Improved Solubility Predictions in scCO Using Thermodynamics-Informed Machine Learning Models.

Journal: Journal of chemical information and modeling
PMID: 40234235

Abstract

Accurate solubility prediction in supercritical carbon dioxide (scCO) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive solubility database containing 31,975 records has been compiled, providing a foundation for developing predictive models applicable to a diverse class of chemical compounds, with a particular focus on drug-like substances. In this study, we propose a domain-aware machine learning approach that incorporates thermodynamic properties governing phase transitions to solubility predictions in scCO. Predictive models were developed using the CatBoost algorithm and a graph-based architecture employing directed message passing to identify the most effective approach. Furthermore, auxiliary properties of the solute, including melting point, critical parameters, enthalpy of vaporization, and Gibbs free energy of solvation, were predicted as part of this work. The findings underscore the efficacy of incorporating domain-specific thermodynamic features to enhance the predictive accuracy of scCO solubility modeling. The interpretation and the applicability domain assessment have confirmed the qualitative selection of the employed descriptors, demonstrating their ability to generalize to unique compounds that fall outside the defined domain.

Authors

  • Dmitriy M Makarov
    Laboratory of Multiscale Modeling of Molecular Systems, G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.
  • Nikolai N Kalikin
    Laboratory of Multiscale Modeling of Molecular Systems, G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.
  • Yury A Budkov
    Laboratory of Multiscale Modeling of Molecular Systems, G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.
  • Pavel Gurikov
    Laboratory of Multiscale Modeling of Molecular Systems, G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.
  • Sergey E Kruchinin
    G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.
  • Abolghasem Jouyban
    b Kimia Idea Pardaz Azarbayjan (KIPA) Science Based Company , Tabriz University of Medical Sciences , Tabriz , Iran.
  • Michael G Kiselev
    Laboratory of NMR-Spectroscopy and Numerical Methods of Investigation of Liquid Systems, G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.

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