在生成基于细胞的药物输送系统和可能与人工智能相遇的知识差距
Negin Mozafari1, Niloofar Mozafari2, Ali Dehshahri3,4
1Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran.
Molecular pharmaceutics
|July 10, 2023
概括
人工智能 (AI) 和机器学习 (ML) 提供预测模型,以克服基于细胞的药物输送系统的挑战. 这些AI/ML方法提高了针对性治疗的细胞衍生纳米载体的设计和安全性.
科学领域:
- 生物医学工程 生物医学工程
- 纳米医学是一种纳米医学.
- 药物输送系统 药物输送系统
背景情况:
- 基于细胞的药物递送系统利用细胞或细胞膜作为受控治疗释放的载体.
- 在开发这些系统方面存在重大挑战,需要用于财产评估和降低风险的预测模型.
- 整合人工智能 (AI) 和纳米技术为先进的纳米医学提供了创新的解决方案.
研究的目的:
- 探索AI和机器学习 (ML) 如何应对开发基于细胞的药物输送系统的挑战.
- 突出预测AI/ML模型在设计更安全,更有效的纳米载体方面的潜力.
- 审查当前基于细胞的传递策略及其相关障碍.
主要方法:
- 对基于细胞的药物输送系统的现有文献的审查.
- 对纳米医学中的AI和ML应用进行分析,重点是预测建模.
- 讨论AI类型及其与设计细胞衍生纳米载体的相关性.
主要成果:
- 人工智能和机器学习可以加速纳米医学中的数据挖掘和决策过程.
- 预测模型提供了一种途径,以减轻基于细胞的交付平台的不良影响.
- 该审查确定了关键的挑战,并展示了AI/ML作为开发先进的基于细胞的治疗方法的解决方案.
结论:
- 人工智能和机器学习对克服基于细胞的药物输送中的发展挑战具有重大前景.
- 这些技术可以提高细胞衍生纳米载体的设计,安全性和有效性.
- 未来的研究应该专注于利用AI/ML用于纳米医学应用中的精确控制和预测.
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