Artificial Intelligence in Medicinal Herb Breeding

Medicinal plant-derived bioactive compounds serve as a crucial foundation for natural pharmaceutical development. Contemporary breeding methodologies can boost both the quality and yield of these medicinally valuable compounds, thereby advancing the development of the pharmaceutical sector. However, conventional breeding encounters substantial challenges when addressing the intricate genetic architectures and polygenic regulatory networks characteristic of medicinal species. These traditional modalities are especially ineffective in optimizing multiple pharmacologically relevant traits while maintaining robust environmental adaptability across diverse cultivation conditions concurrently. Advanced computational tools are emerging for biological research with parallel development of artificial intelligence (AI), which have also been explored for their applications in medicinal plant breeding. In the current comprehensive review, we carried out a systematic examination of the state-of-the-art AI applications across different aspects of the breeding pipeline, encompassing multi-omics data integration, synthetic biology, precision gene editing, trait optimization, and intelligent monitoring systems. Meanwhile, this review elucidated current obstacles of data integration, model generalization, and environmental adaptation when applying AI in medicinal plants, and proposed a concept of constructing a genotype–environment–management (G×E×M) interactive intelligent breeding platform. The integration of AI with biotechnology emphasizes data-driven precision, computational analysis, and potential for trait customization, contributing to shaping new approaches in medicinal plant breeding gradually. Collectively, these developments may facilitate profound improvements in breeding efficiency, compound yield, and environmental sustainability.