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Nat Commun

Title:		Predicting natural language descriptions of mono-molecular odorants
Author(s):		Gutierrez ED; Dhurandhar A; Keller A; Meyer P; Cecchi GA;
Address:		"Computational Biology Center, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA. Artificial Intelligence Foundations, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA. AK Consulting, 508 East 78th Street, Apt 5N, New York, NY, 10075, USA. Computational Biology Center, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA. pmeyerr@us.ibm.com. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. pmeyerr@us.ibm.com. Computational Biology Center, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA. gcecchi@us.ibm.com"
Journal Title:		Nat Commun
Year:		2018
Volume:		20181126
Issue:		1
Page Number:		4979 -
DOI:		10.1038/s41467-018-07439-9
ISSN/ISBN:		2041-1723 (Electronic) 2041-1723 (Linking)
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"
Keywords:		"Humans *Language Models, Biological *Odorants Olfactory Perception/physiology Semantics;"
Notes:		"MedlineGutierrez, E Dario Dhurandhar, Amit Keller, Andreas Meyer, Pablo Cecchi, Guillermo A eng England 2018/11/28 Nat Commun. 2018 Nov 26; 9(1):4979. doi: 10.1038/s41467-018-07439-9"