Intelligence modeling of solubility of raloxifene and density of solvent for green supercritical processing of medicines for enhanced solubility.

In this study, a dataset for solubility of raloxifene and CO2 density was analyzed using different regression models to reveal the correlation between inputs and drug solubility via supercritical processing. The models were developed and analyzed for their accuracy in predicting the process variables. Three models of Elastic Net Regression (ENR), Orthogonal Matching Pursuit (OMP), and Gaussian Process Regression (GPR) are optimized for the dataset of drug. For CO2 density estimation, GPR turned out to be the best R2, which confirmed a robust correlation. Therefore, the GPR model can be considered the most accurate and reliable for predicting CO2 density in this context. Regarding solubility, both the ENR and OMP models demonstrated similar performance with high R2 of 0.89062 and 0.89125, respectively. However, GPR outperformed the others with an impressive R2 of 0.97755, a lower RMSE of 3.3221E-01, and a relatively lower AARD% of 7.08009E + 00. Hence, the GPR model is recommended for accurate solubility predictions of raloxifene. Overall, the GPR model consistently demonstrated excellent predictive performance for both CO2 density and solubility, making it the preferred choice for modeling these relationships.