在超临界二氧化碳中对Lacosamide溶解度的研究,使用机器学习模型
1Department of Chemical Engineering, Marv.C., Islamic Azad University, Marvdasht, Iran. Nadia.Esfandiari@iau.ac.ir.
Scientific reports
|November 27, 2025
概括
机器学习模型准确地预测了lacosamide在超临界二氧化碳 (SC-CO2) 中的溶解度,克服了药物溶解度不佳的问题. 这种方法有助于优化制药过程并提高生物可用性.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 拉科萨米德是一种抗药物,在常规溶剂中溶解性差,限制了其生物可用性.
- 超临界二氧化碳 (SC-CO2) 为制药应用提供了一个环保的替代溶剂.
研究的目的:
- 通过使用各种机器学习技术,模拟和预测lacosamide在SC-CO2中的可溶性.
- 评估不同机器学习模型在预测不同条件下的西胺溶解度方面的准确性.
主要方法:
- 采用了机器学习模型,包括梯度增强决策树 (GBDT),多层感知器 (MLP),随机森林 (RF),高斯过程回归 (GPR),极端梯度增强 (XG Boost) 和多项式回归 (PR).
- 利用跨越广泛压力和温度范围的实验溶解度数据进行模型训练和验证.
- 使用确定系数 (R2) 评估模型性能.
主要成果:
- 所有机器学习模型都表现出在预测lacosamide在SC-CO2中的溶解度方面的能力.
- GBDT (R2 = 0.9989),XG Boost (R2 = 0.9986) 和MLP (R2 = 0.9975) 实现了最高的预测准确率.
- 这些模型有效地捕捉了溶解度,压力和温度之间的非线性关系.
结论:
- 机器学习算法与实验数据相结合,为预测超临界系统中药物溶解度提供了强大的方法.
- 这种预测能力有助于优化涉及超临界流体的制药过程.
- 这项研究突出了通过优化在SC-CO2中的加工来提高lacosamide生物可用性的潜力.
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