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).

Scientists from Mail.Ru Group, Insilico Medicine and MIPT have for the first time applied a generative neural network to create new pharmaceutical medicines with the desired characteristics. By using Generative Adversarial Networks (GANs) developed and trained to "invent" new molecular structures, there may soon be a dramatic reduction in the time and cost of searching for substances with potential medicinal properties. The researchers intend to use these technologies in the search for new medications within various areas from oncology to CVDs and even anti-infectives. The first results were submitted to Oncotarget in June 2016 and spent several months in review. Since that time, the group has made many improvements to the system and engaged with some of the leading pharmaceutical companies.