Predicting human olfactory perception from chemical features of odor molecules

Andreas Keller, Richard C. Gerkin, Yuanfang Guan, Amit Dhurandhar, Gabor Turu, Bence Szalai, Joel D. Mainland, Yusuke Ihara, Chung Wen Yu, Russ Wolfinger, Celine Vens, Leander Schietgat, Kurt De Grave, Raquel Norel, Gustavo Stolovitzky, Guillermo A. Cecchi, Leslie B. Vosshall, Pablo Meyer, A. P. Bhondekar, P. C. BoutrosY. C. Chang, C. Y. Chen, B. W. Cherng, A. Dimitriev, A. Dolenc, A. O. Falcao, A. K. Golińska, M. Y. Hong, P. H. Hsieh, B. F. Huang, L. Hunyady, R. Kaur, M. D. Kazanov, R. Kumar, W. Lesiński, X. Lin, A. Matteson, Y. J. Oyang, B. Panwar, R. Piliszek, A. Polewko-Klim, GPS Raghava, W. R. Rudnicki, L. Saiz, R. X. Sun, M. Toplak, Y. A. Tung, P. Us, P. Várnai, JMG Vilar, M. Xie, D. Yao, M. Zitnik, B. Zupan

Research output: Contribution to journalArticlepeer-review

118 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)820-826
Number of pages7
JournalScience
Volume355
Issue number6327
DOIs
Publication statusPublished - 24 Feb 2017
Externally publishedYes

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