Abstract
Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent.