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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)...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Shuhui Wu, Ting Zhang, Yunxia Tao, Lina Fu, Ying Chen, Weidong Qiang, Enzhong Li
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2025-07-01
Schriftenreihe:Scientific Reports
Schlagworte:
Bagging regression
Hyperparameter tuning
Tree-Based Parzen Estimators
CO2 density
Solubility prediction
Raloxifene
Online-Zugang:https://doi.org/10.1038/s41598-025-05428-9
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