ArticleScientific reports2025
Predicting drug solubility in supercritical carbon dioxide green solvent using machine learning models based on thermodynamic properties.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Implantable Polymer-Based Medical Devices Go Supercritical: From Cleaning to Sterilization.Annals of biomedical engineering · 2026Review
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Authors and funding
2 authors.
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Abstract
Reliable prediction of drug solubility in supercritical carbon dioxide (scCO₂) is crucial for the efficient design of pharmaceutical processes, including particle engineering and supercritical fluid-based extraction. Given that experimental determination of drug solubility in scCO₂ is costly and time-consuming, this study employs machine learning models to predict drug solubility in scCO₂, offering the advantage over thermodynamic models and empirical correlations of being able to predict the solubility of drugs beyond the model's training range. In this work, authors use CatBoost, XGBoost, LightGBM, and RF models to predict the solubility of a set of drugs (Sixty-eight) in scCO
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