Predicting Odor Perceptual Similarity from Odor Structure

Kobi Snitz, Adi Yablonka, Tali Weiss, Idan Frumin, Rehan M. Khan, Noam Sobel

Research output: Contribution to journalArticlepeer-review


To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r >= 0.49, p
Original languageEnglish
Number of pages12
JournalPLoS Computational Biology
Issue number9
StatePublished - Sep 2013


Dive into the research topics of 'Predicting Odor Perceptual Similarity from Odor Structure'. Together they form a unique fingerprint.

Cite this