Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine

Through the rational compatibility of herbal medicines, TCM achieves synergistic effects and toxicity reduction (1). This core principle has demonstrated significant advantages in treating complex diseases. For example, Lianhua Qingwen Capsules have shown remarkable efficacy in alleviating symptoms and reducing hospitalization time for COVID-19 patients (2, 3). Additionally, PHY906, developed by Yale University based on the traditional Huangqin Decoction, has improved colorectal cancer treatment outcomes as a chemotherapy adjuvant (4). In recent years, the rapid development of artificial intelligence (AI) has introduced novel opportunities for investigating the complex mechanisms underlying TCM (5, 6). AI's exceptional data processing capabilities, particularly in multi-dimensional data analysis and complex relationship modeling, are transforming traditional medicine from experience-driven to datadriven paradigms (7-9). Notably, Graph Artificial Intelligence (GraphAI) offers a unique toolkit for exploring complex network-structured data by integrating knowledge graphs, graph computation, and graph neural networks (GNNs) (10, 11). The core challenges of TCM compatibility--complex interactions involving multiple components, targets, and pathways-- align closely with GraphAI's strengths in handling intricate relationships (12-14).