Machine learning estimating paracetamol solubility in supercritical CO2 by utilization of K-nearest neighbor regression and metaheuristic algorithms

Because of extensive usage of paracetamol by patients, its solubility improvement would have major impact on wellbeing. Supercritical processing can be used for nanonization of drug particles which in turn increases their solubility and consequently low dosage of drug for patients. This study presents the results of Neighbor-based ensemble models for predicting the mole fraction of paracetamol drug in supercritical solvent as well as solvent density at different conditions. The models were trained and evaluated using data of 40 instances. The K-nearest neighbor regression algorithm selected here as the base model, and ensemble methods of bagging and AdaBoost, were employed for model improvement. Additionally, two metaheuristic algorithms, BAT and GWO, were applied to adjust the hyperparameters of the models. The assessment of each model's performance was conducted through the utilization of three metrics, namely the R-squared score, MSE, and AARD percentage. The outcomes showed that the GWO-ADA-KNN model demonstrated superior performance in predicting both mole fraction and density, as evidenced by its respective R-squared scores of 0.98105 and 0.96719. These findings indicate that the proposed optimizer and models can predict accurately drug mole fraction and density under different conditions.