Intelligence modeling of nanomedicine manufacture by supercritical processing in estimation of solubility of drug in supercritical CO2

Abstract The primary goal of this research is to apply bagging-based regression techniques to forecast the solubility of raloxifene and the density of carbon dioxide (CO₂). Bagging regression models were utilized, namely Bagging Bayesian Ridge Regression (BAG-BRR), Bagging Linear Regression (BAG-LR)...

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Autors principals:	Shuhui Wu, Ting Zhang, Yunxia Tao, Lina Fu, Ying Chen, Weidong Qiang, Enzhong Li
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2025-07-01
Col·lecció:	Scientific Reports
Matèries:	Bagging regression Hyperparameter tuning Tree-Based Parzen Estimators CO2 density Solubility prediction Raloxifene
Accés en línia:	https://doi.org/10.1038/s41598-025-05428-9
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