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Network pharmacology plays a pivotal role in systems biology, bridging the gap between traditional Chinese medicine (TCM) theory and contemporary pharmacological research. Network pharmacology enables researchers to construct multilayered networks that systematically elucidate TCM’s multi-component, multi-target mechanisms of action. This review summarizes key databases commonly used in network pharmacology, including those focused on herbs, components, diseases, and dedicated platforms for network pharmacology analysis. Additionally, we explore the growing use of network pharmacology in TCM, citing literature from Web of Science, PubMed, and CNKI over the past two decades with keywords like “network pharmacology”, “TCM network pharmacology”, and “herb network pharmacology”. The application of network pharmacology in TCM is widespread, covering areas such as identifying the material basis of TCM efficacy, unraveling mechanisms of action, and evaluating toxicity, safety, and novel drug development. However, challenges remain, such as the lack of standardized data collection across databases and insufficient consideration of processed herbs in research. Questions also persist regarding the reliability of study outcomes. This review aims to offer valuable insights and reference points to guide future research in precision TCM network pharmacology.

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules’ interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases—such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases—are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases. Graphical Abstract

Cancer is a medical problem that has been difficult to overcome on a global scale. Owing to the sharing of single or multiple genes or regulatory modules, cancer treatment often faces severe challenges. The core of network pharmacology lies in constructing human disease gene regulation networks and multi-pharmacology network. With the continuous updating and iteration of new technologies, it is helpful for us to systematically understand the occurrence and development mechanism behind complex diseases and elucidate the pharmacological mechanisms from the perspective of biological network balance. This review aims to clarify the application of network pharmacology in exploring the pharmacological treatment mechanism of natural products, drug repositioning, and new technology combinations in the context of complex pathogenesis of cancer, so as to help realize the full potential of network pharmacology. Additionally, we discuss the future development of network pharmacology to guide clinical diagnosis and treatment. Graphical abstract

Plant secondary metabolites possess significant antioxidant and anti-inflammatory properties, but their complex polypharmacological mechanisms remain poorly understood. Network pharmacology has emerged as a powerful systems-level approach for investigating multi-target interactions of natural products. This review systematically analyzes network pharmacology applications in elucidating the antioxidant and anti-inflammatory mechanisms of plant metabolites, evaluating concordance between computational predictions and experimental validation. A comprehensive literature search was conducted across major databases (2015–2025), focusing on network pharmacology studies with experimental validation. Analysis revealed remarkable convergence toward common molecular mechanisms, despite diverse chemical structures. For antioxidant activities, the Nrf2/KEAP1/ARE pathway emerged as the most frequently validated mechanism, along with PI3K/AKT, MAPK, and NF-κB signaling. Anti-inflammatory mechanisms consistently involved NF-κB, MAPK, and PI3K/AKT pathways. Key targets, including AKT1, TNF-α, COX-2, NFKB1, and RELA, were repeatedly identified. Flavonoids, phenolic acids, and terpenoids dominated as bioactive compounds. Molecular docking studies supported predicted interactions, with experimental validation showing good concordance for pathway modulation and cytokine regulation. Network pharmacology provides a valuable framework for investigating the complex bioactivities of plant metabolites. The convergence toward common regulatory hubs suggests that natural compounds achieve protective effects by modulating central nodes that integrate redox balance and inflammatory responses. Despite limitations, including database dependency, integrating network pharmacology with experimental validation accelerates mechanistic understanding in natural-product drug discovery.

Rosacea is significantly associated with dementia, particularly Alzheimer’s disease (AD). However, the common underlying molecular mechanism connecting these two diseases remains limited. This study aimed to reveal the common molecular regulatory networks and identify the potential therapeutic drugs for rosacea and AD. There were 747 overlapped DEGs (ol-DEGs) that were detected in AD and rosacea, enriched in inflammation-, metabolism-, and apoptosis-related pathways. Using the TF regulatory network analysis, 37 common TFs and target genes were identified as hub genes. They were used to predict the therapeutic drugs for rosacea and AD using the DGIdb/CMap database. Among the 113 predicted drugs, melatonin (MLT) was co-associated with both RORA and IFN-γ in AD and rosacea. Subsequently, network pharmacology analysis identified 19 pharmacological targets of MLT and demonstrated that MLT could help in treating AD/rosacea partly by modulating inflammatory and vascular signaling pathways. Finally, we verified the therapeutic role and mechanism of MLT on rosacea in vivo and in vitro . We found that MLT treatment significantly improved rosacea-like skin lesion by reducing keratinocyte-mediated inflammatory cytokine secretion and repressing the migration of HUVEC cells. In conclusion, this study contributes to common pathologies shared by rosacea and AD and identified MLT as an effective treatment strategy for rosacea and AD via regulating inflammation and angiogenesis.

Background Icariin (ICA), an active ingredient extracted from Epimedium species, has shown promising results in the treatment of Alzheimer's disease (AD), although its potential therapeutic mechanism remains largely unknown. This study aimed to investigate the therapeutic effects and the underlying mechanisms of ICA on AD by an integrated analysis of gut microbiota, metabolomics, and network pharmacology (NP). Methods The cognitive impairment of mice was measured using the Morris Water Maze test and the pathological changes were assessed using hematoxylin and eosin staining. 16S rRNA sequencing and multi-metabolomics were performed to analyze the alterations in the gut microbiota and fecal/serum metabolism. Meanwhile, NP was used to determine the putative molecular regulation mechanism of ICA in AD treatment. Results Our results revealed that ICA intervention significantly improved cognitive dysfunction in APP/PS1 mice and typical AD pathologies in the hippocampus of the APP/PS1 mice. Moreover, the gut microbiota analysis showed that ICA administration reversed AD-induced gut microbiota dysbiosis in APP/PS1 mice by elevating the abundance of Akkermansia and reducing the abundance of Alistipe . Furthermore, the metabolomic analysis revealed that ICA reversed the AD-induced metabolic disorder via regulating the glycerophospholipid and sphingolipid metabolism, and correlation analysis revealed that glycerophospholipid and sphingolipid were closely related to Alistipe and Akkermansia . Moreover, NP indicated that ICA might regulate the sphingolipid signaling pathway via the PRKCA/TNF/TP53/AKT1/RELA/NFKB1 axis for the treatment of AD. Conclusion These findings indicated that ICA may serve as a promising therapeutic approach for AD and that the ICA-mediated protective effects were associated with the amelioration of microbiota disturbance and metabolic disorder. Graphical Abstract

Quercetin is a flavonoid with notable pharmacological effects and promising therapeutic potential. Quercetin plays a significant role in neuroinflammation, which helps reduce Alzheimer's disease (AD) severity. Quercetin (Q) and quercetin 3-O-glucuronide (Q3OG) are some of the most potent antioxidants available from natural sources. However, the natural form of quercetin converted into Q3OG when reacted with intestinal microbes. The study aims to ensure the therapeutic potential of Q and Q3OG. In this study, potential molecular targets of Q and Q3OG were first identified using the Swiss Target Prediction platform and pathogenic targets of AD were identified using the DisGeNET database. Followed by compound and disease target overlapping, 77 targets were placed in that AKT1, EGFR, MMP9, TNF, PTGS2, MMP2, IGF1R, MCL1, MET and PARP1 was the top-ranked target, which was estimated by CytoHubba plug-in. The Molecular docking was performed for Q and Q3OG towards the PDB:1UNQ target. The binding score of Q and Q3OG was − 6.2 kcal/mol and − 6.58 kcal/mol respectively. Molecular dynamics simulation was conducted for Q and Q3OG towards the PDB:1UNQ target at 200 ns. This study's results help identify the multiple target sites for the bioactive compounds. Thus, synthesizing new chemical entity-based quercetin on structural modification may aid in eradicating AD complications.

Integrating network pharmacological analysis and bioinformatic techniques, this study systematically investigated the molecular mechanisms of six medicinal food homologous plants (Astragalus membranaceus, Ganoderma lucidum, Dioscorea opposite, Curcuma longa, Glycyrrhiza uralensis, and Pueraria lobata) against colorectal cancer. Through screening the TCMSP database, 303 active compounds and 453 drug targets were identified. By integrating differential expression gene analysis with WGCNA on the GSE41258 dataset from the GEO database, 49 potential therapeutic targets were identified. GO and KEGG enrichment analyses demonstrated that these targets are primarily involved in drug response, fatty acid metabolism, and key cancer-related pathways. Cross-validation using three machine learning algorithms—LASSO regression, SVM-RFE, and Random Forest—pinpointed four critical target genes: CA1, CCND1, CXCL2, and EIF6. Further, CIBERSORT immune infiltration analysis revealed strong associations between these core genes and the tumor immune microenvironment in colorectal cancer patients, notably in modulating M0 macrophage infiltration and mast cell activity. Molecular docking analyses confirmed robust binding interactions between active compounds and core target proteins. This study systematically elucidated the molecular mechanisms of six medicinal food homologous plants against colorectal cancer, providing scientific evidence for their rational clinical application.

Background The current study aimed to evaluate the anti-inflammatory, anti-oxidant, and pronounced gastro-protective activities of β- Citronellol using in vitro , in vivo assays and in silico approaches. Methods In vitro assays, denaturation of bovine serum albumin, egg protein, and human Red Blood Cells (RBCs) membrane stabilization were performed, using Piroxicam as standard. For in vivo assessment, Histamine (0.1 ml from 1% w/v) and Formaldehyde (0.1 ml from 2% v/v) were used to mediate inflammation. In silico molecular docking and network pharmacology were employed to probe the possible target genes mediating gastroprotective effect of β-Citronellol at 25, 50, and 100 mg/kg, using indomethacin-induced (25 mg/kg i.p) gastric ulcer in rats. Moreover, Gastric tissues were evaluated for morphological, histopathological, and bio-chemical analysis of PGE 2, COX-I, COX-II, 5-LOX, eNOS, ICAM-1, oxygen-free radical scavengers (SOD, CAT), and oxidative stress marker (MDA). Results β - Citronellol prevented denaturation of proteins and RBCs membrane stabilization with maximum effect observed at 6,400 µg/mL. Citronellol decreased rat’s paw edema. Network pharmacology and docking studies revealed gastro-protective potential of Citronellol possibly mediated through arachidonic acid pathways by targeting COX-I, COX-II, PGE 2 , and 5-LOX. Citronellol reduced the ulcer indices, and histopathological changes. Further, β - Citronellol (50 and 100 mg/kg) increased gastric PGE 2, COX-1, and eNOS; while suppressing COX-2, 5-LOX and ICAM-1. Citronellol markedly enhanced the oxidative balance in isolated rat stomach tissues. Conclusions The anti-inflammatory, anti-oxidant, and gastro-protective effects of β - Citronellol against indomethacin-induced gastric ulcer model in rats through mediating COX-I, COX-II, PGE 2, 5-LOX, eNOS, and ICAM-1 inflammatory markers. Graphical abstract

This study explores novel therapeutic avenues for diabetes, a global health concern marked by elevated blood glucose levels. We investigated the anti‐diabetic potential of Gymnema Sylvestre's bioactive compounds, including Gymnemic acid I, Stigmasterol, Deacylgymnemic acid, Beta‐Amyrin acetate, Longispinogenin, Gymnemic acid II, Gymnemic acid, Gymnemic acid X, Gymnemaside VI, Phytic acid and Gymnemic acid X. Employing network pharmacology, molecular docking and molecular dynamics (MD), we elucidated the potential mechanism of action. SwissTargetPrediction identified targets for bioactive constituents, while DisGeNET provided diabetes‐related targets. A GeneVenn diagram revealed 397 common potential targets for diabetes management. The protein–protein interaction network, constructed via the STRING database, underwent topological analysis in Cytoscape, identifying AKT1, SRC, TNF, PPARG and IL1B as top targets. Gene ontology analysis using FunRich identified crucial roles of screened targets in integrin family cell surface interactions and glypican pathways for diabetes management. Molecular interactions and binding affinities with the top target, AKT1, were assessed, with Gymnemic acid I displaying the least binding energy (−9.813) with H‐ and non‐H‐bond interactions. Molecular dynamics simulations provided insights into the distinct behaviours of Gymnemic acid I within the protein complex. In conclusion, our study elucidates the potential anti‐diabetic mechanism of Gymnemic acid I, underscoring the need for further in vitro, in vivo and clinical studies to validate our findings.