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

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 CO density and the solubility of raloxifene. For the CO density prediction, BAG-BRR achieved a coefficient of determination (CoD/R) 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 CO density and the solubility of raloxifene.