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DeepNose: Using artificial neural networks to represent the space of odorants
- Source :
- ICML
- Publication Year :
- 2018
- Publisher :
- Cold Spring Harbor Laboratory, 2018.
-
Abstract
- The olfactory system employs an ensemble of odorant receptors (ORs) to sense odorants and to derive olfactory percepts. We trained artificial neural networks to represent the chemical space of odorants and used that representation to predict human olfactory percepts. We hypothesized that ORs may be considered 3D spatial filters that extract molecular features and can be trained using conventional machine learning methods. First, we trained an autoencoder, called DeepNose, to deduce a low-dimensional representation of odorant molecules which were represented by their 3D spatial structure. Next, we tested the ability of DeepNose features in predicting physical properties and odorant percepts based on 3D molecular structure alone. We found that despite the lack of human expertise, DeepNose features led to perceptual predictions of comparable accuracy to molecular descriptors often used in computational chemistry. We propose that DeepNose network can extract de novo chemical features predictive of various bioactivities and can help understand the factors influencing the composition of ORs ensemble.
- Subjects :
- Olfactory system
Artificial neural network
Computer science
business.industry
media_common.quotation_subject
05 social sciences
Pattern recognition
Space (commercial competition)
Autoencoder
Perception
0501 psychology and cognitive sciences
Artificial intelligence
business
Representation (mathematics)
050107 human factors
psychological phenomena and processes
media_common
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICML
- Accession number :
- edsair.doi.dedup.....1dd76a8d733c0ca458d82d0989f334f9
- Full Text :
- https://doi.org/10.1101/464735