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Imputation of sensory properties using deep learning.

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Mahmoud, Samar 
Irwin, Benedict 
Chekmarev, Dmitriy 
Vyas, Shyam 
Kattas, Jeff 


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.



Deep learning, Imputation, In silico model, Quantitative structure–activity relationship, Sensory properties, Algorithms, Deep Learning, Humans, Quantitative Structure-Activity Relationship, Sensory Receptor Cells, Uncertainty

Journal Title

J Comput Aided Mol Des

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Springer Science and Business Media LLC


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Royal Society (URF\R\201002)
Royal Society