Details

Autor(en) / Beteiligte

• 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

Titel

Predicting human olfactory perception from chemical features of odor molecules

Ist Teil von

• Science (American Association for the Advancement of Science), 2017-02, Vol.355 (6327), p.820-826

Ort / Verlag

United States: American Association for the Advancement of Science

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Science Online_科学在线

Beschreibungen/Notizen

• 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.