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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Summary: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), and Bagging Polynomial Regression (BAG-PR). The hyperparameters of these models were tuned using the Tree-Based Parzen Estimators algorithm to achieve optimal performance. The results demonstrate the efficacy of the bagging regression models in predicting both the CO2 density and the solubility of raloxifene. For the CO2 density prediction, BAG-BRR achieved a coefficient of determination (CoD/R2) of 0.83728, an RMSE of 6.0525E+01, and an AARD% of 1.16098E+01. BAG-LR attained a CoD of 0.85705, an RMSE of 5.8358E+01, and an AARD% of 1.11066E+01. BAG-PR exhibited superior performance with a CoD of 0.98559, an RMSE of 2.5934E+01, and an AARD% of 4.68598E+00. Similarly, for the solubility of raloxifene prediction, BAG-BRR achieved a CoD of 0.90615, an RMSE of 6.5797E−01, and an AARD% of 1.36868E+01. BAG-LR attained a CoD of 0.90002, an RMSE of 6.8669E−01, and an AARD% of 1.54778E+01. BAG-PR demonstrated outstanding performance with a CoD of 0.98565, an RMSE of 2.8158E−01, and an AARD% of 6.28460E+00. The findings highlight the potential of bagging regression models, particularly BAG-PR, for reliable and accurate predictions of CO2 density and the solubility of raloxifene.
ISSN:2045-2322

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