In recent years, artificial intelligence (AI) technologies, particularly machine learning (ML) and deep learning (DL), have demonstrated significant potential in pharmacological research on traditional Chinese medicine (TCM). Due to the complexity of TCM compositions, targets, and pathways, conventional experimental methods encounter limitations in analyzing dose-response relationships and synergistic mechanisms. AI, with its advanced learning and data-processing capabilities, enables the integration of complex information, thereby enhancing the systematic approach, efficiency, and accuracy of research, and creating new opportunities for scientific discovery. This article reviews the latest progress in applying AI to TCM pharmacology, focusing on core algorithms, typical application scenarios, and future technological challenges, with the aim of providing theoretical support and methodological references for the modernization of TCM.
Wang et al. (Mon,) studied this question.
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