在超临界处理方法中使用机器学习对febuxostat溶解度进行高级混合计算分析
Turki Al Hagbani1, Rami M Alzhrani2, Majed Ahmed Algarni3
1Saudi Food and Drug Authority, Riyadh, Saudi Arabia. T.alhagbani@gmail.com.
Scientific reports
|July 12, 2025
概括
超临界流体 (SCF) 为药物溶解性提供了一个环保的替代方案. 机器学习模型使用SCF准确预测了febuxostat溶解度,投票回归模型实现了0.980 R2.2.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 超临界流体 (SCF) 在工业应用中越来越多地被用作有机溶剂的可持续替代品.
- SCF,特别是超临界CO2,显示出显著的潜力,以提高水溶性较差的药物的可溶性.
- 与传统方法相比,基于SCF的工艺提供了诸如环保,成本效益,安全和提高产品纯度等优势.
研究的目的:
- 开发和评估机器学习模型,用于预测febuxostat (FBX) 在超临界流体中的可溶性.
- 用计算建模研究温度和压力对FBX溶解度的影响.
- 为了比较不同的回归模型和优化技术的性能,用于可溶性预测.
主要方法:
- 使用的机器学习回归模型:高斯过程回归 (GPR) 和K-最近邻居 (KNN).
- 开发了一种结合GPR和KNN预测的投票回归模型.
- 使用哈里斯·霍克斯优化 (HHO) 算法对机器学习模型进行超参数调整.
主要成果:
- GPR模型获得了0.819的R2得分,KNN模型获得了0.854.
- 投票回归模型表现出卓越的性能,R2得分为0.980.
- 优化的投票模型表现出较低的错误率:RMSE为2.78 × 10−1和MAPE为3.81 × 10−2.
结论:
- 综合投票回归模型在预测febuxostat溶解度方面明显优于单个GPR和KNN模型.
- 使用HHO算法进行超参数优化可以提高可溶性模型的预测准确性.
- 机器学习方法,特别是优化投票模型,提供了一种可靠和高效的方法来建模药物在超临界流体中的溶解性.
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