PT - JOURNAL ARTICLE AU - Andreas Keller AU - Richard C. Gerkin AU - Yuanfang Guan AU - Amit Dhurandhar AU - Gabor Turu AU - Bence Szalai AU - Joel D. Mainland AU - Yusuke Ihara AU - Chung Wen Yu AU - Russ Wolfinger AU - Celine Vens AU - Leander Schietgat AU - Kurt De Grave AU - Raquel Norel AU - DREAM Olfaction Prediction Challenge Consortium AU - Gustavo Stolovitzky AU - Guillermo Cecchi AU - Leslie B. Vosshall AU - Pablo Meyer TI - Reverse-engineering human olfactory perception from chemical features of odor molecules AID - 10.1101/082495 DP - 2016 Jan 01 TA - bioRxiv PG - 082495 4099 - http://biorxiv.org/content/early/2016/10/21/082495.short 4100 - http://biorxiv.org/content/early/2016/10/21/082495.full 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.