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预测模拟溶剂对药物纳入聚合物纳米载体的影响:一种机器学习方法

Wei Ge^1,2, Ramindu De Silva^1,3,2, Yanan Fan^3,2

¹School of Chemistry, University of New South Wales, Sydney, New South Wales, Australia.

Macromolecular rapid communications

|July 31, 2025

概括

研究人员确定了提高聚合物微粒中药物负载的关键溶剂特性. 优先考虑具有高药物溶解度的溶剂对于在基于流动的组装过程中实现最佳负载至关重要.

科学领域:

材料科学材料科学材料科学
制药科学制药科学
计算化学计算化学

背景情况:

聚合物微粒是药物输送的关键纳米载体.
优化药物加载效率对于治疗疗效至关重要.
基于流程的组装为菌生产提供了一个可扩展的方法.

研究的目的:

确定提高聚合物微粒中药物负载的关键溶剂特性.
评估机器学习算法在预测药物负载方面的性能.
阐明溶剂特性与药物加载效率之间的关系.

主要方法:

作为模型化合物,利用聚乙烯甘醇-块-聚乙烯 (PEG-b-PS) 和黄素.
研究了40种不同的溶剂混合物在基于流动的组装过程中对药物负载的影响.
采用并比较了五种机器学习算法 (RF,GP,XGBoost,SVR,MLP),MLP显示出最高的有效性.

主要成果:

多层感知器 (MLP) 模型展示了药物负载的优异预测性能.
沙普利添加剂扩展 (SHAP) 确定了影响药物加载的关键溶剂描述剂.
黄素的溶解度被确定为高药物负载的最关键因素.

结论:

溶剂溶解度是实现高药载荷在聚合物微粒中的主要决定因素.
机器学习,特别是MLP,可以有效地预测和优化药物加载过程.
了解溶剂特性对于设计使用聚合物微粒的高效药物递送系统至关重要.

关键词:

药物输送是药物输送的过程.毒品装载毒品装载机器学习是机器学习.米塞尔 (Micelle) 是一个小女孩.

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