Predicting natural language descriptions of mono-molecular odorants
It is now possible to predict what a chemical smells like based on its chemical structure, however to date, this has only been done for a small number of odor descriptors. Here, using natural-language semantic representations, the authors demonstrate prediction of a much wider range of descriptors.
Enregistré dans:
Auteurs principaux: | E. Darío Gutiérrez, Amit Dhurandhar, Andreas Keller, Pablo Meyer, Guillermo A. Cecchi |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2018
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/95f89c126d6e43dfa8cacab3db4b0b20 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Serum Tumor Marker Dynamics as Predictive Biomarkers in NSCLC Chemo-Immunotherapy and Mono-Immunotherapy Maintenance: A Registry-Based Descriptive Study
par: Lang D, et autres
Publié: (2020) -
Hemipteran defensive odors trigger predictable color biases in jumping spider predators
par: Michael E. Vickers, et autres
Publié: (2020) -
Olfactory bulb acetylcholine release dishabituates odor responses and reinstates odor investigation
par: M. Cameron Ogg, et autres
Publié: (2018) -
Background stimulus delays detection of target stimulus in a familiar odor–odor combination
par: Naomi Gotow, et autres
Publié: (2021) -
Smell compounds classification using UMAP to increase knowledge of odors and molecular structures linkages.
par: Marylène Rugard, et autres
Publié: (2021)