Advancing nanomedicine production via green method: Modeling and simulation of pharmaceutical solubility at different temperatures and pressures

Abstract

This study employs machine learning algorithms to model the mole fraction of paracetamol drug and the density of supercritical CO2 as a solvent. The tree-based models were employed to evaluate the impact of temperature and pressure on the fluctuations in drug solubility. The dataset utilized in this study has two input variables: temperature and pressure. Conversely, two factors were regarded as outputs: the solubility of drug and the density of solvent. To model the mole fraction and density, Gaussian Process Regression (GPR), Decision Tree (DT), and Linear Regression (LR) models were trained by some collected experimental data. These models were boosted with AdaBoost algorithm to enhance performance. The Firework Algorithm (FWA) was used to optimize the models’ hyper-parameters. All models performed well in predicting both outputs, but outcomes demonstrated that the ADA-GPR model, with R2 values of 0.995 and 0.996, respectively, achieved the highest accuracy for predicting both mole fraction and density. Also, the Explained Variance Scores of this model are 0.995 and 0.998 for mole fraction and density, respectively.