Integrating network pharmacology, machine learning, and molecular docking to explore the therapeutic mechanisms of Huangqin in atopic dermatitis: A STROBE-compliant observational study

Integrated network pharmacology and bioinformatics analysis reveals multi-target mechanisms of HeJie Shengfa Decoction against alopecia areata

Background Alopecia areata (AA) is a common non-scarring autoimmune disease with a complex pathogenesis, high recurrence rates, and difficulty in achieving a cure. Recent years have seen an increasing focus on both clinical and basic research regarding AA. Hejie Shengfa Decoction (HSD), a traditional Chinese medicine formula, has shown certain efficacy in the clinical treatment of AA, though its underlying mechanisms remain unclear. Methods In this study, we retrieved the active ingredients of HSD and their associated targets from public databases. We then identified AA-related targets through bioinformatics analysis of publicly available AA datasets. A protein-protein interaction (PPI) network was constructed to generate the HSD-AA action network by integrating drug-specific targets and disease-related targets. Functional enrichment analysis was subsequently performed. To further investigate key genes, three machine learning algorithms—least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE), and Random Forest—were applied Additionally, immune cell infiltration analysis was carried out to examine the roles of these key genes in the local immune microenvironment. Molecular docking and molecular dynamics simulations were employed to assess the binding stability of the active ingredients with the core targets. Results The results revealed that HSD contains 96 active ingredients and 985 related targets. From the AA dataset, 955 differentially expressed genes and 492 co-expressed modular genes were identified, resulting in 23 intersecting genes after integration. Machine learning algorithms identified four key target genes among these 23. Immune infiltration analysis suggested that HSD could influence the immune microenvironment of AA by modulating the expression of these key targets. Molecular docking and molecular dynamics simulations confirmed strong and stable binding interactions between HSD’s main active ingredients—especially quercetin—and the core targets. Conclusion This study elucidates the potential of HSD as a treatment for AA and provides insights into its mechanisms of action, offering a novel approach for treating AA with multi-targeted traditional Chinese medicine.

Integrative network pharmacology and transcriptomics analysis reveals the multi-target anti-allergic mechanism of ginsenoside Rh1

Integrating network pharmacology, molecular docking, and experimental validation to investigate the therapeutic effects and potential mechanisms of lycopene against pancreatic ductal adenocarcinoma

Background Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive tumor of the digestive system with a very low five-year survival rate. The limited efficacy and significant toxicity of existing chemotherapy regimens make the development of novel natural therapeutic agents an urgent priority. Lycopene is a natural carotenoid that has been shown to inhibit multiple cancers. However, research specifically targeting PDAC remains relatively scarce. Methods This study first employed bibliometric analysis to examine the research landscape and emerging trends in lycopene-related cancer research from 2016 to 2026. Subsequently, network pharmacology methods are applied to screen potential lycopene targets and PDAC-related targets from databases such as CTD, ChEMBL and HERB. Following the identification of overlapping targets, drug-target and protein–protein interaction (PPI) networks are constructed, as well as a disease network. The mechanisms were explored using Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Molecular docking was used to predict the potential interactions between lycopene and representative hub targets, and molecular dynamics simulations were performed for selected high-ranking docking complexes to provide supportive information on complex-level conformational stability. In vitro experiments were then conducted to evaluate the predicted anti-PDAC effects and to perform focused validation of apoptosis-related proteins and the PI3K/Akt/P53 signaling axis. Results Publications on lycopene research in the field of cancer have shown a sustained upward trend. The focus of this research has gradually shifted from areas such as oxidative stress and antioxidant effects towards anti-cancer mechanisms. A total of 132 overlapping targets for lycopene’s anti-PDAC activity were screened, leading to the identification of 10 core targets, including BCL2, AKT1, and TP53. GO enrichment analysis revealed that these targets are involved in biological processes such as the response to oxidative stress and cellular senescence. Meanwhile, KEGG enrichment analysis identified the PI3K-Akt signaling pathway as a key pathway. Molecular docking results showed that the binding energies of lycopene with core targets such as TP53 and BCL2 were below −4.5 kcal/mol. Molecular dynamics simulations provided supportive evidence for the conformational stability of representative lycopene-target complexes. In vitro experiments showed that lycopene inhibited the proliferation and migration of PDAC cells and promoted apoptosis-associated cell death, accompanied by decreased p-PI3K and p-AKT expression and increased P53 expression. Conclusion This study systematically combined bibliometrics, network pharmacology, molecular docking, representative molecular dynamics simulations, and focused experimental validation to explore the potential anti-PDAC activity of lycopene. The inflammation-related hub targets identified by network analysis provide additional hypotheses for future experimental investigation. These findings provide preliminary mechanistic evidence for further preclinical investigation of lycopene in PDAC, but its translational application will require optimized formulations, pharmacokinetic validation, and in vivo efficacy studies to overcome its limited bioavailability.

Target-centered network pharmacology and molecular docking reveal potential mechanisms and bioactive compounds of Zhigancao Decoction in insomnia–Arrhythmia comorbidity

This study aimed to investigate the potential mechanisms of Zhigancao Decoction (ZGCT) in insomnia–arrhythmia comorbidity using a target-centered network pharmacology and molecular docking approach. In addition, a refined candidate bioactive compound library was constructed. Active components of ZGCT were retrieved from TCMSP and HERB databases, and target prediction was performed using SwissTargetPrediction. Disease-related targets for insomnia and arrhythmia were obtained from GeneCards, followed by identification of intersection targets. A protein–protein interaction (PPI) network was constructed using STRING, and hub genes were identified via topological analysis and Maximal Clique Centrality (MCC) in Cytoscape. Functional enrichment analysis was performed using Metascape, and molecular docking was used to evaluate ligand–target interactions. A total of 84 active compounds and 193 associated targets were identified for ZGCT. Intersection analysis yielded 41 common targets. PPI network analysis identified 10 hub targets, including AKT1, PPARG, and MMP9. Based on a target-centered reverse screening strategy, 22 candidate bioactive compounds were identified, and molecular docking showed computationally favorable binding energies between key compounds and core targets. These compounds are associated with neuroendocrine, inflammatory, and cardiovascular signaling pathways. ZGCT is predicted to exert therapeutic effects on insomnia–arrhythmia comorbidity through a systems-level multi-target regulatory network involving neuroendocrine modulation, renin–angiotensin system-related pathways, and cardiovascular remodeling. The proposed target-centered reverse screening strategy provides a refined and interpretable framework for constructing a disease-specific bioactive compound library, offering potential directions for further experimental validation and drug development.

Integrated network pharmacology, molecular docking, and experimental validation reveal the therapeutic potential of ginger jujube tea against colorectal cancer

Background: Colorectal cancer (CRC) is one of the most common and lethal malignancies worldwide, with rising incidence and limited therapeutic options. Traditional Chinese medicine (TCM) formulations, known for their multi-component and multi-target synergistic actions, offer a promising alternative or adjunctive strategy. Ginger jujube tea (GJT), a classic TCM decoction, has been historically used for gastrointestinal regulation, yet its anti-CRC efficacy and pharmacological mechanisms remain unexplored. Methods: An integrated network pharmacology approach was employed to predict the active constituents of GJT and their potential CRC-related targets, followed by molecular docking to validate key compound-target interactions. The predicted pathways were further investigated through in vitro experiments using CRC cell lines (proliferation assays, reactive oxygen species (ROS) detection, apoptosis evaluation) and in vivo studies in a CRC xenograft mouse model, where tumor growth inhibition, safety profiles, and mechanistic markers (MAPK1, ESR1, HIF1A, PI3K-AKT pathway) were assessed. Results: Network pharmacology and molecular docking identified quercetin, naringenin, and other bioactive compounds as major active components, with core targets including AKT1, MAPK1, and ESR1 , predominantly enriched in the PI3K-AKT signaling pathway. In vitro , GJT dose-dependently suppressed CRC cell proliferation, significantly elevated intracellular ROS levels, and induced apoptosis. In vivo , GJT at 10 g/kg achieved a robust tumor growth inhibition rate of 64.69 ± 6.95% without observable toxicity, as confirmed by biochemical and histopathological analyses. Mechanistically, GJT downregulated MAPK1, ESR1, and HIF1A expression and inhibited PI3K-AKT pathway activation, corroborating the network-based predictions. Conclusion: GJT exerts potent anti-CRC effects through a multi-component, multi-target, and multi-pathway mechanism, consistent with the holistic philosophy of TCM. The favorable efficacy and safety profile support its potential as a complementary therapeutic candidate for CRC, warranting further clinical investigation.

Network pharmacology, molecular docking, and dynamics to explore the pharmacodynamic components and mechanism of epimedium in treating skeletal diseases

Periodontitis, chronic osteomyelitis of the jaw, and osteoporosis are etiologically distinct disorders but share persistent inflammation, impaired bone remodeling, and progressive bone loss. Epimedium brevicornum Maxim. has been widely used in traditional Chinese medicine for bone-related conditions; however, its shared mechanisms across these bone-destructive diseases remain insufficiently defined. This study integrated network pharmacology, molecular docking, and molecular dynamics simulations to identify potential active compounds, targets, and pathways of Epimedium. Twenty-three active compounds were screened from Epimedium, and 539 drug-related targets were obtained. A total of 966 disease-related targets were collected from public databases, yielding 160 overlapping targets. Protein-protein interaction analysis identified JUN, TNF, IL6, and TP53 as candidate hub genes. GO and KEGG analyses suggested that the common targets were mainly associated with inflammatory regulation, stress response, immune activation, and bone-homeostasis-related signaling. In the compound-target-pathway-disease network, quercetin, kaempferol, luteolin, genistein, and anhydroicaritin showed high connectivity. Molecular docking indicated favorable binding of quercetin and kaempferol with the hub proteins, with binding energies below -5.0 kcal/mol. A 50 ns molecular dynamics simulation further suggested stable TNF-quercetin and TP53-kaempferol complexes, with RMSD values remaining approximately 0.16-0.20 nm. These findings provide a computational basis for the hypothesis that Epimedium may modulate shared inflammatory and bone-remodeling networks in these diseases, which requires further experimental validation.