https://www.scienceopen.com/hosted-document?doi=10.15212/AMM-2025-0045
Announcing a new publication for
Acta Materia Medica journal. Traditional Chinese medicine (TCM), characterized by multi-component, multi-target, and systemic therapeutic mechanisms, provides unique advantages in managing complex diseases. However, the inherent complexity of TCM formulations, including nonlinear component interactions, elusive compatibility principles, and a lack of quantitative biomarkers, has hindered the systematic elucidation of their efficacy mechanisms and clinical translation. Artificial intelligence (AI) technologies, including machine learning and deep learning, combined with network pharmacology approaches, have emerged as transformative tools to systematically characterize and model TCM’s complexity. Given that TCM and AI share a foundational emphasis on systemic interactions rather than isolated components, AI approaches can facilitate high-throughput prediction of bioactive components, rational design of synergistic formulas, and dynamic modeling of pharmacological effects. Recent interdisciplinary studies have harnessed AI to address TCM challenges including predicting bio-active constituents, optimizing herbal compatibility, and standardizing diagnostic parameters. Whereas prior reviews focused on AI applications in TCM data mining and drug development, this work comprehensively integrates active component prediction, compatibility mechanisms, and pharmacological effect modeling, and additionally discusses emerging applications of large-scale AI models in modern TCM research.
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Acta Materia Medica welcomes the submission of research articles, review articles, databases, mini reviews, commentaries, editorials, short communications, case report articles and study protocols.
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eISSN 2737-7946
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Luan Yin, Xinyun Xue and Shanshan Pan et al. Artificial intelligence in Traditional Chinese Medicine: systematic insights from data mining, large language models, and multimodal fusion.
Acta Materia Medica. 2025. Vol. 4(4):674-697. DOI: 10.15212/AMM-2025-0045