Pub Date : 2025-12-01DOI: 10.1016/j.cjnm.2025.100009
Jialu Ma , Zhenhua Ni , Qingge Chen , Fuqi Ma , Cuiting Shan , Yue Wu , Wenguan Li , Xiayi Miao , Xiongbiao Wang , Yuhua Lin
Traditional Chinese medicine (TCM) establishes that the kidney serves vital systemic functions and its deficiency represents a fundamental factor influencing various diseases, including asthma. The kidney-tonifying method represents a widely implemented clinical approach in TCM to address kidney deficiency. This study hypothesized that bone marrow mesenchymal stem cells (BMSCs) function as key contributors to the kidney-tonifying method. An ovalbumin (OVA)-induced asthma mouse model received treatment with the traditional kidney-tonifying formula, Qi-Xian decoction (QXD). QXD demonstrated significant therapeutic efficacy, enhanced BMSC proliferation in mouse bone marrow, and facilitated their migration to lung tissues. Inhibition of the CXCL12/CXCR4 axis diminished the QXD-induced migration of endogenous BMSCs and reduced QXD’s efficacy in asthma treatment. QXD-containing serum enhanced BMSC proliferation and promoted CXCL12-induced BMSC migration in vitro. These findings indicate that endogenous BMSCs may serve as a crucial mediator in the therapeutic effects of the kidney-tonifying method. Furthermore, the mild and sustained stimulation of production and enhanced homing of endogenous BMSCs presents a potential novel approach for effective asthma treatment.
{"title":"Exploring the kidney-tonifying effect of Qi-Xian decoction for asthma treatment by modulating the proliferation and migration of endogenous BMSCs","authors":"Jialu Ma , Zhenhua Ni , Qingge Chen , Fuqi Ma , Cuiting Shan , Yue Wu , Wenguan Li , Xiayi Miao , Xiongbiao Wang , Yuhua Lin","doi":"10.1016/j.cjnm.2025.100009","DOIUrl":"10.1016/j.cjnm.2025.100009","url":null,"abstract":"<div><div>Traditional Chinese medicine (TCM) establishes that the kidney serves vital systemic functions and its deficiency represents a fundamental factor influencing various diseases, including asthma. The kidney-tonifying method represents a widely implemented clinical approach in TCM to address kidney deficiency. This study hypothesized that bone marrow mesenchymal stem cells (BMSCs) function as key contributors to the kidney-tonifying method. An ovalbumin (OVA)-induced asthma mouse model received treatment with the traditional kidney-tonifying formula, Qi-Xian decoction (QXD). QXD demonstrated significant therapeutic efficacy, enhanced BMSC proliferation in mouse bone marrow, and facilitated their migration to lung tissues. Inhibition of the CXCL12/CXCR4 axis diminished the QXD-induced migration of endogenous BMSCs and reduced QXD’s efficacy in asthma treatment. QXD-containing serum enhanced BMSC proliferation and promoted CXCL12-induced BMSC migration <em>in vitro</em>. These findings indicate that endogenous BMSCs may serve as a crucial mediator in the therapeutic effects of the kidney-tonifying method. Furthermore, the mild and sustained stimulation of production and enhanced homing of endogenous BMSCs presents a potential novel approach for effective asthma treatment.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 12","pages":"Article 100009"},"PeriodicalIF":4.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.cjnm.2025.100016
Zhiliang Xiao , Sicong Jiang , Shengqiang Fu , Xiaohai Zhang , Xueliang Qi , Changhao Li
Epimedium Herba (EH) demonstrates significant therapeutic potential against prostate cancer (PC), though its mechanisms of action remain incompletely understood. This study investigates the pharmacological mechanisms of EH in treating PC through network pharmacology analysis and experimental validation. Active components and potential targets of EH were identified using network pharmacology from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The STRING database facilitated the construction of a protein-protein interaction (PPI) network for shared targets and the identification of core anti-PC targets. Messenger ribonucleic acid (mRNA) and protein expression of core target genes in normal prostate and PC tissues, along with their correlation to overall PC survival, were analyzed using The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the potential targets. Molecular docking of quercetin with key targets (TP53, TNF, heat shock protein 90 alpha family class A member 1 (HSP90AA1), AKT1, CASP3, and ESR1) was conducted, with results visualized using PyMOL. In vitro experiments validated the network pharmacology predictions. Twenty-three active ingredients of EH were identified, and the intersection of potential targets with PC targets yielded 183 potential targets. PPI network analysis revealed six key genes: targets (TP53), TNF, HSP90AA1, AKT1, CASP3, and ESR1. GO enrichment analysis identified 2369 biological processes (BP), 77 cellular components (CC), and 215 molecular functions (MF). KEGG pathway enrichment analysis demonstrated that EH's anti-cancer effects were mediated through interleukin-17 (IL-17), TNF, phosphatidylinositol 3-kinase (PI3K)-AKT, apoptosis, p53, HIF-1, mitogen-activated protein kinase (MAPK), nuclear factor κB (NF-κB), and EGFR tyrosine kinase inhibitor resistance pathways. Core target validation confirmed consistency with the study’s findings. Molecular docking indicated stable binding between the six core targets and quercetin. In vitro experiments confirmed quercetin’s inhibition of proliferation and induction of apoptosis in ACT-1 cells. This investigation identifies potential therapeutic targets for PC through network pharmacology and experimental validation.
{"title":"Unveiling the therapeutic mechanism of Epimedium Herba on prostate cancer through network pharmacology and experimental validation","authors":"Zhiliang Xiao , Sicong Jiang , Shengqiang Fu , Xiaohai Zhang , Xueliang Qi , Changhao Li","doi":"10.1016/j.cjnm.2025.100016","DOIUrl":"10.1016/j.cjnm.2025.100016","url":null,"abstract":"<div><div>Epimedium Herba (EH) demonstrates significant therapeutic potential against prostate cancer (PC), though its mechanisms of action remain incompletely understood. This study investigates the pharmacological mechanisms of EH in treating PC through network pharmacology analysis and experimental validation. Active components and potential targets of EH were identified using network pharmacology from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The STRING database facilitated the construction of a protein-protein interaction (PPI) network for shared targets and the identification of core anti-PC targets. Messenger ribonucleic acid (mRNA) and protein expression of core target genes in normal prostate and PC tissues, along with their correlation to overall PC survival, were analyzed using The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the potential targets. Molecular docking of quercetin with key targets (TP53, TNF, heat shock protein 90 alpha family class A member 1 (HSP90AA1), AKT1, CASP3, and ESR1) was conducted, with results visualized using PyMOL. <em>In vitro</em> experiments validated the network pharmacology predictions. Twenty-three active ingredients of EH were identified, and the intersection of potential targets with PC targets yielded 183 potential targets. PPI network analysis revealed six key genes: targets (<em>TP53</em>), <em>TNF, HSP90AA1, AKT1, CASP3</em>, and <em>ESR1</em>. GO enrichment analysis identified 2369 biological processes (BP), 77 cellular components (CC), and 215 molecular functions (MF). KEGG pathway enrichment analysis demonstrated that EH's anti-cancer effects were mediated through interleukin-17 (IL-17), TNF, phosphatidylinositol 3-kinase (PI3K)-AKT, apoptosis, p53, HIF-1, mitogen-activated protein kinase (MAPK), nuclear factor <em>κ</em>B (NF-<em>κ</em>B), and EGFR tyrosine kinase inhibitor resistance pathways. Core target validation confirmed consistency with the study’s findings. Molecular docking indicated stable binding between the six core targets and quercetin. <em>In vitro</em> experiments confirmed quercetin’s inhibition of proliferation and induction of apoptosis in ACT-1 cells. This investigation identifies potential therapeutic targets for PC through network pharmacology and experimental validation.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 12","pages":"Article 100016"},"PeriodicalIF":4.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60902-2
Xiao Yuan , Xiaobo Yang , Qiyuan Pan , Cheng Luo , Xin Luan , Hao Zhang
Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines’ bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
{"title":"Artificial intelligence in natural products research","authors":"Xiao Yuan , Xiaobo Yang , Qiyuan Pan , Cheng Luo , Xin Luan , Hao Zhang","doi":"10.1016/S1875-5364(25)60902-2","DOIUrl":"10.1016/S1875-5364(25)60902-2","url":null,"abstract":"<div><div>Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines’ bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1342-1357"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60946-0
Junxi Liu , Shan Chang , Qingtian Deng , Yulian Ding , Yi Pan
Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
{"title":"Advances in small molecule representations and AI-driven drug research: bridging the gap between theory and application","authors":"Junxi Liu , Shan Chang , Qingtian Deng , Yulian Ding , Yi Pan","doi":"10.1016/S1875-5364(25)60946-0","DOIUrl":"10.1016/S1875-5364(25)60946-0","url":null,"abstract":"<div><div>Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1391-1408"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60980-0
Chengyuan Yue , Baiyu Chen , Long Chen , Le Xiong , Changda Gong , Ze Wang , Guixia Liu , Weihua Li , Rui Wang , Yun Tang
Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
{"title":"KG-CNNDTI: a knowledge graph-enhanced prediction model for drug-target interactions and application in virtual screening of natural products against Alzheimer’s disease","authors":"Chengyuan Yue , Baiyu Chen , Long Chen , Le Xiong , Changda Gong , Ze Wang , Guixia Liu , Weihua Li , Rui Wang , Yun Tang","doi":"10.1016/S1875-5364(25)60980-0","DOIUrl":"10.1016/S1875-5364(25)60980-0","url":null,"abstract":"<div><div>Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph <em>via</em> the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated <em>in vitro</em> assays.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1283-1292"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60903-4
Hongyu Chen , Ruotian Tang , Mei Hong , Jing Zhao , Dong Lu , Xin Luan , Guangyong Zheng , Weidong Zhang
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.
{"title":"Applications of artificial intelligence in the research of molecular mechanisms of traditional Chinese medicine formulas","authors":"Hongyu Chen , Ruotian Tang , Mei Hong , Jing Zhao , Dong Lu , Xin Luan , Guangyong Zheng , Weidong Zhang","doi":"10.1016/S1875-5364(25)60903-4","DOIUrl":"10.1016/S1875-5364(25)60903-4","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1329-1341"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60986-1
Ziyi Wang , Tingyu Zhang , Boyang Wang , Shao Li
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.
{"title":"TCM network pharmacology: new perspective integrating network target with artificial intelligence and multi-modal multi-omics technologies","authors":"Ziyi Wang , Tingyu Zhang , Boyang Wang , Shao Li","doi":"10.1016/S1875-5364(25)60986-1","DOIUrl":"10.1016/S1875-5364(25)60986-1","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1425-1434"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60945-9
Sishu Li , Jing Fan , Haiyang He , Ruifeng Zhou , Jun Liao
The accurate prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties represents a crucial step in early drug development for reducing failure risk. Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks. This research proposes molecular properties prediction with parallel-view and collaborative learning (MolP-PC), a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints (MFs), 2D molecular graphs, and 3D geometric representations, incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions. Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks, with its multi-task learning (MTL) mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks. Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization. A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life (T0.5) and clearance (CL), indicating its practical utility in drug modeling. However, the model exhibits a tendency to underestimate volume of distribution (VD), indicating potential for improvement in analyzing compounds with high tissue distribution. This study presents an efficient and interpretable approach for ADMET property prediction, establishing a novel framework for molecular optimization and risk assessment in drug development.
{"title":"MolP-PC: a multi-view fusion and multi-task learning framework for drug ADMET property prediction","authors":"Sishu Li , Jing Fan , Haiyang He , Ruifeng Zhou , Jun Liao","doi":"10.1016/S1875-5364(25)60945-9","DOIUrl":"10.1016/S1875-5364(25)60945-9","url":null,"abstract":"<div><div>The accurate prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties represents a crucial step in early drug development for reducing failure risk. Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks. This research proposes molecular properties prediction with parallel-view and collaborative learning (MolP-PC), a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints (MFs), 2D molecular graphs, and 3D geometric representations, incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions. Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks, with its multi-task learning (MTL) mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks. Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization. A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life (T0.5) and clearance (CL), indicating its practical utility in drug modeling. However, the model exhibits a tendency to underestimate volume of distribution (VD), indicating potential for improvement in analyzing compounds with high tissue distribution. This study presents an efficient and interpretable approach for ADMET property prediction, establishing a novel framework for molecular optimization and risk assessment in drug development.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1293-1300"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/S1875-5364(25)60941-1
Xin Shao , Yu Chen , Jinlu Zhang , Xuting Zhang , Yizheng Dai , Xin Peng , Xiaohui Fan
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.
{"title":"Advancing network pharmacology with artificial intelligence: the next paradigm in traditional Chinese medicine","authors":"Xin Shao , Yu Chen , Jinlu Zhang , Xuting Zhang , Yizheng Dai , Xin Peng , Xiaohui Fan","doi":"10.1016/S1875-5364(25)60941-1","DOIUrl":"10.1016/S1875-5364(25)60941-1","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10002,"journal":{"name":"Chinese Journal of Natural Medicines","volume":"23 11","pages":"Pages 1358-1376"},"PeriodicalIF":4.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号