Predicting natural language descriptions of mono-molecular odorants

E. Darío Gutiérrez, Amit Dhurandhar, Andreas Keller, Pablo Meyer () and Guillermo A. Cecchi ()
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E. Darío Gutiérrez: T.J. Watson IBM Research Laboratory
Amit Dhurandhar: T.J. Watson IBM Research Laboratory
Andreas Keller: AK Consulting
Pablo Meyer: T.J. Watson IBM Research Laboratory
Guillermo A. Cecchi: T.J. Watson IBM Research Laboratory

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract There has been recent progress in predicting whether common verbal descriptors such as “fishy”, “floral” or “fruity” apply to the smell of odorous molecules. However, accurate predictions have been achieved only for a small number of descriptors. Here, we show that applying natural-language semantic representations on a small set of general olfactory perceptual descriptors allows for the accurate inference of perceptual ratings for mono-molecular odorants over a large and potentially arbitrary set of descriptors. This is noteworthy given that the prevailing view is that humans’ capacity to identify or characterize odors by name is poor. We successfully apply our semantics-based approach to predict perceptual ratings with an accuracy higher than 0.5 for up to 70 olfactory perceptual descriptors, a ten-fold increase in the number of descriptors from previous attempts. These results imply that the semantic distance between descriptors defines the equivalent of an odorwheel.

Date: 2018
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DOI: 10.1038/s41467-018-07439-9

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