Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method.

Over the last decades, industrial employment of supercritical fluids (SCFs) as a trustworthy alternative of organic solvents has increased substantially. SCFs (mainly COSCF) have illustrated great potential to improve the solubility of poorly water-soluble drugs. Compared to traditional procedures, SCF-based processes possess irrefutable advantages like good sustainability, eco-friendliness, affordability, safety during application, thermodynamic stability, low consumption of energy and obtaining new products with better purity. This research was done with the aim of modeling the solubility of febuxostat (FBX) drug with the help of machine learning methods. Temperature and pressure are the input values on which the modeling is done. Regression models including GPR, KNN, and a voting regression model using these two basic models are employed in this research. As an innovative aspect of this research, in addition to the Voting model, the HHO algorithm has been used to tune the hyper-parameters of the models. The final models obtained were then evaluated and compared using different criteria. With the R criterion, the GPR predictive model has a score of 0.819 and the KNN model has a score of 0.854, but the voting model has a score of 0.980, which shows that the combined voting method using the other two models has a better result than both of them. Also, the RMSE error rate of the Voting model is 2.78 × 10 and with MAPE metric the error of this model is 3.81 × 10.