Imputation of Sensory Properties Using Deep Learning
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Authors
Mahmoud, Samar
Irwin, Benedict
Chekmarev, Dmitriy
Vyas, Shyam
Kattas, Jeff
Whitehead, Thomas
Mansley, Tamsin
Bikker, Jack
Segall, Matthew
Journal Title
Journal of Computer-Aided Molecular Design
Type
Article
This Version
AM
Metadata
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Conduit, G., Mahmoud, S., Irwin, B., Chekmarev, D., Vyas, S., Kattas, J., Whitehead, T., et al. Imputation of Sensory Properties Using Deep Learning. Journal of Computer-Aided Molecular Design https://doi.org/10.17863/CAM.77085
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.
Sponsorship
Royal Society
Funder references
Royal Society (URF\R\201002)
Embargo Lift Date
2024-10-19
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.77085
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329636
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All rights reserved
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http://www.rioxx.net/licenses/all-rights-reserved
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