人工智能驱动的网络药理学:中国传统医学的多层次机制,从分子分析到患者分析
Guoqian Cui1,2,3, Muzi Li2,3, Wenbo Guo2,3
1Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, China.
Computational and structural biotechnology journal
|December 1, 2025
概括
人工智能增强了传统中医药 (TCM) 研究的网络药理学. 这种整合允许对TCM进行精确的分析.
科学领域:
- 综合医学是一个整体的医学.
- 计算生物学 计算生物学
- 药理学 药理学 是一个学科.
背景情况:
- 传统中医 (TCM) 提供复杂,多目标的治疗效果,但在机制阐明和标准化方面面临挑战.
- 网络药理学 (NP) 为了解TCM提供了一个整体的框架,整合了化学,奥米克和临床数据.
- 传统的NP方法与数据噪声,维度,动态和跨规模集成作斗争,限制了精确的分析和临床应用.
研究的目的:
- 审查用于TCM研究的人工智能-网络药理学 (AI-NP) 的进展.
- 突出AI在克服传统NP方法的局限性方面的作用.
- 探索AI-NP在从分子到患者层面的多尺度机制分析中的应用.
主要方法:
- 对AI-NP方法的系统审查.
- 检查使用人工智能的研究案例,在分子,细胞,组织和患者尺度上分析TCM机制.
- 在AI-NP.中综合当前的挑战和未来的方向.
主要成果:
- 人工智能,包括机器学习,深度学习和图形神经网络,显著增强了NP的权力.
- AI-NP使TCM的系统和准确的跨尺度机制分析成为可能.
- 典型的研究案例证明了AI-NP在理解从分子相互作用到患者结果的TCM方面的实用性.
结论:
- AI-NP代表了现代化TCM研究的关键进展.
- 人工智能-NP有助于更深入,更精确地理解TCM的系统治疗智慧.
- 未来AI-NP的发展有望释放TCM的全部临床潜力.
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