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

Abstract

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.