The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the "monarch, minister, assistant, envoy" compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.
Artificial Intelligence ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Drug Synergism

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

OBJECTIVES: To explore the efficacy of DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy for management of cancer pain and provide reference for its standardized clinical application. Methods and. RESULTS: Recommendations were formulated based on literature review and expert group discussion, and consensus was reached following expert consultation. The consensus recommendations are comprehensive, covering the entire treatment procedures from preoperative assessment and preparation, surgical operation process, postoperative management and traditional Chinese medicine treatment to individualized treatment planning. The study results showed that the treatment plans combining traditional Chinese with Western medicine effectively alleviated cancer pain, reduced the use of opioid drugs, and significantly improved the quality of life and enhanced immune function of the patients. Postoperative follow-up suggested good treatment tolerance among the patients without serious complications. CONCLUSIONS The formulated consensus is comprehensive and can provide reference for clinicians to use DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy. The combined treatment has a high clinical value with a good safety profile for management of cancer pain.
Humans ; Medicine, Chinese Traditional ; Cancer Pain/therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Drug Delivery Systems ; Pain Management/methods* ; China

OBJECTIVES: To investigate the therapeutic mechanism of Qingshen Granules (QSG) in patients with chronic kidney disease (CKD) damp-heat syndrome. METHODS: The regulatory targets of QSG were retrieved and mapped using TCMSP and UniProt. Small RNA sequencing technology was used to screen the target genes of chronic renal failure damp-heat syndrome to construct the "active ingredients-intersection targets-diseases" network, followed by KEGG pathway enrichment analysis and molecular docking of the core targets. Sixty patients with CKD (stage 3-5) presenting with damp-heat syndrome and not undergoing dialysis were randomized equally into two groups for conventional Western medicine treatment (control group) and additional treatment with QSG (observation group) for 8 weeks, with 20 healthy individuals as the normal control group. The expression levels of miR-23b-5p, Nrf2 and HO-1 protein in peripheral blood mononuclear cells (PBMC), renal function indicators (Scr and eGFR), and serum ROS, AOPP and PON-1 levels were compared among the 3 groups after the treatments. RESULTS: Six main active ingredients of QSG were identified, and their key targets included ACTB, JUN, PTEN, ESR1, GSK3B, PPARG, PIK3CA, APP, PIK3R1, and BECN1. MiR-23b-5p expression was significantly up-regulated in CKD damp-heat syndrome, in which the Nrf2 pathway abnormality played an important pathogenic role. Molecular docking results suggested good binding activity of the core targets with the active ingredients of QSG, and NFE2L2 had the strongest binding with luteolin. In patients with CKD damp-heat syndrome, QSG treatment significantly decreased serum Scr, ROS and AOPP levels, obviously improved eGFR, and increased serum PON-1 levels, expression levels of Nrf2 and HO-1 proteins in PBMCs, and the expression level of miR-23b-5p. CONCLUSIONS QSG can improve the renal function in patients with CKD damp-heat syndrome possibly by up-regulating miR-23b expression, activating the Nrf2 antioxidant pathway, and reducing oxidative stress levels.
Humans ; Renal Insufficiency, Chronic/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; NF-E2-Related Factor 2/metabolism* ; MicroRNAs/genetics* ; Signal Transduction ; Male ; Medicine, Chinese Traditional ; Adult ; Middle Aged ; Female

Immune suppression in the tumor microenvironment (TME) is closely related to abnormal glycolysis. Tumor cells gain metabolic advantages and suppress immune responses through the "Warburg effect". Traditional Chinese medicine (TCM) has been shown to regulate key glycolysis enzymes (such as HK2 and PKM2), metabolic signaling pathways (such as PI3K/AKT/mTOR, HIF-1α) and non-coding RNAs at multiple targets, thus synergistically inhibiting lactate accumulation, improving vascular abnormalities, and relieving metabolic inhibition of immune cells. Studies have shown that TCM monomers and formulas can promote immune cell infiltration and functions, improve metabolic microenvironment, and with the assistance by the nano-delivery system, enhance the precision of treatment. However, the dynamic mechanism of the interaction between TCM-regulated glycolysis and TME has not been fully elucidated, for which single-cell sequencing and other technologies provide important technical support to facilitate in-depth analysis and clinical translational research. Future studies should be focused on the synergistic strategy of "metabolic reprogramming-immune activation" to provide new insights into the mechanisms of tumor immunotherapy.
Humans ; Tumor Microenvironment/immunology* ; Glycolysis/drug effects* ; Neoplasms/drug therapy* ; Medicine, Chinese Traditional ; Signal Transduction ; Drugs, Chinese Herbal/pharmacology*

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; 23(2): 138-145.
Female ; Humans ; Male ; Bayes Theorem ; Disease Progression ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Pulmonary Fibrosis/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Research Design ; Adaptive Clinical Trials as Topic

Cynanchum atratum Bunge (C. atratum) and Cynanchum versicolor Bunge (C. versicolor) are two related species that have been used as "Baiwei" (Cynanchi Atrati Radix Et Rhizoma) in traditional medicine in China and other Asian countries for a long time. However, to date, no comprehensive review of C. atratum and C. versicolor has been published. This review provides a comprehensive summary on the botany, phytochemistry, traditional uses and pharmacology of Baiwei; The authors focus especially on the revision of errors in previous articles and reviews, updating information and providing a comparison of C. atratum and C. versicolor. Furthermore, current research reveals significant disparities in the chemical composition and pharmacological effects between C. atratum and C. versicolor. Up to November 2023, 178 compounds have been isolated from C. atratum and C. versicolor, including C₂₁ steroids, acetophenones, alkaloids and volatile oils. These compounds and extracts have been proven to exhibit significant pharmacological activities, including anti-inflammatory, anti-tumor, anti-virus, anti-fungal, memory-enhancing and anti-pyretic action, immune modulatory effects, reducing blood lipid, inhibition of melanin production, and anti-parasitic effects. Therefore, this review presents new insights into these two herbs used as "Baiwei" and further study is warranted to enhance their clinical application. Please cite this article as: Xie W, Liu XY, Li X, Jin YS. Cynanchum atratum Bunge and Cynanchum versicolor Bunge for Baiwei: An updated review of their botany, phytochemistry, traditional uses and pharmacological activities. J Integr Med. 2025; 23(3): 230-255.
Cynanchum/chemistry* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Phytochemicals/pharmacology* ; Animals ; Medicine, Chinese Traditional ; Plant Extracts/chemistry*