使用基于物理的机器学习框架,预测纳米载体的素释放动力学.
Sonia Fathi-Karkan1, Abbas Rahdar2
1Natural Products and Medicinal Plants Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran; Department of Advanced Sciences and Technologies in Medicine, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran; Food and Drug Research Center, Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran.
Computational biology and chemistry
|January 25, 2026
概括
一个新的基于物理的机器学习框架准确地预测了各种纳米载体的黄素释放动力学. 这种方法使得数据驱动的配方工程能够优化控制释放纳米药物.
科学领域:
- 纳米医学是一种纳米医学.
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 黄素纳米配方表现出可变的释放动力学,阻碍了可预测的药物输送.
- 现有的模型缺乏统一的框架来分析跨多种纳米载体材料的扩散.
- 有限的数据集和不一致的实验条件阻碍了对黄素释放参数的理解.
研究的目的:
- 开发一个基于物理的机器学习框架,用于预测黄素释放动力学.
- 在异质纳米载体的预测中实现高物理一致性.
- 建立库尔库输送系统数据驱动工程的基础.
主要方法:
- 分析了75种库尔库纳米配方的数据集,使用了13个物理化学描述符.
- 释放动力学是使用两组分扩散模型来建模的.
- 一个具有单调性和非负性约束的物理信息神经网络 (PINN) 被训练并验证.
主要成果:
- PINN模型实现了0.92 R2和98%的物理一致性,表现优于基线模型.
- 使用31种独立配方进行外部验证,结果为0.885 R2.
- 设计空间映射确定了快速,平衡和持续释放配置文件的最佳参数.
结论:
- 开发的框架准确地预测了各种纳米载体的动力参数.
- 这种方法促进了库尔库输送系统的数据驱动的配方工程.
- 该方法为控制释放纳米药物的自动设计铺平了道路.
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