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Imputation of sensory properties using deep learning

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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.

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Data availability

We are unable to publish the full data set used in this study due to the proprietary nature and high cost of the data. Nevertheless, we believe that the first demonstration of the potential for deep learning imputation to make accurate predictions of such challenging in vivo properties warrants publication. To ensure reproducibility and enable comparisons with other methods, we have previously published benchmarking studies accompanied with public domain data sets including all predicted values (Whitehead et al. [33]; Irwin et al. [35]).

Code availability

The code used in this study is proprietary.

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Funding

GC would like to acknowledge financial support from the Royal Society.

Author information

Authors and Affiliations

  1. Optibrium Limited, Cambridge, UK

    Samar Mahmoud, Benedict Irwin, Tamsin Mansley & Matthew Segall

  2. Research and Development, International Flavors & Fragrances, Union Beach, NJ, USA

    Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas & Jack Bikker

  3. Intellegens Limited, Cambridge, UK

    Thomas Whitehead & Gareth Conduit

  4. University of Cambridge, Cambridge, UK

    Gareth Conduit

Authors
  1. Samar Mahmoud
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  2. Benedict Irwin
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  3. Dmitriy Chekmarev
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  4. Shyam Vyas
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  5. Jeff Kattas
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  6. Thomas Whitehead
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  7. Tamsin Mansley
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  8. Jack Bikker
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  9. Gareth Conduit
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  10. Matthew Segall
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Contributions

All authors contributed to the study conception and design. Data curation was performed by DC; modelling and analysis of the results was performed by SM; the Alchemite method was developed by GC and TW. The first draft of the manuscript was written primarily by SM with contributions from all authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Samar Mahmoud.

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Conflict of interest

SM, BI, TM and MS are employees of Optibrium Limited. DM, SV, JK and JB are employees of International Flavors & Fragrances, Inc. TW and GC are employees of Intellegens Limited.

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Mahmoud, S., Irwin, B., Chekmarev, D. et al. Imputation of sensory properties using deep learning. J Comput Aided Mol Des 35, 1125–1140 (2021). https://doi.org/10.1007/s10822-021-00424-3

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  • DOI: https://doi.org/10.1007/s10822-021-00424-3

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