关于使用人工智能和纳米信息学来开发纳米药物的新视角
Nandita Tyagi1, Sneha Singh1, Seema Dagar2
1Guru Jambheshwar University of Science and Technology, a University in Hisar, Haryana.
Current topics in medicinal chemistry
|January 22, 2026
概括
人工智能 (AI) 和机器学习 (ML) 通过优化物理化学性质来加速纳米医学的发展. 纳米信息学和基于生理学的药理动力学 (PBPK) 模型提高药物设计,预测分发,并评估毒性,以获得更安全,更有效的疗法.
科学领域:
- 生物医学工程 生物医学工程
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 药物开发是昂贵和耗时的.
- 人工智能 (AI) 和机器学习 (ML) 为设计翻译纳米药物提供了解决方案.
- 纳米医学的有效性取决于物理化学特性,如大小,形状和电荷.
研究的目的:
- 审查纳米信息学和人工智能在翻译纳米药物的实施情况.
- 突出纳米交付系统设计的计算策略.
- 检查AI/ML贡献,挑战和纳米医学发展的未来方向.
主要方法:
- 定量结构-活动/财产关系 (QSAR/QSPR) 模型用于财产优化.
- 基于生理学的药理动力学 (PBPK) 模型用于预测生物分布和毒性.
- 设计纳米交付系统,评估纳米毒性和开发模拟模型的AI/ML工具.
主要成果:
- 纳米信息学可以系统地优化纳米药物特性,以提高功能.
- PBPK模型预测药物和纳米药物分布,有助于毒性评估.
- AI/ML促进了纳米材料的选择和体外/体外仿真模型的开发.
结论:
- 人工智能和纳米信息学显著加快了纳米医学从临床到临床的转化.
- 计算方法在纳米医学开发过程中最大限度地降低了健康和环境风险.
- 讨论的方法在多个科学学科中具有广泛的适用性,包括药物发现和纳米技术.
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