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.

翻译：传统中药（TCM）涉及复杂的配伍机制，其特点是多组分、多靶点的相互作用，难以量化。为应对这一挑战，我们应用图人工智能构建了一个连接传统中医理论与现代生物医学科学的中药多维知识图谱（https://zenodo.org/records/13763953）。通过特征工程与嵌入技术，我们处理了关键的中医术语与中药材（CHP），引入药性作为虚拟节点，并采用带注意力机制的图神经网络对6,080个中药方剂（CHF）进行建模与分析。我们的方法定量评估了中药材在方剂中的作用，并利用为COVID-19防治设计的215个方剂进行了验证。凭借可解释的模型、开源数据与代码（https://github.com/ZENGJingqi/GraphAI-for-TCM），本研究为推进中医理论与药物发现提供了有力的工具。