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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Samuel Boobier & David R. J. Hose & A. John Blacker & Bao N. Nguyen, 2020. "Machine learning with physicochemical relationships: solubility prediction in organic solvents and water," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19594-z
    DOI: 10.1038/s41467-020-19594-z
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    Cited by:

    1. Juran Noh & Hieu A. Doan & Heather Job & Lily A. Robertson & Lu Zhang & Rajeev S. Assary & Karl Mueller & Vijayakumar Murugesan & Yangang Liang, 2024. "An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Yuan Gao & Yueling Guo & Nurul Atiqah Romli & Mohd Shareduwan Mohd Kasihmuddin & Weixiang Chen & Mohd. Asyraf Mansor & Ju Chen, 2022. "GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 10(11), pages 1-28, June.

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