Artificial intelligence algorithms drive the deciphering of traditional Chinese medicine by analyzing the chemicalome, targetome, and bioactivome

Artificial intelligence is fundamentally reshaping the paradigm of research in traditional Chinese medicine. This review systematically elaborates on how artificial intelligence algorithms drive the modernization of traditional Chinese medicine through the integrated analysis of three core concepts: the chemicalome, defined as the holistic set of chemical constituents; the targetome, representing the complete repertoire of interacting biological macromolecules; and the bioactivome, referring to the cascade of integrated biological activities and phenotypic outcomes. The review first demonstrates how artificial intelligence comprehensively characterizes complex in vitro and in vivo chemicalomes by transforming mass spectrometry analysis and metabolite identification. Subsequently, it analyzes how deep learning and graph neural networks systematically predict and support the validation of the targetome, in synergy with high-throughput experimental technologies. Furthermore, the review explains how artificial intelligence, through the integration of multi-omics data, deciphers the bioactivome resulting from traditional Chinese medicine interventions and reveals multi-pathway synergistic mechanisms. Finally, it discusses methodologies for establishing comprehensive interconnections among the chemicalome, targetome, and bioactivome. This analytical framework suggests that artificial intelligence is not merely a tool for improving research efficiency but also a foundational methodology that systematically decodes traditional Chinese medicine and bridges traditional knowledge with modern scientific discovery.