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

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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Bibliographic Details
Main Authors: Shuhui Wu, Ting Zhang, Yunxia Tao, Lina Fu, Ying Chen, Weidong Qiang, Enzhong Li
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Bagging regression
Hyperparameter tuning
Tree-Based Parzen Estimators
CO2 density
Solubility prediction
Raloxifene
Online Access:https://doi.org/10.1038/s41598-025-05428-9
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https://doi.org/10.1038/s41598-025-05428-9

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