Advanced modeling based on machine learning for evaluation of drug nanoparticle preparation via green technology: Theoretical assessment of solubility variations

Abstract

Variability of drug solubility in supercritical solvent was investigated theoretically in this study using multiple machine learning models. The method of supercritical preparation can be used to manufacture solid-dosage drugs with enhanced solubility which can increase drug bioavailability and efficacy. In this study, we applied bagging regression to three core models, namely SVM, KNN, and linear regression (LR) to predict the solubility and density of Hyoscine drug based on Temperature and Pressure parameters as inputs. The WOA optimization algorithm was used to tune hyperparameters, and the performance of these models was compared with r2, RMSE, MAE, and AARD% metrics. The results revealed that the BAG-KNN model outperformed the others in predicting solubility, with an r2 score of 0.989 and a low RMSE of 0.041. Also, the BAG-LR model had the highest AARD% value of 7.50974, indicating that it has higher average relative deviation from the actual density values. Overall, the bagging approach proved to be effective in improving the accuracy of the core models, and the WOA optimization algorithm was successful in fine-tuning their hyperparameters. These findings can have significant implications for the accurate prediction of chemical properties in various fields, such as drug discovery and material science.