Imputation of sensory properties using deep learning.
Deep learning
Imputation
In silico model
Quantitative structure–activity relationship
Sensory properties
Journal
Journal of computer-aided molecular design
ISSN: 1573-4951
Titre abrégé: J Comput Aided Mol Des
Pays: Netherlands
ID NLM: 8710425
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
11
06
2021
accepted:
15
10
2021
pubmed:
31
10
2021
medline:
8
3
2022
entrez:
30
10
2021
Statut:
ppublish
Résumé
Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network. The imputation model achieved a substantially higher accuracy of prediction, with improvements in R
Identifiants
pubmed: 34716833
doi: 10.1007/s10822-021-00424-3
pii: 10.1007/s10822-021-00424-3
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1125-1140Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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