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• W4224323786 abstract "Salsalate solubility in supercritical carbon dioxide was studied in this research by computational simulation to correlate the solubility to input parameters including temperature and pressure. A dataset was collected from resources and the models were correlated to the data. Both training and validation steps have been performed to implement the computational tasks. Indeed, we are dealing with a dataset with two P and T inputs and one output (drug solubility), and 32 data points. We chose three models based on Gaussian Process Regression (GPR) including simple (raw) GPR, Ada-boosted GPR, and Bagged GPR as two ensemble methods for correlation of the solubility data. All hyper-parameters were tuned for more general models and models evaluated with standard metrics. The GPR, Adaboost + GPR, and Bagging + GPR have scores of 0.9779, 0.9992, and 0.9795 using R-squared, respectively. Also, in terms of RMSE models have error rates of 1.25 × 10−4, 1.20 × 10−4, and 1.29 × 10−4, respectively. Finally, considering the standard criteria and visual analysis, the boosted GPR model is selected as the main model. The optimal values found as (P = 400, T = 338.0, Y = 0.003879)." @default.
• W4224323786 created "2022-04-26" @default.
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• W4224323786 date "2022-07-01" @default.
• W4224323786 modified "2023-10-18" @default.
• W4224323786 title "Development and validation of machine learning models for prediction of nanomedicine solubility in supercritical solvent for advanced pharmaceutical manufacturing" @default.
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• W4224323786 doi "https://doi.org/10.1016/j.molliq.2022.119208" @default.
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