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探索人工智能方法来预测传统中医药中活性化合物的协同效应,基于分子兼容性理论
Yiwen Wang1, Tong Wu1, Xingyu Li1
1College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
人工智能 (AI) 有助于预测传统中医药 (TCM) 化合物的协同效应,克服传统方法的局限性. 这种方法提高了对复杂疾病的TCM组合疗法的理解和应用.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 传统中国医药 传统中国医药
背景情况:
- 结合疗法对于复杂疾病至关重要,因为它具有协同效应,并减少了副作用.
- 传统中医 (TCM) 使用"君主,部长,助手,使者"理论来确定药物兼容性,形成了许多经典公式.
- 传统医学复杂性阻碍了使用传统方法对协同模式的全面分析.
研究的目的:
- 审查人工智能 (AI) 在预测TCM活性化合物的协同效应方面的应用进展.
- 探索AI在模拟TCM组合关系中的潜力,以实现现代化药物发现.
- 解决AI在TCM协同预测中的挑战和前景.
主要方法:
- 对人工智能在TCM活性化合物的协同预测中的应用进行审查.
- 分析人工智能从多omics和结构数据中挖掘协同模式的能力.
- 探索人工智能用于建模和优化TCM组合.
主要成果:
- 人工智能为资源密集型传统实验方法提供了一种有效的替代方案,用于识别协同模式.
- 人工智能可以从各种数据源中挖掘复杂的协同模式,促进TCM研究.
- 人工智能为建模和优化TCM组合提供了一个强大的工具.
结论:
- 人工智能有助于推进TCM活性化合物的协同预测.
- 人工智能有助于现代化TCM理论和方法的创新.
- 人工智能驱动的方法对发现新的TCM组合疗法具有重大前景.
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