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General expert consensus on the application of network pharmacology in the research and development of new traditional Chinese medicine drugs

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Chinese Journal of Natural Medicines 2025年第2期23卷 129-142页

作者： Shao Li Wei Xiao Institute of TCM-X/MOE Key Laboratory of Bioinformatics Bioinformatics DivisionBNRist/Department of AutomationTsinghua UniversityBeijing 100084China State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture Jiangsu Kanion Pharmaceutical Co.Ltd.Lianyungang 222047China

The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain ***,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug *** pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research *** emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM *** approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM *** is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug *** goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.

关键词： Network pharmacology Research and development of new traditional Chinese medicine drugs Expert consensus

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A review of recent artificial intelligence for traditional medicine

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Journal of Traditional and Complementary Medicine 2025年第3期15卷 215-228页

作者： Hou, Chengbin Gao, Yanzhuo Lin, Xinyu Wu, Jinchao Li, Ning Lv, Hairong Chu, William Cheng-Chung School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fujian 350109 China Maynooth International Engineering College Fuzhou University Fuzhou 350108 China MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China

Traditional Medicine (TM) has played a crucial role in global healthcare due to its long history and holistic approach. Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such as data mining, pattern recognition, and decision-making. The integration of Artificial Intelligence for Traditional Medicine (AITM) presents a promising frontier in advancing medicine and healthcare. In this review, we explore AITM from two perspectives: recent AI techniques and TM applications. Specifically, we investigate how Machine Learning, Deep Learning, and Large Language Models are applied to TM, covering applications such as diagnosis (before, during, after) and research (drug research, structured knowledge, data analysis). By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. We anticipate this review can bridge the gap between AI and TM communities. And the goal is to foster collaboration and innovation between both communities, enabling them to exploit the state-of-the-art AI techniques to advance TM diagnosis and research, ultimately contributing to the enhancement of human health. © 2025 Center for Food and Biomolecules, National Taiwan University

关键词： AI for health AI for medicine Artificial intelligence Deep learning Large language models Machine learning Traditional medicine

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Capsaicin(CAP)exerts a protective effect against ethanol-induced oxidative gastric mucosal injury by modulating the chemokine receptor 4(CCR4)/Src/p47phox signaling pathway both in vitro and in vivo

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Chinese Journal of Natural Medicines 2025年第2期23卷 191-202页

作者： Zhiru Yang Haolin Guo Pengfei Zhang Kairui Liu Junli Ba Xue Bai Shiti Shama Bo Zhang Xiaoning Gao Jun Kang School of Life Sciences Tianjin UniversityTianjin 300072China School of Traditional Chinese Medicine Beijing University of Chinese MedicineBeijing 102499China Tianjin Pharmaceutical and Cosmetic Evaluation and Inspection Center Tianjin 300191China Institute for TCM-X MOE Key Laboratory of BioinformaticsBioinformatics DivisionBNRistDepartment of AutomationTsinghua UniversityBeijing 100084China Institute of Biomedical Research Henan Academy of SciencesZhengzhou 450046China

Ethanol(EtOH)is a common trigger for gastric mucosal diseases,and mitigating oxidative stress is essential for attenuating gastric mucosal ***(CAP)has been identified as a potential agent to counteract oxidative damage in the gastric mucosa;however,its precise mechanism remains *** study demonstrates that CAP alleviates EtOH-induced gastric mucosal injuries through two primary pathways:by suppressing the chemokine receptor 4(CCR4)/Src/p47phox axis,thereby reducing oxidative stress,and by inhibiting the phosphorylation and nuclear translocation of nuclear factor-κB p65(NF-κB)p65,resulting in diminished inflammatory *** findings elucidate the mechanistic pathways of CAP and provide a theoretical foundation for its potential therapeutic application in the treatment of gastric mucosal injuries.

关键词： Capsaicin Gastric mucosal damage Chemokine signaling NF-κB p65

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SpaGRA:Graph augmentation facilitates domain identification for spatially resolved transcriptomics

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Journal of Genetics and Genomics 2025年第1期52卷 93-104页

作者： Xue Sun Wei Zhang Wenrui Li Na Yu Daoliang Zhang Qi Zou Qiongye Dong Xianglin Zhang Zhiping Liu Zhiyuan Yuan Rui Gao Center of Intelligent Medicine School of Control Science and EngineeringShandong UniversityJinanShandong 250061China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Institute of Precision Medicine Peking University Shenzhen HospitalShenzhenGuangdong 518036China Department of Clinical Laboratory The Second HospitalCheeloo College of MedicineShandong UniversityJinanShandong 250033China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and InnovationShanghai Pudong HospitalFudan University Pudong Medical CenterFudan UniversityShanghai 200433China

Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various ***-based geometric deep learning has gained widespread adoption for spatial domain identification ***,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain *** tackle this challenge,we propose a novel method,SpaGRA(https://***/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph *** uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive ***,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random *** results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different *** SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD ***,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

关键词： Spatial domain identification Spatially resolved transcriptomics Multi-head graph attention networks Graph augmentation Geometric contrastive learning

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Dendrobium officinale extract exerts antioxidant effects against skin photoaging through MMP9-TNF pathway

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Traditional Medicine Research 2025年第3期10卷 32-41页

作者： Bei-Bei Dong Yun Wang Han Zhao Wu-Yan Guo Bo Zhang College of Pharmacy Nankai UniversityTianjin 300350China Hepatobiliary Pancreatic Center Xuzhou Central HospitalXuzhou 221009China Clinic for Kidney and Hypertensive Diseases Hanover Medical SchoolHanover 30625Germany Tianjin JiAnKang Bio&TCM-technology Development Co. Ltd.Tianjin 300457China Institute for TCM-X MOE Key Laboratory of BioinformaticsBioinformatics DivisionBNRistDepartment of AutomationTsinghua UniversityBeijing 100084China

Background:Skin photoaging is a physiological or pathological process caused by multiple *** anti-skin photoaging drugs is a hot topic in cosmetology research *** purpose of this study was to explore the therapeutic effect of Dendrobium officinale(***)on skin ***:The ingredients of *** were detected by UHPLC-Q-TOF/*** targets of *** were screened by Swiss Target Prediction ***,NCBI,and OMIM databases were utilized to find out the targets associated with skin *** targets of *** and skin photoaging were obtained by Venn *** ingredient-disease target network and protein-protein interaction network were constructed by using the STRING database and Cytoscape *** key compounds and hub genes were obtained by analyzing *** DAVID database was applied for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the overlapping *** Vina software was used to simulate molecular docking and the results were visualized using ***,the skin photoaging models of cells and mice were established to validate the results predicted by network ***:In ***,a total of 59 compounds and 595 targets were detected,of which 59 proteins were intersectional with skin photoaging *** top 10 active ingredients(Dendrophenol,Herbacetin,Lyoniresinol,Trans-ferulaldehyde,Naringenin,and so on)and 8 hub genes(AKT1,TNF,VEGFA,MAPK3,CASP3,MMP9,CTNNB1,and EGFR)were *** the key active compounds could bind well with core protein targets(binding energy<-5 kcal/mol).The potential therapeutic targets were related to the response to reactive oxygen species,collagen catabolic process,extracellular matrix organization,and apoptotic process,*** also found that *** could enhance the cell viability and activity of anti-oxidases,reduce reactive oxygen species and MMP9 levels,and stable mitoc

关键词： Dendrobium officinale skin photoaging matrix metalloproteinase anti-oxidative stress

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Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data

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Journal of Genetics and Genomics 2022年第9期49卷 891-899页

作者： Yanhong Wu Qifan Hu Shicheng Wang Changyi Liu Yiran Shan Wenbo Guo Rui Jiang Xiaowo Wang Jin Gu MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing 100084China

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)*** with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity *** mathematically find the optimal expression patterns that can maximize the proposed scoring *** comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation ***,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

关键词： Single-cell RNA-sequencing Feature selection Spatially resolved transcriptomic data Regional patterns Graphical models

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VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder

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Genomics, Proteomics & bioinformatics 2018年第5期16卷 320-331页

作者： Dongfang Wang Jin Gu MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics Division & Center for Synthetic and Systems BiologyDepartment of Automation Tsinghua University

Single-cell RNA sequencing(scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell subpopulations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data(VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.

关键词： Single cell RNA sequencing Deep variational autoencoder Dimension reduction Visualization Dropout

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TCM network pharmacology:A new trend towards combining computational,experimental and clinical approaches

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Chinese Journal of Natural Medicines 2021年第1期19卷 1-11页

作者： WANG Xin WANG Zi-Yi ZHENG Jia-Hui LI Shao Institute for TCM-X MOE Key Laboratory of Bioinformatics/Bioinformatics DivisionBNRISTDepartment of AutomationTsinghua UniversityBeijing 100084China

Traditional Chinese medicine(TCM)is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of *** holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and *** gave birth to a new method featuring holistic and systematic"network target",a core theory and method of network *** is also an important research object of network *** network pharmacology,which aims to understand the network-based biological basis of complex diseases,TCM syndromes and herb treatments,plays a critical role in the origin and development process of network *** review introduces new progresses of TCM network pharmacology in recent years,including predicting herb targets,understanding biological foundation of diseases and syndromes,network regulation mechanisms of herbal formulae,and identifying disease and syndrome biomarkers based on biological *** studies show a trend of combining computational,experimental and clinical approaches,which is a promising direction of TCM network pharmacology research in the *** that TCM network pharmacology is still a young research field,it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.

关键词： Network pharmacology Traditional Chinese medicine Network target Computation Experiment Clinical approach

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Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

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Cancer Biology & Medicine 2024年第4期21卷 312-330页

作者： Qian Zhang Mingran Yang Peng Zhang Bowen Wu Xiaosen Wei Shao Li Institute for TCM-X MOE Key Laboratory of BioinformaticsBioinformatics DivisionBNRISTDepartment of AutomationTsinghua UniversityBeijing 100084China

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

关键词： Gastric cancer inflammation-induced tumorigenesis multi-omics artificial intelligence network-based methods

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Biological Mechanism of Traditional Chinese Medicine Formula and Herbs in Treating Diseases from the Perspective of Cold and Hot

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World Journal of Traditional Chinese Medicine 2024年第3期10卷 274-283页

作者： Bo-Yang Wang Pan Chen Peng Zhang Shao Li Bioinformatics Division Department of Automation MOE Key Laboratory of Bioinformatics Institute for TCM-X Tsinghua UniversityBNRist

Cold and Hot syndrome, also known as “ZHENG” in Mandarin, is a fundamental theory in traditional Chinese medicine(TCM) and plays a pivotal role in the differentiation of diseases in TCM. Diseases are treated with varying formulas according to the specific syndrome differentiations in TCM. A way of the principles followed in TCM medical strategy is “cold herbs for hot syndrome, and hot herbs for cold syndrome.”Therefore, from the perspective of cold/hot syndrome, we summarizes the present research regarding the characteristics and mechanisms of cold/hot herbs(including herbs with cool and warm properties) in digestive system diseases, respiratory diseases, and autoimmune diseases,among others. As novel technologies have advanced, various methods, such as those based on network target, machine learning, and deep learning, have emerged to reveal the mechanisms underlying cold/hot syndrome and cold/hot herbs. With the help of these technologies, it has been found that cold and hot herbs, as well as formulae with cold or hot intentions, have similarities and differences in the treatment of these diseases. In conclusion, cold and cool may have stronger antibacterial, antiviral, and anti-infiammatory effects, whereas hot and warm herbs may specifically enhance immune regulation. With the assistance of advancing data algorithms, uncovering the mechanisms of cold/hot herbs may accelerate and provide a new research paradigm for further achieving precision in TCM.

关键词： Cold and hot herbs cold and hot syndrome machine learning multi-omics network target traditional Chinese medicine

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