AI-enabled target discovery in traditional Chinese medicine: from computational prediction to experimental validation

Traditional Chinese medicine (TCM), with a history of thousands of years, has been increasingly supported by modern clinical studies for its effectiveness in disease prevention, treatment, and rehabilitation. However, the complex, multi-component and multi-target characteristics of TCM make it challenging to identify bioactive compounds and understand the molecular mechanisms underlying their effects. These features also complicate the standardization of clinical applications. However, investigation of TCM targets remains limited. Systems biology and network pharmacology have become valuable approaches for analyzing the complex multi-component and multi-target interactions of TCM. Omics-based approaches, including transcriptomics and single-cell sequencing, have further improved the ability to characterize gene expression changes and cell heterogeneity. Recently, artificial intelligence (AI) has emerged as a powerful tool for identifying potential targets within TCM. The continuous improvement of professional TCM databases supports the integration of information including prescriptions, bioactive constituents, target interactions, disease associations and pharmacological annotations, gradually building a more comprehensive data system for TCM research. These databases serve as key resources, allowing AI-based approaches to identify targets in TCM. This review summarizes recent progress in AI-assisted TCM target identification, discusses experimental strategies for validating computational predictions, and outlines the main methodological challenges and future directions.