Improved Solubility Predictions in scCO2 Using Thermodynamics-Informed Machine Learning Models DOI
Dmitriy M. Makarov, Nikolai N. Kalikin, Yury A. Budkov

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Accurate solubility prediction in supercritical carbon dioxide (scCO2) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive database containing 31,975 records has been compiled, providing a foundation developing predictive models applicable to diverse class of chemical compounds, with particular focus on drug-like substances. In this study, we propose domain-aware machine learning approach that incorporates thermodynamic properties governing phase transitions predictions scCO2. Predictive were developed using CatBoost algorithm graph-based architecture employing directed message passing identify most effective approach. Furthermore, auxiliary solute, including melting point, critical parameters, enthalpy vaporization, Gibbs free energy solvation, predicted as part work. The findings underscore efficacy incorporating domain-specific features enhance accuracy scCO2 modeling. interpretation applicability domain assessment have confirmed qualitative selection employed descriptors, demonstrating their ability generalize unique compounds fall outside defined domain.

Language: Английский

Improved Solubility Predictions in scCO2 Using Thermodynamics-Informed Machine Learning Models DOI
Dmitriy M. Makarov, Nikolai N. Kalikin, Yury A. Budkov

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Accurate solubility prediction in supercritical carbon dioxide (scCO2) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive database containing 31,975 records has been compiled, providing a foundation developing predictive models applicable to diverse class of chemical compounds, with particular focus on drug-like substances. In this study, we propose domain-aware machine learning approach that incorporates thermodynamic properties governing phase transitions predictions scCO2. Predictive were developed using CatBoost algorithm graph-based architecture employing directed message passing identify most effective approach. Furthermore, auxiliary solute, including melting point, critical parameters, enthalpy vaporization, Gibbs free energy solvation, predicted as part work. The findings underscore efficacy incorporating domain-specific features enhance accuracy scCO2 modeling. interpretation applicability domain assessment have confirmed qualitative selection employed descriptors, demonstrating their ability generalize unique compounds fall outside defined domain.

Language: Английский

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