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Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine
Jingqi Zeng, Xiaobin Jia · 2024-11-18 · via cs.LG updates on arXiv.org
AI 总结

  1. 研究构建了中医药多维知识图谱，将传统中医理论与现代生物医学相连接。通过特征工程和嵌入技术处理关键中医术语及中药饮片，引入药性作为虚拟节点，利用图神经网络和注意力机制建模分析了6080个中药方剂，实现了对中药饮片在方剂中作用的量化评估。

  2. 方法在215个针对COVID-19的中药方剂上得到验证，提供了可解释的模型、开源数据与代码。该研究为推进中医药理论和药物发现提供了稳健工具，相关资源和代码已分别发布于Zenodo（https://zenodo.org/records/13763953）和GitHub（https://github.com/ZENGJingqi/GraphAI-for-TCM）。

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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