosophila also need few olfactory receptor activities Sensory Neuroscience; Bioinformatics; Automation in Bioinformatics; Computational Bioinformatics