机器学习增强的纳米粒子设计用于精确的癌症药物输送
Qingquan Wang1, Yujian Liu1, Chenchen Li1
1School of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|June 19, 2025
概括
机器学习 (ML) 为优化癌症纳米医学中的纳米粒子 (NP) 设计提供了创新解决方案. 这种方法加速了NP合成,并增强了对纳米生物相互作用的理解,以改善药物输送和治疗结果.
科学领域:
- 纳米医学是一种纳米医学.
- 计算生物学 计算生物学
- 在瘤学中的机器学习
背景情况:
- 基于纳米粒子 (NP) 的药物递送显示了针对性癌症治疗的前景.
- 在NP合成优化和理解复杂的体内纳米-生物相互作用方面存在重大挑战.
- 目前的局限性阻碍了纳米医学的全部潜力,以有效治疗癌症.
研究的目的:
- 审查机器学习 (ML) 在纳米粒子 (NP) 癌症药物递送系统的整个生命周期中的应用.
- 突出ML如何解决NP设计,合成和体内性能方面的挑战.
- 讨论ML在推进精确癌症纳米医学方面的变革潜力.
主要方法:
- 对 ML 和纳米医学的计算方法近期进展的审查.
- 在NP合成和配方中检查ML应用.
- 分析ML在理解和预测纳米生物相互作用 (蛋白相互作用,循环,瘤透,细胞吸收) 中的作用.
主要成果:
- 机器学习可以加速寻找最佳的NP合成参数,简化配方.
- ML模型可以预测和优化瘤微环境 (TME) 和细胞内化过程中的NP行为.
- ML促进了NP的合理设计方法,提高了药物递送效率.
结论:
- 机器学习具有很大的潜力,可以克服癌症纳米医学当前的挑战.
- 机器学习使纳米粒子的合理设计成为可能,以提高药物输送和治疗疗效.
- 机器学习的整合对于推进精确癌症纳米医学至关重要.
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