Anti-Cancer Drug Solubility Development within a Green Solvent: Design of Novel and Robust Mathematical Models Based on Artificial Intelligence

Nowadays, supercritical CO(2)(SC-CO(2)) is known as a promising alternative for challengeable organic solvents in the pharmaceutical industry. The mathematical prediction and validation of drug solubility through SC-CO(2) system using novel artificial intelligence (AI) approach has been considered as an interesting method. This work aims to evaluate the solubility of tamoxifen as a chemotherapeutic drug inside the SC-CO(2) via the machine learning (ML) technique. This research employs and boosts three distinct models utilizing Adaboost methods. These models include K-nearest Neighbor (KNN), Theil-Sen Regression (TSR), and Gaussian Process (GPR). Two inputs, pressure and temperature, are considered to analyze the available data. Furthermore, the output is Y, which is solubility. As a result, ADA-KNN, ADA-GPR, and ADA-TSR show an R(2) of 0.996, 0.967, 0.883, respectively, based on the analysis results. Additionally, with MAE metric, they had error rates of 1.98 × 10(−6), 1.33 × 10(−6), and 2.33 × 10(−6), respectively. A model called ADA-KNN was selected as the best model and employed to obtain the optimum values, which can be represented as a vector: (X1 = 329, X2 = 318.0, Y = 6.004 × 10(−5)) according to the mentioned metrics and other visual analysis.