Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Samuel Boobier, David R. J. Hose, A. John Blacker and Bao N. Nguyen ()
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Samuel Boobier: University of Leeds, Woodhouse Lane
David R. J. Hose: Chemical Development, Pharmaceutical Technology and Development, Operations, AstraZeneca
A. John Blacker: University of Leeds, Woodhouse Lane
Bao N. Nguyen: University of Leeds, Woodhouse Lane

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.

Date: 2020
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DOI: 10.1038/s41467-020-19594-z

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