Predicting human olfactory perception from chemical features of odor molecules

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorith...

Full description

Saved in:
Bibliographic Details
Published inScience (American Association for the Advancement of Science) Vol. 355; no. 6327; pp. 820 - 826
Main Authors Keller, Andreas, Gerkin, Richard C., Guan, Yuanfang, Dhurandhar, Amit, Turu, Gabor, Szalai, Bence, Mainland, Joel D., Ihara, Yusuke, Yu, Chung Wen, Wolfinger, Russ, Vens, Celine, Schietgat, Leander, De Grave, Kurt, Norel, Raquel, Stolovitzky, Gustavo, Cecchi, Guillermo A., Vosshall, Leslie B., Meyer, Pablo
Format Journal Article
LanguageEnglish
Published United States American Association for the Advancement of Science 24.02.2017
The American Association for the Advancement of Science
Subjects
Accuracy
Adult
Algorithms
Competition
Crowdsourcing
Datasets as Topic
Fish
Garlic
Humans
Learning algorithms
Machine learning
Male
Mathematical models
Models, Biological
Odor intensity
Odorants
Odors
Olfaction
Olfactory Perception
Predictions
Psychophysics
Semantics
Sensory evaluation
Sensory properties
Smell
Spices
Sweet taste
Teams
Online AccessGet full text

Cover

More Information
Summary:It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors (“garlic,” “fish,” “sweet,” “fruit,” “burnt,” “spices,” “flower,” and “sour”). Regularized linear models performed nearly as well as random forest–based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
These authors contributed equally to this work
ISSN:0036-8075
1095-9203
1095-9203
DOI:10.1126/science.aal2014