RT Journal Article
SR Electronic
T1 Reverse-engineering human olfactory perception from chemical features of odor molecules
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 082495
DO 10.1101/082495
A1 Andreas Keller
A1 Richard C. Gerkin
A1 Yuanfang Guan
A1 Amit Dhurandhar
A1 Gabor Turu
A1 Bence Szalai
A1 Joel D. Mainland
A1 Yusuke Ihara
A1 Chung Wen Yu
A1 Russ Wolfinger
A1 Celine Vens
A1 Leander Schietgat
A1 Kurt De Grave
A1 Raquel Norel
A1 DREAM Olfaction Prediction Challenge Consortium
A1 Gustavo Stolovitzky
A1 Guillermo Cecchi
A1 Leslie B. Vosshall
A1 Pablo Meyer
YR 2016
UL http://biorxiv.org/content/early/2016/10/21/082495.abstract
AB Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (“garlic”, “fish”, “sweet”, “fruit”, “burnt”, “spices”, “flower”, “sour”). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.One Sentence Summary Results of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.