Aug 2026· Advanced Electromagnetics· Vol 15, pp. 5362-5373· 0 citations
Abstract
To address the low accuracy of multi-component-multi-target interaction prediction in Bupleurum chinense caused by complex molecular structures and limited target associations, this paper employs advanced graph models combined with biological network topology information. The same graph-based representation strategy is relevant to materials informatics, including the screening of molecular or polymer structures for electromagnetic sensing, dielectric response, and functional coating design. Specifically, RDKit is used to construct chemically defined molecular graphs, with atoms as nodes and covalent bonds as edges, and to encode multidimensional atomic features. These graphs are directly input into Graphormer. Graphormer then incorporates a fully connected attention mechanism that integrates topological distances and spatial geometric information from RDKit graphs to extract higher-order molecular representations. Node2Vec is used to embed the protein-protein interaction network in a low-dimensional space and identify potential target pathways. Finally, compound and protein embeddings are concatenated and fed into a two-channel neural network to predict interaction probabilities. With negative sampling for training, the method achieves stable average AUROC values of 0.91-0.93 and AUPRC values of 0.88-0.90. Even when only 10% of targets are visible, the F1-score reaches 0.683±0.021 and Coverage Rate@50 reaches 76.4%, indicating strong inductive reasoning under sparse knowledge conditions.
BACKGROUND
Rosacea is a chronic inflammatory skin disorder with limited therapeutic options. Puhuaiyin (PHY), a traditional Chinese medicinal formula, shows clinical efficacy, but its multi-component mechanisms remain unclear.
METHODS
Chemical constituents of PHY were identified by UPLC-Q-TOF-MS. Network pharmacology was used to predict potential targets, which were intersected with rosacea-associated genes. Bioinformatics analyses (differential expression, WGCNA, and machine learning) were applied to the GEO dataset GSE65914 to refine core targets. Molecular docking and molecular dynamics simulations were conducted to validate the binding modes and stability between key active constituents and the core targets.
RESULTS
A total of 59 chemical constituents were identified in PHY, with five key active components subsequently screened: quercetin, emodin, kushenol N, physcion, and palmitic acid. Network pharmacology analysis revealed 44 intersecting targets, which were significantly enriched in inflammation-related pathways, such as MAPK, NF-κB, and JAK-STAT signaling. Integrated bioinformatics and machine learning approaches identified MMP9 and IL1B as core targets, both of which were markedly upregulated in rosacea lesions and demonstrated prominent diagnostic value (AUC = 0.999 for MMP9, 0.964 for IL1B). Molecular docking indicated strong binding affinity between the core components and MMP9/IL1B. Molecular dynamics simulations confirmed stable complex conformations over 200 ns, with MM/PBSA binding free energies of -15.54 Kcal/mol (quercetin-MMP9) and -15.57 Kcal/mol (quercetin- IL1B).
DISCUSSION
This study, through a multidisciplinary approach, systematically elucidates the "multi-component, multi-target, and multi-pathway" mode of action of PHY in the treatment of rosacea. However, the computational predictions remain to be further validated by in vivo and in vitro experiments. Future research should focus on verifying its therapeutic efficacy in animal or cellular models, as well as elucidating the regulatory effects of key active components on the MMP9 and IL1B targets.
CONCLUSION
These computational predictions suggest that PHY may exert therapeutic effects against rosacea via quercetin and other components targeting MMP9 and IL1B, thereby modulating MAPK, NF-κB, and JAK-STAT pathways. The proposed mechanisms include inhibition of inflammation, regulation of the immune microenvironment, attenuation of vascular dilation, and promotion of skin barrier recovery. These findings provide a theoretical basis for future experimental validation.
Dan Sun, Na-Na Yang, Yi-Ding Zhao et al.· Current Computer - Aided Dru...· 0 citations
Bread and cereal-based products represent not only a source of macronutrients but also a complex food matrix containing low-molecular-weight phenolic, aromatic and fermentation-derived compounds. A number of these molecules have been associated with antioxidant, anti-inflammatory, antifungal and receptor-mediated effects in experimental studies and pharmacological databases. However, these records mainly describe test systems for individual compounds and do not demonstrate the corresponding effects directly in bread products. The objective was to develop a reproducible chemoinformatics framework for internal prioritization of compounds associated with bread and cereal matrices based on structural features and ChEMBL pharmacological annotations. The study included 43 curated compounds. After structural standardization and InChIKey deduplication, 2,058 molecular features were calculated, including physicochemical descriptors and topological fingerprints. Functional labels were generated from ChEMBL records for four activity classes: antioxidant, anti-inflammatory, antifungal, and AhR-modulating. Logistic regression, random forest, CatBoost, and XGBoost were compared using nested repeated multilabel cross-validation; no independent external dataset was used in this study. Random forest showed the best balance of performance and robustness, with a macro average precision of 0.8041 (95 % CI: 0.7766–0.8320) and a macro ROC-AUC of 0.8056. The anti-inflammatory class showed the most stable performance, whereas the antioxidant class was more heterogeneous. The highest-ranking compounds were protocatechuic, caffeic, gallic and ferulic acids, quercetin, and (+)-catechin. Evaluation with molecular scaffold-based splitting reduced macro average precision to 0.6245, indicating limited transferability to structurally distant compounds. The proposed approach should be regarded as a tool for preliminary candidate selection for further external and biological validation, rather than as experimental confirmation of their functional effects in the bread matrix.
M. Kuznetsov, I. Nikitin· Food systems· 0 citations
Inflammation is a defensive immune response to tissue damage or infection. Polygonatum cyrtonema Hua (P. cyrtonema Hua, PCH) is bioactive on immune homeostasis due to its rich components and reportedly plays a therapeutic role in the treatment and prevention of diabetes. Flavonoids of PCH possess anti-inflammatory properties, but their molecular mechanisms remain elusive. Here, chemical profiling of key flavonoids was conducted using UPLC-QTOF-MS/MS and the Global Natural Products Social Molecular Networking (GNPS) platform. Network pharmacology predicted potential targets and pathways, validated by molecular docking, surface plasmon resonance (SPR), and western blotting. Totally, 67 compounds were identified, with methylophiopogonanone B (MOB) as the key bioactive flavonoid. Although MOB's anti-inflammatory activity has been previously noted, the present study provides the first evidence that this effect may be mediated through targeting SRC and modulating the PI3K/AKT signaling axis. Core therapeutic targets were identified as SRC, TNF, and AKT1 by topological analysis of network pharmacology. Molecular docking and SPR confirmed strong MOB-SRC binding affinity. Western blotting revealed MOB dose-dependently inhibited LPS-induced phosphorylation of SRC, PI3K, and AKT1, without altering total protein levels. Furthermore, MOB significantly suppressed the phosphorylation of NF-κB pathway proteins IκB and p65, confirming the involvement of NF-κB as a downstream effector. In conclusion, this study integrates chemical profiling and network pharmacology with experimental validation to define the flavonoid composition of PCH, and is the first to implicate the SRC-PI3K-Akt pathway in MOB's anti-inflammatory action and providing novel evidence supporting the mechanism and use of PCH in contemporary diabetes treatment.
Yi Han, Ren-Bin Li, Baobao Yao et al.· Biomedical chromotography· 0 citations
It is suggested that Huangqin exerts multi-target effects on AD, centered on AKT1-mediated signaling crosstalk, to regulate inflammatory and immune pathways.
This study elucidates the potential of HSD as a treatment for Alopecia areata and provides insights into its mechanisms of action, offering a novel approach for treating AA with multi-targeted traditional Chinese medicine.
OBJECTIVE
This study integrates machine learning with network pharmacology, molecular docking, and molecular dynamics simulations to screen bioactive compounds from Mulberry leaves and elucidate their potential mechanisms against atherosclerosis (AS).
METHODS
A training dataset of anti-AS active compounds was compiled and encoded as Morgan fingerprints. Three machine learning classifiers, specifically Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XG-Boost), were constructed and evaluated using multiple performance metrics. Potential active components from Mulberry leaves and AS-related targets were retrieved, followed by protein-protein interaction network construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was then performed to evaluate binding affinities between core targets and candidate compounds, and the most stable complex was subjected to molecular dynamics simulations using GROMACS (2025).
RESULTS
The RF model achieved superior performance (accuracy= 0.8354, F1 = 0.8408, AUC = 0.9119) with 100% external validation accuracy. Thirteen anti-AS candidates were prioritized from mulberry leaves, four of which have been previously documented. Network pharmacology revealed AKT1 and IL6 as core targets, enriched in pathways such as endocrine resistance. Molecular docking and dynamics simulations confirmed strong binding between oxysanguinarine and AKT1, with the complex exhibiting high stability.
CONCLUSION
The RF model provides a reliable computational tool for prioritizing anti-AS compounds from Mulberry leaves. The integrated analysis reveals that Mulberry leaves exert anti-atherosclerotic effects through multi-target (e.g., AKT1, IL6) and multi-pathway (e.g., PI3K-Akt) mechanisms, offering a framework for further experimental validation.
Min-Li Gan, Raoqing Liu, Hongsheng Liu et al.· Computational biology and ch...· 0 citations
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