Imputation of sensory properties using deep learning.
J Comput Aided Mol Des
; 35(11): 1125-1140, 2021 11.
Article
en En
| MEDLINE
| ID: mdl-34716833
ABSTRACT
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 R2 between 0.26 and 0.45 over the next best method for each sensory property. We also demonstrate that robust uncertainty estimates generated by the imputation model enable the most accurate predictions to be identified and that imputation also more accurately predicts activity cliffs, where small changes in compound structure result in large changes in sensory properties. In combination, these results demonstrate that the use of imputation, based on data from less expensive, early experiments, enables better selection of compounds for more costly studies, saving experimental time and resources.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Células Receptoras Sensoriales
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Comput Aided Mol Des
Asunto de la revista:
BIOLOGIA MOLECULAR
/
ENGENHARIA BIOMEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Reino Unido