使用机器学习算法将分析物的物理化学性质与溶剂的汉森溶解度参数进行关联,以预测合适的提取溶剂
Eman A Mostafa1, Mohammad Abdul Azim2, Asmaa A ElZaher2
1Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt. eman.saleh@pharma.cu.edu.eg.
Scientific reports
|August 13, 2024
概括
人工神经网络预测液-液提取的最佳溶剂,简化了生物分析样本的准备. 这种方法减少了有机溶剂的使用,与绿色化学原则保持一致.
科学领域:
- 计算化学是一种计算化学.
- 分析化学是一种分析化学.
- 生物技术是生物技术.
背景情况:
- 液体-液体提取 (LLE) 对于生物分析样本准备至关重要.
- 选择合适的提取溶剂是一个耗时且具有挑战性的过程.
- 人工神经网络 (ANN) 为复杂的数据分析和预测提供了强大的工具.
研究的目的:
- 开发和验证一个强大的人工神经网络 (ANN) 模型,用于预测适合液液提取 (LLE) 的溶剂.
- 为了简化生物分析样本准备中的溶剂选择过程.
- 通过尽量减少有机溶剂消耗,促进绿色化学.
主要方法:
- 使用分析描述符作为输入构建了一个ANN模型.
- 该模型预测了最佳提取溶剂的汉森溶解度参数.
- 实验验证涉及从人体血中提取20种不同的药物,使用预测的溶剂,并通过HPLC/UV量化回收.
主要成果:
- 开发的ANN模型成功地预测了各种分析物的合适提取溶剂.
- 实验验证证明了使用预测的溶剂组合有效提取各种药物.
- 该模型促进了对LLE.有效识别最佳溶剂混合物的有效识别.
结论:
- 该ANN模型为生物分析中LLE溶剂预测提供了一种可靠和有效的方法.
- 这种方法显著减少了与溶剂选择相关的劳动和时间.
- 该模型通过在分析程序中最大限度地减少有机溶剂的使用来支持绿色化学倡议.
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