Network Pharmacology in Food-Medicine Homology: AI-Driven Decoding of Multi-Target Synergy from Molecular Networks to Precision Health

Network Pharmacology in Traditional Chinese Medicine

Homeopathy has long faced challenges in elucidating its mechanisms of action within the frameworks of modern biomedical science, primarily due to its high dilution and individualistic approaches. Recent advances in network pharmacology offer a promising avenue to bridge this gap by emphasizing the interconnectedness of biological systems and the multitarget nature of therapeutic agents. This paradigm aligns conceptually with homeopathy's holistic approach to health and disease. This article explores how network pharmacology can be applied to homeopathic research, particularly for remedies derived from plant, mineral, and animal sources. By leveraging bioinformatics databases, molecular docking tools, and systems biology platforms, potential targets and pathways modulated by homeopathic remedies can be identified. Integration of phytochemical profiling, disease–gene associations, and symptomatology data from materia medica allows the construction of remedy–target–disease networks, providing insights into the systemic effects of homeopathic medicines. We discuss key methodological strategies, such as in silico target prediction, multi-omics integration, and network visualization, as well as the challenges posed by ultra-high dilutions and limited pharmacological data. Future directions emphasize the need for interdisciplinary research, experimental validation, and dedicated homeopathy-specific databases to enhance the utility of network pharmacology in this domain. By aligning the principles of homeopathy with systems-level scientific tools, network pharmacology has the potential to transform our understanding of homeopathic therapeutics. This integrative approach may lead to greater scientific acceptance, improved clinical outcomes, and a renewed role for homeopathy in contemporary health care.

In this study, a combination of network pharmacology, bioinformatics analysis, molecular docking and transcriptomics was used to investigate the active ingredient and potential target of Gelsemium elegans in the treatment of colorectal cancer. Koumine was screened as the active component by targeting PDK1 through network pharmacology and reverse docking. RNA-Seq, enrichment analysis and validation experiment were then further employed to reveal koumine might function in inhibiting Akt/mTOR/HK2 pathway to regulate cell glycolysis and detachment of HK2 from mitochondria and VDAC-1 to activate cell apoptosis both in vitro and in vivo. In the present study, we provide a systematical approach for the identification of effective ingredient and potential target of herbal medicine. Our results have important implication for the intensive study of koumine as novel anticancer agents for colorectal cancer and could be supportive in its further structural modification.

Advancing network pharmacology with artificial intelligence: the next paradigm in traditional Chinese medicine.

Food allergy represents a significant global health concern with considerable socioeconomic impacts. While natural bioactive components offer promising intervention potential, their discovery and mechanistic elucidation through traditional experimental methods remain constrained by high costs, low throughput, and extended research cycles. Computational approaches have emerged as powerful and efficient alternatives in this field, providing increasingly interpretable tools for accelerating research on anti-allergic intervention strategies. This review examines major computational methodologies for food allergy intervention research, including quantitative structure-activity relationship (QSAR) modeling, molecular docking, molecular dynamics (MD) simulations, quantum chemical calculations, network pharmacology, multi-omics integration, and AI technologies. We outline their principles, workflows, applications, advantages, and limitations, with case studies demonstrating their practical utility in both discovery and mechanistic investigation. These methods enable efficient virtual screening, design, and prioritization of anti-allergic candidates from large libraries, providing multi-scale insights from atomic-level interactions to system-level regulatory networks. Beyond accelerating discovery, these approaches support structural optimization, synergy analysis, and early safety evaluation. Future development should emphasize deeper multi-scale integration, enhanced interpretability, improved adaptability to real food systems and processing conditions, and stronger synergy between computation and experiments, thereby promoting the rational design of anti-allergic functional foods, precision nutrition strategies, and industrial translation.

Pharmacological effects and mechanisms of alkamides DDA-E and DDA-Z from Asari Radix et Rhizoma in migraine: Insights from serum pharmacochemistry, network pharmacology, and experimental validation.

The You-gui pill (YGP) is a classical compound used for treating antidiabetic erectile dysfunction (DMED). However, the specific active ingredients responsible for its effects on DMED and their mechanisms remain unclear. In this paper, we used data mining techniques to analyze high-frequency herbs and herb combinations used in Chinese medicine for the treatment of DMED based on existing literature. Using network pharmacology to study the active components and mechanism of action of YGP against DMED, molecular docking was used to analyze the interactions of the active components with major structural proteins, nonstructural proteins, and mutants. Also, the therapeutic effect of YGP on hyperglycemic modelling and its underlying mechanisms were experimentally validated in CCEC cells by analyzing the expression of its relevant target mRNAs. Network pharmacological analysis identified the three core components of YGP as quercetin, kaempferol, and β-sitosterol, and constructed a PPI network map of common targets of YGP and DMED, which included HIF-1α, ALB, Bcl-2, INS, IL-1β, IL-6, TNF-α, CASP3, and TP53. Combined with molecular docking results, these targets had a strong binding affinity between them and the active ingredient compounds, with the highest affinity for HIF-1α and TNF-.. During the in vitro cellular assay validation, the HIF-1α, ALB, Bcl-2, TNF-α, and IL-6 mRNA in CCECs cells showed positive regulation after YGP intervention. The combination of "data mining - network pharmacology - molecular docking - experimental validation" provides a powerful methodological basis for the study of the main active components and mechanism of action of YGP against DMED, as well as the development and application of the drug.

Integrating network pharmacology and experimental validation to advance psoriasis treatment: Multi-target mechanistic elucidation of medicinal herbs and natural compounds.

Multi-omics and experimental validation reveal the mechanism of DanxiaTiaoban decoction in treating atherosclerosis.

Sepsis is a dysregulated systemic response to infection, and no effective treatment options are available. Acacetin is a natural flavonoid found in various plants, including Sparganii rhizoma, Sargentodoxa cuneata and Patrinia scabiosifolia. Studies have revealed that acacetin potentially exerts anti-inflammatory and antioxidative effects on sepsis. In this study, we investigated the potential protective effect of acacetin on sepsis and revealed the underlying mechanisms using a network pharmacology approach coupled with experimental validation and molecular docking. First, we found that acacetin significantly suppressed pathological damage and pro-inflammatory cytokine expression in mice with LPS-induced fulminant hepatic failure and acute lung injury, and in vitro experiments further confirmed that acacetin attenuated LPS-induced M1 polarization. Then, network pharmacology screening revealed EGFR, PTGS2, SRC and ESR1 as the top four overlapping targets in a PPI network, and GO and KEGG analyses revealed the top 20 enriched biological processes and signalling pathways associated with the therapeutic effects of acacetin on sepsis. Further network pharmacological analysis indicated that gap junctions may be highly involved in the protective effects of acacetin on sepsis. Finally, molecular docking verified that acacetin bound to the active sites of the four targets predicted by network pharmacology, and in vitro experiments further confirmed that acacetin significantly inhibited the upregulation of p-src induced by LPS and attenuated LPS-induced M1 polarization through gap junctions. Taken together, our results indicate that acacetin may protect against sepsis via a mechanism involving multiple targets and pathways and that gap junctions may be highly involved in this process.

Explorating the mechanism of Huangqin Tang against skin lipid accumulation through network pharmacology and experimental validation

Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., also known as Indian bay leaf, holds a distinctive position in complementary and alternative medicinal systems due to its anti-inflammatory properties. However, the active constituents and key molecular targets by which C. tamala essential oil (CTEO) exerts its anti-inflammatory action remain unclear. The present study used network pharmacology and experimental validation to investigate the mechanism of CTEO in the treatment of inflammation. GC-MS analysis was used to identify the constituents of CTEO. The key constituents and core targets of CTEO against inflammation were obtained by network pharmacology. The binding mechanism between the active compounds and inflammatory genes was ascertained by molecular docking and molecular dynamics simulation analysis. The pharmacological mechanism predicted by network pharmacology was verified in lipopolysaccharide-stimulated murine macrophage (RAW 264.7) cell lines. Forty-nine constituents were identified by GC-MS analysis, with 44 constituents being drug-like candidates. A total of 549 compounds and 213 inflammation-related genes were obtained, revealing 68 overlapping genes between them. Compound target network analysis revealed cinnamaldehyde as the core bioactive compound with the highest degree score. PPI network analysis demonstrated Il-1β, TNF-α, IL8, IL6 and TLR4 as key hub anti-inflammatory targets. KEGG enrichment analysis revealed a Toll-like receptor signalling pathway as the principally regulated pathway associated with inflammation. A molecular docking study showed that cinnamaldehyde strongly interacted with the Il-1β, TNF-α and TLR-4 proteins. Molecular dynamics simulations and MMPBSA analysis revealed that these complexes are stable without much deviation and have better free energy values. In cellular experiments, CTEO showed no cytotoxic effects on RAW 264.7 murine macrophages. The cells treated with LPS exhibited significant reductions in NO, PGE2, IL-6, TNF-α, and IL-1β levels following treatment with CTEO. Additionally, CTEO treatment reduced the ROS levels and increased the antioxidant enzymes such as SOD, GSH, GPx and CAT. Immunofluorescence analysis revealed that CTEO inhibited LPS-stimulated NF-κB nuclear translocation. The mRNA expression of TLR4, MyD88 and TRAF6 in the CTEO group decreased significantly compared to the LPS-treated group. The current findings suggest that CTEO attenuates inflammation by regulating TLR4/MyD88/NF- κB signalling pathway.

To investigate the synergistic effects of Chuanxiong-Chishao herb-pair (CCHP) on promoting angiogenesis in silico and in vivo. The mechanisms of action of an herb-pair, Chuanxiong-Chishao, were investigated using the network pharmacological and pharmacodynamic strategies involving computational drug target prediction and network analysis, and experimental validation. A set of network pharmacology methods were created to study the herbs in the context of targets and diseases networks, including prediction of target profiles and pharmacological actions of main active compounds in Chuanxiong and Chishao. Furthermore, the therapeutic effects and putative molecular mechanisms of Chuanxiong-Chishao actions were experimentally validated in a chemical-induced vascular insuffificiency model of transgenic zebrafifish in vivo. The mRNA expression of the predicted targets were further analyzed by real-time polymerase chain reaction (RT-PCR). The computational prediction results found that the compounds in Chuanxiong have antithrombotic, antihypertensive, antiarrhythmic, and antiatherosclerotic activities, which were closely related to protecting against hypoxic-ischemic encephalopathy, ischemic stroke, myocardial infarction and heart failure. In addition, compounds in Chishao were found to participate in anti-inflflammatory effect and analgesics. Particularly, estrogen receptor α (ESRα) and hypoxia-inducible factor 1-α (HIF-1α) were the most important potential protein targets in the predicted results. In vivo experimental validation showed that post-treatment of tetramethylpyrazine hydrochloride (TMP•HCl) and paeoniflorin (PF) promoted the regeneration of new blood vessels in zebrafifish involving up-regulating ESRα mRNA expression. Co-treatment of TMP•HCl and PF could enhance the vessel sprouting in chemical-induced vascular insuffificiency zebrafifish at the optimal compatibility proportion of PF 10 μmol/L with TMP•HCl 1 μmol/L. The network pharmacological strategies combining drug target prediction and network analysis identified some putative targets of CCHP. Moreover, the transgenic zebrafifish experiments demonstrated that the Chuanxiong-Chishao combination synergistically promoted angiogenic activity, probably involving ESRα signaling pathway.

Diabetic kidney disease (DKD) is a major cause of end-stage renal disease (ESRD), and inflammation is the main causative mechanism. Schisandra chinensis fruit Mixture (SM) is an herbal formulation that has been used for a long time to treat DKD. However, its pharmacological and molecular mechanisms have not been clearly elucidated. The aim of this study was to investigate the potential mechanisms of SM for the treatment of DKD through network pharmacology, molecular docking and experimental validation. The chemical components in SM were comprehensively identified and collected using liquid chromatography-tandem mass spectrometry (LC-MS) and database mining. The mechanisms were investigated using a network pharmacology, including obtaining SM-DKD intersection targets, completing protein-protein interactions (PPI) by Cytoscape to obtain key potential targets, and then revealing potential mechanisms of SM for DKD by GO and KEGG pathway enrichment analysis. The important pathways and phenotypes screened by the network analysis were validated experimentally in vivo. Finally, the core active ingredients were screened by molecular docking. A total of 53 active ingredients of SM were retrieved by database and LC-MS, and 143 common targets of DKD and SM were identified; KEGG and PPI showed that SM most likely exerted anti-DKD effects by regulating the expression of AGEs/RAGE signaling pathway-related inflammatory factors. In addition, our experimental validation results showed that SM improved renal function and pathological changes in DKD rats, down-regulated AGEs/RAGE signaling pathway, and further down-regulated the expression of TNF-α, IL-1β, IL-6, and up-regulated IL-10. Molecular docking confirmed the tight binding properties between (+)-aristolone, a core component of SM, and key targets. This study reveals that SM improves the inflammatory response of DKD through AGEs/RAGE signaling pathway, thus providing a novel idea for the clinical treatment of DKD.

Elucidating the mechanism of action of Isobavachalcone induced autophagy and apoptosis in non-small cell lung cancer by network pharmacology and experimental validation methods