Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail 81442, Saudi Arabia.
Depaertmen of Pharmaceutics, College of Pharmacy, University of Hail, Hail 81442, Saudi Arabia.
Molecules. 2022 Aug 12;27(16):5140. doi: 10.3390/molecules27165140.
Nowadays, supercritical CO(SC-CO) 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 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 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 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, 1.33 × 10, and 2.33 × 10, 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) according to the mentioned metrics and other visual analysis.
如今，超临界 CO（SC-CO）被认为是制药工业中具有挑战性的有机溶剂的一种有前途的替代品。使用新型人工智能（AI）方法通过 SC-CO 系统预测和验证药物溶解度已被认为是一种有趣的方法。本工作旨在通过机器学习（ML）技术评估三苯氧胺作为化疗药物在 SC-CO 中的溶解度。本研究采用并增强了三种利用 Adaboost 方法的不同模型。这些模型包括 K-最近邻（KNN）、Theil-Sen 回归（TSR）和高斯过程（GPR）。考虑了两个输入，压力和温度，以分析可用数据。此外，输出是 Y，即溶解度。结果表明，基于分析结果，ADA-KNN、ADA-GPR 和 ADA-TSR 的 R 值分别为 0.996、0.967 和 0.883。此外，它们的 MAE 指标的误差率分别为 1.98×10、1.33×10 和 2.33×10。选择一个名为 ADA-KNN 的模型作为最佳模型，并根据所述指标和其他可视分析，获得最佳值，最佳值可以表示为一个向量：（X1 = 329，X2 = 318.0，Y = 6.004×10）。
