用图神经网络量化中药复方配伍机制，助力现代中医研究。
Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine
- 构建中药多维知识图谱，融合传统理论与现代生物医学。
- 分析6080个复方，识别关键药材在配方中的作用权重。
- 开源模型与数据，适合中医药研究者与AI开发者使用。
中医复方具有多成分、多靶点相互作用的复杂配伍机制，难以量化。为此，我们引入图人工智能技术，构建连接传统中医理论与现代生物医学的中药多维知识图谱（https://zenodo.org/records/13763953）。通过特征工程与嵌入方法处理关键中医术语和中药饮片（CHP），将药性作为虚拟节点，并采用带注意力机制的图神经网络建模与分析6,080个中药复方（CHF）。该方法可定量评估中药饮片在复方中的角色，经215个用于新冠肺炎管理的复方验证有效。基于可解释模型、开源数据与代码（https://github.com/ZENGJingqi/GraphAI-for-TCM），本研究为推进中医理论发展与药物发现提供可靠工具。
原文摘要 · Abstract (English)
Traditional Chinese Medicine (TCM) involves complex compatibility mechanisms characterized by multi-component and multi-target interactions, which are challenging to quantify. To address this challenge, we applied graph artificial intelligence to develop a TCM multi-dimensional knowledge graph that bridges traditional TCM theory and modern biomedical science (https://zenodo.org/records/13763953 ). Using feature engineering and embedding, we processed key TCM terminology and Chinese herbal pieces (CHP), introducing medicinal properties as virtual nodes and employing graph neural networks with attention mechanisms to model and analyze 6,080 Chinese herbal formulas (CHF). Our method quantitatively assessed the roles of CHP within CHF and was validated using 215 CHF designed for COVID-19 management. With interpretable models, open-source data, and code (https://github.com/ZENGJingqi/GraphAI-for-TCM ), this study provides robust tools for advancing TCM theory and drug discovery.
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