Numerical optimization of drug solubility inside the supercritical carbon dioxide system using different machine learning models

This research comprehensively investigates the solubility characteristics of five distinct drugs including: Nystatin, Niflumic acid, Tolfenamic acid, Glibenclamide, and Rivaroxaban, across a range of pressure (P) and temperature (T) conditions. The solubility is computed in supercritical carbon dioxide as the solvent. It was aimed to build a holistic view of solubility estimation using machine learning technique. To predict drug solubility accurately, three regression models- K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Polynomial Regression (PR)-were employed, with hyperparameter optimization conducted using the Harmony Search (HS) algorithm. Performance evaluation metrics, including R-squared (R2) scores, Root Mean Square Error (RMSE), and Maximum Error, were employed to assess model effectiveness. Notably, HS-PR emerged as the top-performing model, achieving an impressive score of 0.96449 in terms of R2 metric, highlighting its proficiency in modeling drug solubility under varying conditions.

Automated summary

This research comprehensively investigates the solubility characteristics of five distinct drugs under varying pressure and temperature conditions using a machine learning technique. The study finds that the Polynomial Regression model optimized with the Harmony Search algorithm performs the best in predicting drug solubility.