Machine learning aided drug development: Assessing improvement of drug efficiency by correlation of solubility in supercritical solvent for nanomedicine preparation

Abstract

In this study, the solubility of phenytoin and the supercritical CO2 (solvent) density were investigated using three different models: Multilayer Perceptron (MLP), NU-SVR, and Poisson Regressor (POR). The models were optimized using the BA algorithm. The inputs for the models were temperature (T) in Kelvin and pressure (P) in bar, while the outputs were the solubility (y) of phenytoin and the CO2 density. For the solubility prediction, the MLP model achieved an impressive R2 score of 0.998 with a MSE of 0.051, a MAE of 0.200, and a MAPE of 0.080. The NU-SVR model also performed well with an R2 score of 0.966, MSE of 1.094, MAE of 0.708, and MAPE of 0.194. The POR model yielded an R2 score of 0.957, MSE of 0.959, MAE of 0.765, and MAPE of 0.337. For the CO2 density prediction, the MLP model achieved an excellent R2 score of 0.998 with an MSE of 75.232, MAE of 5.866, and MAPE of 0.018. The NU-SVR model performed reasonably well with an R2 score of 0.858, MSE of 3853.3, MAE of 49.697, and MAPE of 0.119. The POR model yielded an R2 score of 0.839, MSE of 4012.3, MAE of 52.297, and MAPE of 0.140. These results reveal that the MLP model provides highly reliable forecasts for both the solubility of phenytoin and the CO2 density.