Lycium barbarum L) are herbal medicine that have a long history of use and multiple pharmacological activities. In this study, we investigated the potential therapeutic effects of Goji berries on atherosclerosis (AS) using network pharmacology and molecular docking.Goji berries (The active compounds of Goji berries were identified using the Traditional Chinese Medicine Systems Pharmacology platform, as well as the literature and the targets of each active compound were obtained using the Swiss Target Prediction database. The AS-related targets were collected from the GeneCards and OMIM databases to obtain the common targets of Goji berries and AS. The drug-compound-target-disease network and protein-protein interaction network were constructed using the Cytoscape software to obtain the core target proteins of Goji berries related to AS. Gene ontology analysis of the core targets and Kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed by Metascape. The target-chemical correlations were verified using AutoDock molecular docking.After analysis, 44 active compounds within Goji berries were obtained that exhibit associations with AS. Among these, the proteins exhibiting the highest degrees of interaction within the compound-targeted protein protein-protein interaction network were AKT1, SRC, MAPK3, MAPK1, RELA, and STAT3. The gene ontology-biology process analysis showed that compound-targeted proteins were mainly involved in regulating small molecule metabolic process, cellular response to chemical stress, reactive oxygen species metabolic process, and regulation of inflammatory response. Kyoto encyclopedia of genes and genomes pathway mainly included lipid and AS in which AKT1, SRC, MAPK3, and MAPK1 were involved. Advanced glycation end-product-receptor for advanced glycation end-product signaling pathway in diabetic complications, Chagas disease, and pancreatic disease. Molecular docking assessment showed that fucosterol is bound to AKT1, MAPK3, and SRC.This study demonstrates that network pharmacology and molecular docking analyses contribute to a better understanding of Goji berries active compounds and targets as potential therapeutic drugs for treating AS. Inflammation plays a vital role in both physiological and pathological alterations throughout the progression of AS. While often associated with cardiac implications, AS can affect arteries across the body. AS may cause angina pectoris, myocardial infarction, arrhythmia, and even sudden death within the coronary arteries. Similarly, cerebral AS can trigger cerebral ischemia, brain atrophy, or stroke. Renal AS is often associated with nocturia, hypertension, or impairments in renal function. Additionally, when AS occurs in the extremities, it may lead to claudication or induce lowered blood pressure within the affected limbs.,4 Generally, mild AS tends not to engender complications.Atherosclerosis (AS) stands as a chronic cardiovascular disease (CVD) primarily driven by lipid deposition within arterial walls.Lycium barbarum L., can be found in complex herbal formulations. Meanwhile, it is also used as a nutritional supplement to meals.,6 Goji berries has the effect of nourishing the kidney and liver, contributing to enhance immunity, anti-aging effects, blood sugar modulation, and lipid reduction. Nutritional science is now increasingly emphasized for its crucial role in intervening against non-communicable diseases, with dietary management being more feasible and adherable for patients than other treatments. In the last decades, epidemiological, clinical, and experimental studies have shown that diet plays a central role preventing AS.,9 Based on the established clinical utilization of Goji berries in TCM treatment, we raised the question of whether Goji berries could be used as a nutritional supplement to aid in the prevention and treatment of AS.Traditional Chinese medicine (TCM) is progressively garnering recognition beyond Chinese regions due to its notable efficacy and minimal toxicity. Goji berries, the dried fruit of ,11 In recent years, network pharmacology has been increasingly applied and recognized in the study of TCM.Due to the complex composition of Goji berries, it isn’t easy to systematically study its molecular pharmacological mechanism. Network pharmacology is an emerging discipline based on the theory of system biology, pharmacokinetic and pharmacodynamic properties of drugs, drug targets, and their pharmacological functions, analysis of biological system networks, and selection of specific signaling nodes for drug design and modification.In this study, network pharmacology was used to investigate the relationship between the bioactive components of Goji berries and AS-related target proteins. The pathway enrichment analysis and protein-protein interaction (PPI) analysis were used to derive the AS -related target proteins that Goji berries active compounds can interact with. Finally, the binding of Goji berries bioactive compounds to AS core target proteins was verified by molecular docking assay Fig. . Our reshttps://old.tcmsp-e.com/tcmsp.php), the chemical composition of Goji berries was searched and the components were screened according to OB  ≥30% and DL (drug-likeness) ≥0.18.In the traditional Chinese medicine systems pharmacology, (https://pubchem.ncbi.nlm.nih.gov). Then, the SMILES of each chemical was entered into the Swiss Target Prediction (http://www.swisstargetprediction.ch/index.php) database, and the species was set as Homo sapiens to obtain the predicted target of the compound. All results were collected and pooled to get the action targets of Goji berries.SMILES (simplified molecular input line entry specification) was obtained by searching for each active compound in “2.1” through the compound database PubChem (https://www.genecards.org) and the online Mendelian human genetic database OMIM (http://www.omim.org). The outcomes of these searches were then combined and summarized to obtain AS-related disease target proteins.AS-related target proteins were searched in the human gene database GeneCards (https://www.uniprot.org), transformed into standard gene names, and imported into Cytoscape software (http://www.cytoscape.org), construct the drug-component-target-disease network. The resultant network was subsequently subjected to a node degree analysis, informing the classification of nodes via diverse colors and shapes.The AS-related target proteins and Goji berries active-compound targets were imported into the target standardization database UniProt (https://cn.string-db.org), “Multiple Proteins” was selected, species was set to Homo sapiens, confidence of screening condition was set to highest confidence (≥0.9), obtain the PPI network, and save its related TSV format information file. The TSV files were imported into Cytoscape, organized and plotted to obtain the PPI network graph. Then, the network topology analysis was performed, with the node color intensity and size indicative of high or low degree. While edge thickness corresponded to the combined score between interconnected nodes.The potential targets of Goji berries acting on AS were obtained by importing the amalgamated compilation of Goji berries component targets and AS targets into Venny 2.1.0 software and taking the intersection of the 2 datasets. Subsequently, the potential targets of Goji berries in the context of AS, derived from Venny analysis, were introduced to the STRING 11.5 analysis platform (https://metascape.org), the species was selected as Homo sapiens, and KEGG analysis and GO enrichment analysis were performed, respectively. Outcomes were subsequently filtered based on a significance threshold of P < .05, ranked in ascending order according to P values, and the top 10 entries with the smallest P values were selected, and they were visualized using online tools (http://www.bioinformatics.com.cn/).The potential targets of Goji berries acting on AS were entered into the Metascape database . AKT1 , SRC , MAPK3 , MAPK1 , RELA , and STAT3  for energy minimization and imported into AutoDockTools software after removing impurities using Pymol software. Water molecules were removed, non-polar hydrogen atoms were added, and charges were calculated and saved as molecular docking receptors. The resulting receptor was molecularly docked with the ligand, and the number of dockings was set to 50 to obtain the final docking score. The lower the score, the lower the binding energy, the easier the receptor and ligand can bind, and the more stable the binding configuration.To elucidate the binding interactions, molecular docking assays were conducted involving the core target proteins obtained and the primary active compounds present in Goji berries using AutoDockTools software. with higher OB and DL having a higher druggability. These include quercetin, betaine, atropine, β-sitosterol, fucosterol, stigmasterol, etc.The components of Goji berries were searched by the traditional Chinese medicine systems pharmacology platform and screened according to OB ≥30% as well as DL ≥0.18 as conditions, and a total of 44 compounds were obtained, as shown in Table The SMILES of 44 major components of Goji berries were found in PubChem and were entered into the Swiss Target Prediction database to predict the potential action targets. After sorting and removing duplicate targets, the 44 active compounds of Goji berries yielded 453 action targets. The targets were searched in the GeneCards OMIM databases with “AS” as the keyword, and the relevance score threshold was set to surpass the average. The genes obtained from the GeneCards were 1243, and an additional 119 genes were from the OMIM database. These datasets were sorted and merged to obtain 1358 targets. The AS-related targets and Goji berries active-compound targets were imported into Venny 2.1.0 software to draw a Venn diagram  and KEGG pathway enrichment analysis, respectively. The top ten entries for GO BP, cellular component, and molecular function (MF) were visualized  accumulate in diabetic patients and appear to play an important role in the development of AS. AGEs are involved in the atherosclerotic process either directly or through receptor-mediated mechanisms, and the most widely studied receptor is RAGE (receptor for AGEs). RAGE deficiency reduces atherosclerotic plaque area in diabetic mice. Indubitably, insulin resistance directly contributes to the development of atherosclerotic CVD by inhibiting nitric oxide production  and stimulating MAPK pathways.,60 Our analysis showed multiple targets of Goji berries involved in atherosclerotic pathways  signaling pathway, insulin resistance, and IL-17 signaling pathway. The hallmark lesion characteristic of AS resides in lipid deposition within the arterial portion. In its early stages, atherosclerotic development is called “fatty streaks,” which are areas of intracellular lipid accumulation in the vessel wall characterized by foam cells. Notably, diabetes and AS are inextricably linked.Molecular docking assessment showed that the phytosterol, β-sitosterol, stigmasterol, and fucosterol have high binding energy to all core target proteins Table . FurtherIt should be noted that this study has certain limitations. Firstly, the data on bioactive compounds and targets were retrieved from literature and databases. Hence, the reliability and accuracy of predictions are contingent upon the quality of the data. Secondly, this research employed data mining techniques; further cellular experiments, animal studies, and clinical trials are required to validate the findings.Quercetin, betaine, atropine, β-sitosterol, fucosterol, and stigmasterol may be the main active compounds of Goji berries for the treatment of AS. Its critical targets include AKT1, SRC, MAPK1, MAPK3, RELA, and STAT3. The main indicated pathways are lipid and AS and the AGE-RAGE signaling pathway in diabetic complications. The therapeutic effects of Goji berries in treating AS might be mediated through multi-compounds, multi-targets, and multi-pathways, which required further research for validation.We thank Dr Haijie Yu and Dr Xinzhi Li for the fruitful discussion.Conceptualization: Xinchen Qin, Zikai Xie, Xi Chen, Xiaoxuan Wang.Data curation: Xinchen Qin, Zikai Xie, Xiaoxuan Wang.Investigation: Lijuan Ma.Methodology: Xinchen Qin, Zikai Xie, Xi Chen.Supervision: Lijuan Ma.Writing – original draft: Xinchen Qin.Writing – review & editing: Lijuan Ma.


Licorice, a traditional Chinese medicine, has been widely used for the treatment of COVID-19, but all active compounds and corresponding targets are still not clear. Therefore, this study proposed a deep learning-based network pharmacology approach to identify more potential active compounds and targets of licorice. 4 compounds , 2 targets (SYK and JAK2) and the relevant pathways  were predicted, which were confirmed by previous studies to be associated with SARS-CoV-2-infection. In addition, 2 new active compounds (glabrone and vestitol) and 2 new targets (PTEN and MAP3K8) were further validated by molecular docking and molecular dynamics simulations (simultaneous molecular dynamics), as well as the results showed that these active compounds bound well to COVID-19 related targets, including the main protease (Mpro), the spike protein (S-protein) and the angiotensin-converting enzyme 2 (ACE2). Overall, in this study, glabrone and vestitol from licorice were found to inhibit viral replication by inhibiting the activation of Mpro, S-protein and ACE2; related compounds in licorice may reduce the inflammatory response and inhibit apoptosis by acting on PTEN and MAP3K8. Therefore, licorice has been proposed as an effective candidate for the treatment of COVID-19 through PTEN, MAP3K8, Mpro, S-protein and ACE2. Existing therapies, including conventional treatments  and immunomodulators, can only play a preventive role, and the rapid development of specific drugs and vaccines targeting COVID-19 has become the greatest challenge2. Studies have shown that traditional Chinese medicine (TCM) can improve clinical symptoms, delay disease progression, as well as reduce mortality and recurrence rates in patients with COVID-194. Of the available formulas, preventive prescriptions, and therapeutic prescriptions for confirmed cases proposed by TCM, licorice is one of the most frequently used for the treatment of COVID-196.Coronavirus disease 2019 (COVID-19) has had a significant impact on global health systems and economic development due to its highly infectious nature and complex pathogenesis7, leguminous or plant rhizomes and dried roots are frequently used in medicinal preparations8. Many licorice compounds and corresponding targets have been shown to play a central role in the treatment of COVID-19 through network pharmacology analysis, as well as in in vivo or in vitro studies11. In terms of compounds, for example, Glycyrrhizic Acid, and other compounds found in licorice can bind to Mpro, ACE2 and S proteins, respectively, which could inhibit COVID-19 replication and block virus binding sites14; in terms of targets, MAPKs, ILs and NF-kB can regulate the MAPK signaling pathway, the IL-17 signaling pathway and the NF-kB signaling pathway, exerting anti-inflammatory and immunomodulatory effects13.Licorice is a perennial herb commonly used in TCM15, has the advantages of being comprehensive, systematic, and holistic, which is consistent with the multi-compound, multi-target, and multi-pathway characteristics of TCM, expanding the potential applications of TCM research16. However, it still presents some challenges, such as the lack of comprehensive data on various drugs, genes, and proteins17. Therefore, we proposed a deep learning (DL)-based network pharmacology method, in which a model integrated with a drug-target interaction (DTI) method was adopted [Highlight the innovative nature of the method].Traditional network pharmacology, namely P1 (DeepDGC) for DTI prediction, which included two DL algorithms, graph convolutional neural network (GCN) and CNN, to extract more characteristic information from the molecular structure of the compound. GCN and CNN were used to obtain two representations of the compound—the molecular map and the Morgan fingerprint, respectively. In addition, CNN was used to learn the amino acid sequence of the disease targets. After two vectors were generated in the above two steps, they were inserted into a fully connected layer, followed by a regression layer, in which the output was the compound-target affinity value.According to the above description, this study included a total of three processes, i.e., P1  ≥ 30% and drug-likeness (DL) ≥ 0.18, 92 active compounds remained. The corresponding targets of the active compounds were obtained from the TCMSP20, SwissTargetPrediction21, PharmMapper22 and GeneCards23 databases, and 1140 targets were named after deleting duplicate items.We retrieved 249 compounds by searching the TCMSP23, OMIM24, DrugBank25 and other databases with the keywords "COVID-19" produced 13,542 targets for COVID-19 after deleting duplicate values.Searching the GeneCards26 from 1140 licorice targets and 13,542 targets for COVID-19, which were considered potential targets of licorice acting in COVID-19.A total of 774 overlapping targets remained after intersection screening using Venny27 to obtain the PPI network. After the removal of isolated targets, the PPI network was imported into Cytoscape software28, where the Centiscape plug-in was used to screen for the key targets, with three parameters  used as thresholds. The key compounds were then acquired according to the key targets.The overlapping targets were loaded into the STRING database29 was used to conduct GO and KEGG enrichment analysis (p < 0.01), and a licorice-compound-target-pathway network was built to screen core targets by the Cytoscape software. All compounds corresponding to the core targets on the network were defined as core compounds.Based on the key targets, the Metascape databaseThe DeepDGC model Fig.  was used18 configurations to set up the CNN block for the DeepDGC model, and for GCN block, we used three graph convolutional layers. In each layer, the covalent bonds and node information were extracted according to the molecular map [Highlight the innovative nature of DL model].CNN was an architecture containing one or more convolutional layers, usually followed by a pooling layer, and GCN, an optimization of CNN, was a graph neural network using convolutional operations and compensated for the inability of CNN to handle non-Euclidean structured data. Therefore, the model combined GCN and CNN. It comprised two separate CNN blocks and a GCN block. For CNN block, we used DeepDTA’sThe KIBA dataset (pretraining dataset) for DeepDGC comprised primarily of the SMILES strings of 2111 active compounds, the amino acid sequences of 229 targets, and 118,254 binding affinity values. And it was employed to train the model using five-fold-cross-validation. Furthermore, to ensure the generalization ability, we set the activation functions relu and dropout. Finally, CI and MSE were used as evaluation indicators and the average results were reported. And with regard to the parameter settings, some of them were based on relevant studies, while other important parameters were compared experimentally for optimum results. The relevant parameters were set as shown in Table Subsequent to the evaluation of the pre-training dataset, the DeepDGC model was used to predict affinity values in process P2 and P3. The prediction dataset used in process P2 was composed primarily of the SMILES strings of the key compounds of licorice , and amino acid sequences of the key targets ; while the prediction dataset used in process P3 was composed primarily of the SMILES strings of active compounds of licorice (screened by OB ≥ 30% and DL ≥ 0.18), and amino acid sequences of targets (for COVID-19).30. In this study, the physicochemical and pharmacokinetic properties of the active components were predicted through the SwissADME database31 and the pkCSM database32, respectively.ADMET prediction is the assessment of five aspects , which plays a key role in drug developmentThe physicochemical properties considered here include molecular weight (MW), rotatable bond count (RB), H-bond acceptors (HBA), donor count, TPSA, and leadlikeness violations (LSV). The pharmacokinetic properties included absorption , distribution , excretion , and toxicity , hepatotoxicity, skin sensitization, and minnow toxicity).33 and PDB34 databases. Second, AutoDockTools was used to conduct a range of operations, such as hydrogenation, charge addition, removal of water molecules, and removal of metal ions. Third, the PubChem database35 was used to construct the 3D structures of the active compounds. Subsequently, global docking boxes were generated by AutoDockTools while blind docking was performed using qvina-w. The binding score was used to evaluate the ability of a natural compound to bind to the target. Finally, heat maps and 3D docking maps of the docking results were created using Python and Pymol.Molecular docking was used to further verify the binding capabilities of the core compounds and related targets outlined above. First, the monomeric component structures of the protein targets and related information were obtained from the UniProb36. To ensure the total charge neutrality of the simulated systems, corresponding amounts of sodium ions were added to the three systems to displace water molecules and produce solvent boxes of appropriate size. Next, periodic boundary conditions (PBC) were applied in each of the three directions of the system, thereby determining the force field parameters for the entire atom. Finally, two complexes were simulated for a 100 ns NPT ensemble .The compound-protein target pair with the highest binding energy of molecular docking was subjected to molecular dynamics (MD) simulations to further check the binding stability of the two. MD simulations were then performed using Gromacs softwareTo explore the mechanism underlying the therapeutic effects of licorice against COVID-19, 774 targets were imported into the STRING database to construct a PPI network. After screening according to three thresholds, we obtained 88 key compounds Table  and 156 To determine the molecular mechanisms underlying the efficacy of licorice treatment against COVID-19, we used Metascape to perform the biofunctional annotation of GO and KEGG pathway enrichment analysis of the key targets. The GO biofunctional annotation results showed that a variety of terms were identified, including 254 biological process (BP) terms that mainly relate to the positive regulation of protein phosphorylation and the positive regulation of cell migration, 141 cellular component (CC) terms that mainly relate to the lumen of the vesicle, membrane rafts, and receptor complexes, 59 molecular function (MF) terms that were mainly related to kinase binding, protein serine/threonine/tyrosine kinase activity and transcription factor binding. The top 10 considerably enriched terms for BP, CC, and MF were visualized in Fig. The KEGG pathway enrichment analysis results showed that the key targets were enriched in 180 pathways, and the top 20 paths with the highest level of enrichment were chosen for visualization in Fig. Furthermore, to better understand the mechanism by which licorice acts on COVID-19, a licorice compound-target-pathway relationship network was built based on the above KEGG pathway Fig. , which s37, SimBoost38, DeepDTA15, WideDTA39, DeepPurpose19 as baseline models and the comparison results were shown in Table To explore more core compounds and core targets, a DeepDGC model was presented in this study. After the model was trained on the pre-training dataset, it was used to predict the affinity values between compounds and targets. We used four models  and 6 targets  had been explored previously. Therefore, the other 15 core compounds  and 2 core targets (PTEN and MAP3K8) will be discussed below.The combined results of the three processes , including all core compounds and core targets, were shown in Table 46), indicating they could be considered excellent drug candidates against COVID-19  were docked to COVID-19 binding sites, such as Mpro, S-protein and ACE2. The binding affinities were shown in Table To support our findings mentioned above, we used molecular docking to evaluate the interaction between the active core compounds and the core targets, in which binding affinity less than − 7.0 kcal/mol indicated a good interactionThe larger the protein Calpha root-mean-square deviation (RMSD) of the MD simulations, the more violent the fluctuations indicating greater motility and less stability.The RMSD data of licorice, include glabrone and S-protein, as well as licoisoflavone B and MAP3K8, were shown in Fig. The study design was divided into three processes, including the traditional network pharmacology process (P1) and the two improved processes (P2 and P3). Using this approach 20 core compounds and 6 core targets were predicted, among which 4 active compounds  and 2 targets (SYK and JAK2), were associated with COVID-19 infection, as confirmed by previous studies. Furthermore, after ADMET and molecular docking analysis, two new active compounds were identified: vestitol was identified in P1 and glabrone was jointly identified in processes P2 and P3. and 2 new targets were also predicted, among which PTEN was identified in P2, and MAP3K8 were identified in P3. In addition, this study also revealed the signaling pathways of P53, cAMP, and NF-kB.48. Naringenin also induces anti-inflammatory and antiviral activity49. As a flavonoid with anticancer, antioxidant, hepatoprotective, immune regulatory, and antiplatelet aggregation properties, liquiritigenin has served as a therapy for COVID-19. For example, liquiritigenin was used to form a complex with the Mpro of SARS-CoV2 because it was found to inhibit the catalytic activity of the main protease50. Licoisoflavanone is an isoflavonoid compound that plays a role in the reduction of antiviral, cytokine storms, prevention of ARDS and multi-organ damage, and reduction of the severity of inflammatory diseases51.Considering the 4 active compounds , their potential roles in the treatment of COVID-19 have been demonstrated in previous studies. Quercetin has anti-inflammatory activity exerted by inhibiting the secretion of pro-inflammatory factors , by an antiviral effect by blocking the entry of coronavirus into host cells, as well as by an anticoagulant activity exerted by inhibiting plasma protein disulfide isomerase52. JAK2 involves in M2 macrophage polarisation, inflammatory response, pulmonary fibrosis and thrombosis by activating STAT3, a signal transduction and transcriptional activator53. An increasing number of studies have also highlighted that JAK2 is an important gene belonging to the JAK2/STAT3 signaling pathway, and can induce overexpression of IL-6 and IL-18, which can exacerbate the inflammatory response and lung injury46. Furthermore, the SYK inhibitor (fostamatinib)54 and the JAK2 inhibitor (fedratinib)55, evaluated in clinical studies, are highly effective in the prevention and treatment of COVID-19.Previous studies have also indicated that SYK and JAK2 may serve as target proteins related to COVID-19. SYK also plays an important role in the treatment of COVID-19 and has been reported to regulate signal transduction pathways implicated in these complications associated with COVID-19The examples given above demonstrate the important roles of the 4 active compounds and 2 targets in COVID-19, identified in our analysis, and confirmed by previous studies, suggesting the usefulness of our model in the prediction of active compounds and targets. In fact, we also identified 2 new compounds (vestitol and glabrone) and 2 new targets (PTEN and MAP3K8) that have not yet been demonstrated to be directly associated with the treatment of COVID-19, but have been attested to have a role in the treatment of COVID-19-related diseases .56. Glabrone can achieve an antioxidant activity by modulating the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway and an anti-inflammatory effect by regulating the NF-kB signaling pathway57. In addition, the antiviral activity of glabrone was demonstrated by cytopathic effect (CPE) inhibition assays targeting the influenza A virus58.Many related studies had shown that the newly identified active compounds vestitol and glabrone play a key role in the treatment of COVID-19. Because they not only inhibit viral replication through stable binding to the three viral binding sites of S-protein, ACE2, and Mpro, but also exert antioxidant, anti-inflammatory, and antiviral effects. For example, vestitol can achieve an anti-inflammatory effect by inhibiting the NF-kB signaling pathway and has shown to be a considerable promising new anti-inflammatory agent59. MAP3K8 participates in the pulmonary fibrotic response and the lung inflammatory response. An increasing number of studies had also highlighted the significance of MAP3K8 in suppressing lung inflammation and fibrosis (the main symptom of COVID-19)60.Two new targets identified using our approach, including PTEN and MAP3K8, were potentially key targets for the treatment of COVID-19. Studies had shown that PTEN can activate dendritic cells, B cells and T cells, which are innate immune cells, and secrete pro-inflammatory factors, including interferon (IFN), TNF-α and IL10, thus inducing the formation of the cytokine storm in patients with COVID-19. Therefore, targeting PTEN can inhibit the formation of cytokines storms61. Furthermore, p53, an intrinsic host restriction factor of SARS-CoV-2, can reduce virus production62. The cAMP signaling pathway is the most important signaling pathway in EG pathway enrichment, and EG could also act on the PI3K-Akt, JAK-STAT and chemokine signaling pathways, thus reducing responses such as inflammation and apoptosis40. In turn, the NF-kB signaling pathway, considered as an inflammation center63, induces various target genes in inflammatory diseases65, as well as regulates cytokine storm syndromes and immunosuppression67.In terms of the signaling pathway, the P53, cAMP, and NF-kB signaling pathways, discovered by using KEGG pathway analysis, are involved in inflammation, immunomodulation and infection. The P53 signaling pathway is a pathway known to influence immune responses69, and Mpro was a key target in the second process of infection (replication and transcription)70. In this paper, we preliminarily concluded that vestitol and glabrone had good binding stability with S-protein, ACE2 and Mpro by molecular docking. Analysis of the S protein and glabrone was then further performed by MD simulations to demonstrate that glabrone may inhibit host cell infection at the first stage of attachment and entry. In addition, we also conducted molecular docking of two new targets (PTEN and MAP3K8) with the compounds of licorice (including vestitol and glabrone), and the results showed good binding stability. We followed by MD simulations of MAP3K8 and licoisoflavone B with the highest binding energy, which demonstrated that licorice may be considered an effective candidate for the treatment of COVID-19 through MAP3K8 [MD simulations analysis].S-protein and ACE2 were key protein targets in the first process of infection (attachment and cell entry)Based on the above analysis, we knowed that the DL-based network pharmacology method could compensate to some extent for the impossibility of obtaining all compounds and targets in traditional network pharmacology through databases and analysis software, and achieve a promising predictive results. However, due to the predictive performance of DL was very dependent on the quantity and quality of the data, and it also had limitations in feature information extraction. Therefore, there was still room for improvement in future work in terms of the quantity and quality of data and feature extraction [Highlight the innovation of the method and the shortcomings of the new method].In this study, we proposed a DL-based DeepDGC model that learned from both molecular maps and Morgan fingerprint data representations of drugs, which contains more feature information for drug characterization that can be evaluated and optimize the model. As a result, 2 new compounds and 2 new targets were also found to possess potential effects on COVID-19 treatment. Although our findings are not sufficient to reach more definite conclusions and further validation, using in vivo or in vitro studies, is still encouraged, we believe that this method has a certain translational value that can also be applied to drug and target discovery studies in other diseases.Supplementary Information.


Atherosclerosis, a pathological condition marked by the accumulation of lipids and fibrous substances in the arterial walls, is a leading cause of heart failure and death. The present study aimed to utilize network pharmacology to assess the potential pharmacological effects of bioactive compounds in Tualang honey on atherosclerosis. This is significant as previous studies have indicated the cardioprotective effects of Tualang honey, yet a comprehensive evaluation using network pharmacology has yet to be conducted. The bioactive compounds in Tualang honey were screened and the potential gene targets for these compounds were predicted through Swiss Target Prediction and SuperPred databases. Atherosclerosis genes were retrieved from the OMIM, DisGeNet, and GeneCards databases. The interaction between these compounds and atherosclerosis genes was established through protein–protein interaction, gene ontology, and KEGG pathway analysis. The results of these analyses were then further confirmed through molecular docking studies using the AutoDock Tools software. The results revealed that 6 out of 103 compounds in Tualang honey met the screening criteria, with a total of 336 potential gene targets, 238 of which were shared with atherosclerosis. Further analysis showed that these active compounds had a good affinity with key targets and were associated with biological processes related to protein phosphorylation and inflammation as well as pathways related to lipid and atherosclerosis and other signaling pathways. In conclusion, the study provides insight into the potential pharmacological effects of Tualang honey bioactive compounds on atherosclerosis, supporting its use as a promising treatment for the disease. Cardiovascular disease (CVD) is a major health concern worldwide, with approximately 19.9 million deaths in 2020 .Atherosclerosis is a prevalent underlying pathology of CVD, characterized by plaque formation in the arterial walls. The pathogenesis of atherosclerosis involves a complex interplay of multiple stages—endothelial cell dysfunction, lipoprotein deposition and oxidation, inflammatory factor effects, and fibrous cap development . VariousApis dorsata) from the branches of the Tualang tree (Kompassia excelsa) commonly found in the northwestern region tropical rainforests of Peninsular Malaysia [Honey, with its rich content of polyphenols and other phytochemicals, has been used for medicinal purposes for centuries. The beneficial effects of honey on cardiovascular health have been attributed to several mechanisms, including improving lipid profiles , reducinMalaysia . PreviouMalaysia , a cruciMalaysia ,17. DespIn recent years, network pharmacology, aided by advancements in bioinformatics and related databases, has become a widely utilized tool in drug research to uncover the interrelationships between drugs, their targets, pathways, and related diseases . MoleculIn this study, we aim to investigate Tualang honey bioactive compound target and signaling pathways in the context of preventing atherosclerosis through a network pharmacology approach. By utilizing various databases, the complex relationships between the disease, drugs, and targets are systematically predicted and analyzed. The potential mechanisms of key targets for Tualang honey in treating atherosclerosis are also examined, with molecular docking techniques used to verify the potential targets of Tualang honey against atherosclerosis. The complete research process is depicted in https://pubmed.ncbi.nlm.nih.gov, accessed on 26 January 2023) [https://link.springer.com, accessed on 26 January 2023) [https://www.sciencedirect.com, accessed on 26 January 2023) [Identification of bioactive compound of Tualang honey were scholarly searched through the literature repositories such as PubMed (ry 2023) , and Sciry 2023) .https://old.tcmsp-e.com/tcmsp.php, accessed on 30 January 2023) [In order to thoroughly understand the potential therapeutic benefits of a natural product, it is necessary to examine its human absorption, distribution, metabolism, and excretion (ADME) properties. To evaluate these parameters, an in silico integrative ADME model was utilized with the use of Traditional Chinese Medicine Systems Pharmacology Database   in orderhttp://www.swisstargetprediction.ch/, accessed on 1 February 2023) [https://prediction.charite.de/index.php, accessed on 1 February 2023) [The Swiss Target Prediction database (ry 2023)  and the ry 2023)  were utihttps://www.omim.org/, accessed on 3 February 2023) [https://www.disgenet.org/, accessed on 3 February 2023) [http://www.genecards.org/, accessed on 3 February 2023) [The identification of target genes associated with the condition of atherosclerosis was sourced from three well-established databases: Online Mendelian Inheritance in Man  , Diseasery 2023) , and Genry 2023) . The inqhttp://www.interactivenn.net/, accessed on 4 February 2023) [The potential target genes for both Tualang honey and atherosclerosis were analyzed through InteractiVenn (ry 2023) , with thhttps://cytoscape.org/, accessed on 7 February 2023) [The establishment of a compound-target network was constructed through the utilization of the Cytoscape software version 3.9.1 (ry 2023)  in orderhttps://string-db.org/, accessed on 7 February 2023) [Homo sapiens species and only those with a confidence score of at least 0.900 were considered for network visualization. The resulting network was depicted using the Cytoscape platform, integrating data obtained from the STRING database. The significance of genes in the network was determined through the utilization of the CytoHubba plugin [The intersection between the target genes of bioactive compounds in Tualang honey and atherosclerosis were analyzed through a protein–protein interaction network analysis, utilizing the STRING database version 11.5 (ry 2023) . The anaa plugin , employihttps://david.ncifcrf.gov/home.jsp, accessed on 9 February 2023) [p-values were calculated, and statistically significant results were indicated by a p-value of less than 0.05.A gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted to gain a deeper understanding of the biological mechanisms underlying the combination of Tualang honey bioactive compounds and its potential effects against atherosclerosis. The analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), version 2021 (ry 2023) . The sofry 2023) ,36,37. Thttps://www.rcsb.org/, accessed on 13 February 2023) [https://pubchem.ncbi.nlm.nih.gov/, accessed on 14 February 2023) [http://autodock.scripps.edu/, accessed on 16 February 2023) [The molecular docking approach was employed to analyze the interactions between the bioactive compounds of Tualang honey and the hub genes identified in the study. This computational method is commonly utilized in drug discovery as it enables predictions of binding modes and affinities between receptors and ligands. The crystal structures of the six hub genes—SRC (PDB ID: 1O43), PIK3R1 (PDB ID: 315R), PIK3CA (PDB ID: 6PYS), EGFR (PDB ID: 8A27), PTPN11 (PDB ID: 3B40), and AKT1 (PDB ID: IUNQ) were obtained from the RCSB Protein Data Bank (ry 2023) . The 3D ry 2023) . The comry 2023) , and theAccording to the literature search ,45,46,47A total of 336 target genes of the six bioactive compounds in Tualang honey were identified through the utilization of Swiss Target Prediction and SuperPred databases . AdditioThe potential targets of Tualang honey against atherosclerosis were represented by the Venn diagram . The diaA network representation of the interaction between Tualang honey bioactive compounds and its potential targets for treating atherosclerosis was created using the Cytoscape software and depicted as the compound-target network . This neA PPI network analysis was conducted to demonstrate the interaction between the Tualang honey bioactive compounds and their potential targets in relation to atherosclerosis . This anIn order to gain further insights into the biological processes, cellular components, molecular functions, and pathway underlying the therapeutic effects of Tualang honey bioactive compounds against atherosclerosis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID bioinformatics resources. The results of the GO analysis  revealedAn analysis was performed to explore the interactions between the selected six active compounds of Tualang honey  and six potential target genes  at a molecular level through molecular docking studies. The AutoDock Tools software was utilized to conduct the molecular docking studies and a total of 36 docking results were obtained, 32 of which had binding energies below −5.0 kcal/mol. The docking score is shown in Atherosclerosis is a complex disease characterized by the accumulation of lipid-rich plaques in arterial walls, leading to the narrowing of the arteries which contributes to most cardiovascular diseases . Statin Tualang honey has been shown to possess multiple pharmacological activities that may target different pathways and processes involved in atherosclerosis. Tualang honey has exhibited promising results in animal studies, including its ability to provide cardioprotective effects against oxidative stress  and to lThe results of this study show that 238 potential targets for the treatment of atherosclerosis with Tualang honey bioactive compounds have been identified through network pharmacology analysis. The six hub targets, SRC, PIK3R1, PIK3CA, EGFR, PTPN11, and AKT1, were also discovered in the network diagram as the potential targets. To determine the efficacy of bioactive compounds in Tualang honey as an active agent, it is crucial to consider the pharmacokinetic properties of its components. In this study, only compounds in Tualang honey with an oral bioavailability (OB) higher than 30% and drug-likeness (DL) index greater than 0.18 were considered to be pharmacokinetically active, as they are likely to be absorbed and distributed within the human body. Six compounds—catechin, ethyl oleate, fisetin, hesperetin, kaempferol, and luteolin, were selected from Tualang honey as bioactive compounds that met the requirements of the drug screening and may play a significant role in the treatment of atherosclerosis.The results of the KEGG pathway enrichment analysis demonstrate that, in addition to the enrichment observed in the lipid and atherosclerosis pathways, the main compounds found in Tualang honey also target several other signaling pathways that are closely linked to the development of atherosclerosis. These pathways include the neurotrophin signaling pathway, Ras signaling pathway, EGFR-tyrosine kinase inhibition pathway, prolactin signaling pathway, and sphingolipid signaling pathway. Numerous studies have substantiated the role of the neurotrophin signaling pathway in regulating the response of vascular smooth muscle cells to injury, a key aspect in the pathogenesis of atherosclerosis . FurtherA molecular docking verification was conducted on six key targets of the hub genes and six active ingredients of Tualang honey. The molecular docking results indicate that 5 out of 6 compounds in Tualang honey, which are luteolin, fisetin, hesperitin, catechin, and kaempferol, play an important role in atherosclerotic treatment since these compounds could bind stably to the active pocket of the key target with binding energy lower than −5.0 kcal/mol. These compounds are all flavonoids which have received considerable attention for their potential to positively impact cardiovascular health. This is attributed to their ability in the inhibition of low-density lipoprotein oxidation, enhancement of endothelial-dependent vasodilation, reduction of adhesion molecule and inflammatory marker levels, protection of nitric oxide and endothelial function during oxidative stress, and prevention of platelet aggregation . Some stThe results of the molecular docking studies conducted in this study indicated that a majority of the bioactive components of Tualang honey had a robust binding affinity for PIK3CA. This was evidenced by the fact that luteolin, fisetin, hesperitin, catechin, and kaempferol bound to PIK3CA with binding energy values of −10.67, −10.50, −10.33, −10.28, and −10.08, respectively. The stability of the ligand–receptor binding conformation and the potential for interaction increase as the binding energy decreases . These rNetwork pharmacology and the molecular docking approach were utilized in this study to investigate the potential active components, possible targets, and important biological pathways involved in the treatment of atherosclerosis by the polyphenolic compounds in Tualang honey. The results offer an initial theoretical foundation for further experimental investigation. However, it is worth mentioning that the pharmacological mechanism of Tualang honey bioactive compounds in the treatment of atherosclerosis is based on computational technologies and still requires further validation through pharmacological and clinical studies.In summary, this study utilized a network pharmacology approach to investigate the potential mechanism of Tualang honey bioactive compounds in the treatment of atherosclerosis. Our results indicated that compounds in Tualang honey such as luteolin, fisetin, hesperitin, catechin, and kaempferol may have a crucial impact on atherosclerosis through their effects on key biological targets, including PIK3CA, SRC, P1K3R1, EGFR, PTPN11, and AKT1. Our molecular docking analysis also suggested that these active components of Tualang honey could interact effectively with these targets. While these findings provide a valuable basis for further investigation, it is important to note that further pharmacological and clinical research is required to validate our conclusions.


To characterize potential mechanisms of fisetin on hepatic insulin resistance (IR) using network pharmacology and in vitro validation.Putative targets of fisetin were retrieved from the Traditional Chinese Medicine Systems Pharmacology database, whereas the potential genes of hepatic IR were obtained from GeneCards database. A protein–protein interaction (PPI) network was constructed according to the intersection targets of fisetin and hepatic IR using the Venn diagram. The biological functions and potential pathways related to genes were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Cell experiments were also conducted to further verify the mechanism of fisetin on hepatic IR.A total of 118 potential targets from fisetin were associated with hepatic IR. The areas of nodes and corresponding degree values of TP53, AKT1, TNF, IL6, CASP3, CTNNB1, JUN, SRC, epidermal growth factor receptor (EGFR), and HSP90AA1 were larger and could be easily found in the PPI network. Furthermore, GO analysis revealed that these key targets were significantly involved in multiple biological processes that participated in oxidative stress and serine/threonine kinase activity. KEGG enrichment analysis showed that the PI3K/AKT signaling pathway was a significant pathway involved in hepatic IR. Our in vitro results demonstrated that fisetin treatment increased the expressions of EGFR and IRS in HepG2 and L02 cells under normal or IR conditions. Western blot results revealed that p-AKT/AKT levels were significantly up-regulated, suggesting that fisetin was involved in the PI3K/AKT signaling pathway to regulate insulin signaling.We explored the pharmacological actions and the potential molecular mechanism of fisetin in treating hepatic IR from a holistic perspective. Our study lays a theoretical foundation for the development of fisetin for type 2 diabetes.The online version contains supplementary material available at 10.1186/s12986-023-00770-z. Diabetes is a multifactorial chronic health condition that affects a large portion of the population, and 90–95% of patients with diabetes have type 2 diabetes (T2D) , 2. In-dMetabolic diseases are often diet-related and have a chronic course, and finding dietary components in fruits and vegetables as a way to prevent and treat chronic diseases is gaining widespread attention. Fisetin  based on the similarity of two- or three-dimensional structure of fisetin from the PubChem website [Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform is a unique pharmacology platform of Chinese herbal medicine system and can elucidate drug targets at the systematic level based on the method of systematic pharmacology. The all target genes of fisetin were obtained from the TCMSP database. The targets were also predicted by SwissTargetPrediction database  [Hepatic IR-related targets were obtained from GeneCards database (rds.org) , a powerThrough the Venn diagram, the target data sets of fisetin and hepatic IR were both uploaded, and the intersection was identified as the common gene targets, that is, the potential target genes of fisetin in treating hepatic IR.https://cn.string-db.org) [http://www.ebi.ac.uk/intact) [The anchor protein–protein interaction (PPI) network is composed of different proteins interacting with each other that provide the direction for further exploration of gene function. Protein interactions of the intersection target genes were predicted between fisetin and hepatic IR with the STRING database (-db.org)  and InAc/intact) . Based oThe obtained results were imported into the Cytoscape software, and the plugin CytoHubba was used to calculate and obtain the parameter values of the potential targets, such as degree, betweenness, closeness. The hub genes were screened and identified by the degree value.The Gene Ontology (GO) database includes three categories: biological process (BP), cellular component (CC), and molecular function (MF), which describes the possible molecular functions, the cellular environment, and the involved BPs of gene products. The above intersection targets of fisetin and hepatic IR were analyzed with R software package clusterProfiler (version 3.14.3), by setting the minimum gene set to 5, and the maximum gene set to 5000. The false discovery rate (FDR) < 0.05 was regarded as the screening condition for statistical difference.The Kyoto Encyclopedia of Genes and Genomes (KEGG) database system was used to analyze the metabolic pathways of gene products in cells. The KEGG REST API was used to obtain recent KEGG pathway gene annotations as the background. The above intersection targets of fisetin and hepatic IR were analyzed with R software package clusterProfiler (version 3.14.3) after placing genes into the background sets. The parameter settings were the same as above.The component–target–disease network contributed to intuitively understand and analyze the potential combination of therapeutic mechanisms of components on multiple targets. The fisetin–target–hepatic IR network was constructed using the Cytoscape software. The “node” represented fisetin or hepatic IR or the intersection targets of fisetin and hepatic IR, while “edge” represented the interaction relationships.Fisetin (98%) was purchased from RHAWN . NSC228155 (EGFR activator) was purchased from Sigma Aldrich . Fisetin (50 mM) and NSC228155 (20 mg/mL) were prepared and stored in dimethyl sulfoxide (DMSO), and diluted with culture medium as required. The vehicle control in all experiments was 0.2% (v/v) DMSO. The antibody phospho-Akt Ser473, AKT, EGFR, IRS and GAPDH were purchased from CST (USA).2 incubator. For IR, HepG2 and L02 were exposed to 5 × 10−7 mol/L insulin 24 h to induce cell IR [Human hepatic cells  were obtained from Human Fenghui Biotechnology Co., Ltd and cultured in the DMEM medium , supplemented with 10% fetal bovine serum (FBS) in a 37℃, 5% CO cell IR . To clar−ΔΔCT method. The results were expressed as mean ± SE. See Additional file The RNA from the collected cells was extracted using TRIzol reagents . The RNA samples were pretreated with deoxyribonuclease I  and cDNA was synthesized with SuperScript Kit . Quantitative RT-PCR was amplified using the miScript SYBR Green PCR kit  and ABI PRISM 7700 cycler . Each sample was repeated with ribosomal 18S RNA as an internal control. Folding changes of gene expression were measured using the 2The culture plate was cleaned twice with cold phosphate-buffered solution, and the total proteins were obtained from a cell lysis buffer for western blotting and immunoprecipitation  that contained a cocktail of 1% (v/v) protease inhibitors. Then the proteins were separated by 7.5% SDS-PAGE and then electrophoretically transferred to Immobilon polyvinylidene fluoride . After blocked by 5% (m/v) skim milk at room temperature for 2 h, the PVDF membrane was incubated with phospho-Akt Ser473 (1: 1000), AKT (1: 1000), EGFR (1: 1000), IRS (1: 1000) or GAPDH (1: 1000) antibody at 4 °C overnight. The corresponding horseradish peroxidase was then coupled to the second antibody and incubated at room temperature for 1 h. The protein signal was enhanced with the Omni-ECL™ Femto Light chemiluminescence kit  and then visualized with the ChemiDoc XRS imaging system . Image J was used to quantify protein expression levels.−ΔΔCt method. An independent sample t-test or a one-way ANOVA were used to analyze the significance of the difference. A P-value < 0.05 (double-tailed) was considered statistically significant.All data are expressed as mean ± standard deviation. The GraphPad Prism 8.0 software was used for mapping analysis. Relative gene expression was calculated using the 2Fisetin can improve IR and has a significant hypoglycemic effect. In this study, the molecular targets and the possible mechanisms of fisetin in the treatment of hepatic IR were explored Fig. . A totalThe 134 targets of fisetin were obtained from the TCMSP database and the SwissTargetPrediction database. Based on the GeneCards database, a total of 6,321 targets associated with hepatic IR were obtained. Then, 118 intersection targets for the treatment of hepatic IR were identified by comparing the fisetin targets and the disease targets through the Venn diagram , is depicted in Fig. To clarify the molecular mechanism of fisetin alleviating hepatic IR, a compound–target–disease network was constructed based on top 60 hub genes of fisetin in treating hepatic IR Fig. A. ParticStudies have been found that insulin receptor and EGFR share parallel activation mechanisms for signal transduction across the plasma membrane . EGFR isFor a long time, despite glucose-lowering therapy, 50% of the T2D patients require insulin therapy within 10 years. However, now to be sure, by restoring the normal metabolism of carbohydrates and fats, disease process can be reversed. Fisetin is a flavonoid ingredient as well as an antioxidant. It exists in fruits and vegetables, including strawberries, apples, and cucumbers. It has been found to reduce blood glucose levels in diabetic animals by enhancing glycolysis, inhibiting glycogen production and increasing glycogen storage . It can Insulin resistance and β-cell dysfunction are the main pathophysiological factors leading to T2D. Insulin binds to the insulin receptor , which leads to phosphorylation of IRS, which activates PI3K and AKT and further activates downstream signaling involving the AKT substrate . HoweverGO pathway enrichment analysis revealed that the key intersection targets of IR affected by fisetin were primarily involved in BPs such as oxidative stress and serine/threonine kinase activity, and the location of action was concentrated on the cell membrane. Based on the known molecular mechanism of insulin signaling pathway, we speculated that fisetin may act on proteins located on the cell membrane with similar functions as INSR, thereby activating the downstream insulin metabolic pathway. Among the 20 hub genes screened above, EGFR, as a potential target, is another important central gene in cell membranes, besides INSR, according to bioinformatics analysis based on validated related genes in public disease gene banks , 35. It As observed in the results of KEGG pathway enrichment analysis, the key intersection targets of fisetin acting on hepatic IR was primarily related to the PI3K/AKT signaling pathway. Studies have shown that AKT, as a key protein kinase, is activated by a cascade of the main classical insulin signal required to maintain blood sugar levels in the liver. The AKT kinase is required for insulin regulation of pathways that controls glucose homeostasis throughout the body, including glucose transport in fat cells and muscle, liver glucose gluconeogenesis, and cellular autonomic activation of liver fat production . On the The etiology of IR is complex and multifaceted, involving cellular autonomic mechanisms and interorgan communication. IR in the liver can be traced to defects in insulin receptor levels and may therefore affect all branches of insulin signaling in hepatocytes. This study provides a network pharmacological method to explore the potential molecular mechanism of fisetin in treating hepatic IR. We conclude that fisetin could regulate insulin metabolism signaling pathway through EGFR then IRS activation of the PI3K/AKT signaling pathway or inhibiting oxidative stress and chronic inflammation by AGE-RAGE signaling pathway. Our study lays a theoretical foundation for the development of fisetin for T2D.Additional file 1: Fig. S1 The PPI network of 118 intersection targets with STRING web tool using protein-protein interactions alone. The red lines represent experimentally determined, the blue lines represent from curated databases, and the yellow lines represent textmingAdditional file 2: Fig. S2 The PPI network of 118 intersection targets with InActAdditional file 3: Supplemental Table 1-8.


The exclusive proprietary Chinese medicine Ganweikang (GWK) tablet has been marketed for years in the treatment of chronic hepatitis B (CHB). However, the pharmacodynamic material basis and underlying mechanism of GWK are not completely clear. This study is aimed at investigating the pharmacological mechanism of the GWK tablet in the treatment of CHB. The chemical ingredient information was obtained from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID), and Shanghai Institute of Organic Chemistry of CAS. Ingredients and disease-related targets were defined by a combination of differentially expressed genes from CHB transcriptome data and open-source databases. Target-pathway-target (TPT) network analysis, molecular docking, and chemical composition analysis were adopted to further verify the key targets and corresponding active ingredients of GWK. Eight herbs of GWK were correlated to 330 compounds with positive oral bioavailability, and 199 correlated targets were identified. The TPT network was constructed based on the 146 enriched targets by KEGG pathway analysis, significantly associated with 95 pathways. Twenty-five nonvolatile components and 25 volatile components in GWK were identified in UPLC-QTOF/MS and GC-MS chromatograms. The key active ingredients of GWK include ferulic acid, oleanolic acid, ursolic acid, tormentic acid, 11-deoxyglycyrrhetic acid, dibenzoyl methane, anisaldehyde, wogonin, protocatechuic acid, psoralen, caffeate, dimethylcaffeic acid, vanillin,  Additionally, a series of cell immune-related genes are affected, such as Toll-like receptors (TLRs) and retinoic acid inducible gene-1 (RIG-1), and various pathways are involved, including ROS/NF-κB signaling pathway, JAK-STAT pathway, and p53 pathway [The hepatitis B virus (HBV) is one of the major viral infection problems in worldwide public health . About 3 pathway –6. The a pathway . Other c pathway . As HBV  pathway . The inf pathway .α (PEG-IFNα) or interferonα (IFNα), and other therapies, such as anti-inflammatory, antioxidation, liver protection, and antifibrosis. The first-line drugs entecavir (ETV), tenofovir disoproxil (TDF), and tenofovir alafenamide (TAF) mainly focus on the inhibition of DNA duplication. However, long treatment cycles, drug resistance, and dosage-dependent effects cause heavy economic burdens to the patients and drag down the quality of life [In China, the implementation of the hepatitis B vaccination policy in the 1990s made prominent relief on HBV prevalence; however, CHB is still a public health problem at present . The dom of life .Traditional Chinese medicine (TCM) is a classical medical system existing over thousand years . With thGanweikang (GWK) tablets serve as an exclusive proprietary Chinese medicine that has been marketed for years. The original formulation consisted of eight herbs—Astragali Radix  Bge. var. mongholicus (Bge.) Hsiao) (Huangqi), Verbenae Herba , Stellariae Radix (Stellaria dichotoma L. var. lanceolata Bge.) (Mabiancao), Atractylodis Macrocephalae Rhizoma  (Baizhu), Stellaria dichotoma L. var. lanceolata Bge (Yinchaihu), Saposhnikoviae Radix (Saposhnikovia divaricata (Turcz.) Schischk.) (Fangfeng), Pogostemonis Herba (Pogostemon cablin (Blanco) Benth) (Huoxiang), Forsythiae Fructus (Forsythia suspensa (Thunb.) Vahl) (Lianqiao), and Glycyrrhizae Radix et Rhizoma  (Gancao). As sovereign herbs in the formulation, Huangqi has been proven to have anti-HBV effects and is commonly used in clinics, while Mabiancao is usually combined with other herbs for its anti-inflammatory and antioxidant activities , 25. GlyUltraperformance liquid chromatography coupled with a hybrid quadrupole orthogonal time-of-flight mass spectrometer (UPLC-QTOF-MS) is a technology that has been successfully used for fast and high-resolution determination of nonvolatile components and with the required sensitivity and accuracy. It has become a crucial tool for compositional analysis of complex TCM systems . In addiThe Ganweikang (GWK) tablet consists of eight herb components—Astragali Radix  Bge. var. mongholicus (Bge.) Hsiao) (Huangqi), Verbenae Herba , Stellariae Radix (Stellaria dichotoma L. var. lanceolata Bge.) (Mabiancao), Atractylodis Macrocephalae Rhizoma  (Baizhu), Saposhnikoviae Radix (Saposhnikovia divaricata (Turcz.) Schischk.) (Fangfeng), Pogostemonis Herba (Pogostemon cablin (Blanco) Benth) (Huoxiang), Forsythiae Fructus (Forsythia suspensa (Thunb.) Vahl) (Lianqiao), and Glycyrrhizae Radix et Rhizoma  (Gancao).http://tcmspw.com/tcmsp.php) [http://www.megabionet.org/tcmid) [http://www.organchem.csdb.cn/scdb/default.asp) [http://pubchem.ncbi.nlm.nih.gov) [K(%F) > 0) according to the admetSAR database (http://lmmd.ecust.edu.cn/admetsar2) [The chemical ingredient information of GWK was acquired from three chemical databases: Traditional Chinese Medicine Database and Analysis Platform  , Traditig/tcmid) , and Shault.asp) . Common nih.gov) . Furthermetsar2) .http://sea.bkslab.org/), which is a protein target prediction tool based on chemical similarity. The similarity is computed from the daylight fingerprint and involves information from the MDL Drug Data Report (MDDR) database. The ligand significances are ranked by similarity scores, and a minimum spanning tree is generated. The clusters are mapped merely based on chemical similarities; nevertheless, some of the biological sensitivities are presented as well [Max Tc > 0.57 were selected as candidate targets.The relevant targets of each compound were predicted using the similarity ensemble approach  [http://www.omim.org) [http://www.genome.jp/kegg) [http://www.uniprot.org) [Disease-original targets were partially collected from three databases: Comparative Toxicogenomics Database  , Online mim.org) , and Kyojp/kegg)  by the trot.org)  was usedThe main portion of disease-original targets was obtained from differentially expressed genes (DEGs) of the chronic hepatitis B (CHB) blood sample microarray data. In this data, we selected the transcriptome data of 10 healthy donors and 9 CHB patients from our cohort to do DEG analysis. The cDNA samples were hybridized onto the NimbleGen Homo sapiens 12 × 135K Array . The raw data were processed using NimbleScan 2.5 software. Quantile normalization and background correction were conducted . R languThe molecular mechanism of traditional Chinese medicine (TCM) is difficult to clarify due to its features of complexity and holism; however, network analysis may provide access to this solution. From the perspective of “network” and “system,” network analysis developed as a novel paradigm for the research of holistic medical system . The netA single target is usually associated with multiple pathways, and vice versa, one pathway is related to multiple targets. The target-pathway-target (TPT) network was constructed to delineate the target interactions in terms of related pathways. The novel network analysis approach TPT network turns two-mode “target-pathway” relationships into one-mode “target-target” interactions. Targets related to similar pathways tend to be identified in the same network module. The biological information between targets and pathways was clearly demonstrated in a single network . The netIn graph theory, centrality indicators are commonly identified as the measurements of node importance in a network. In this study, we selected three centrality indicators—degree, closeness, and betweenness—as the input variables in the TPT network centrality analysis. Three centrality indicators were standardized and averaged. Targets with high normalized centrality values were considered key targets. In this study, both the NED method and centrality were implemented to assess the TPT network.A compound-target-pathway (CTP) network was constructed after the key targets were identified through the TPT network. The CTP network was conducted to illustrate the scientific basis of GWK. Based on a comprehensive network analysis, the multiple relationships between compounds, targets, and relative pathways are clearly revealed. The nodes stand for key targets, correlated compounds, and hepatitis B- (HBV-) related pathways. HBV-related pathways were defined according to the KEGG term “Hepatitis B – Homo sapiens (human).” The CTP network was visualized by Cytoscape 3.9.0 software .χ2) test. The robustness of target rank under the NE approach in the TPT network was tested by the paired sample Wilcoxon test. Spearman's test was adopted for the correlation between the NED value in the TPT network and the frequency of CHB literature on each target. The existing literature was retrieved from the PubMed database  using “chronic hepatitis B” AND “target name” in the field of “title/abstract.” Spearman's correlation test was then conducted on the NED values and the literature frequencies.The association between the network module and disease targets was verified using a chi-square (https://www.rcsb.org/) [http://altair.sci.hokudai.ac.jp) [The three-dimensional (3D) structures of key protein targets were downloaded from the Protein Data Bank (PDB) database (sb.org/) . Crystalsb.org/)  and Autosb.org/) . The acti.ac.jp) . The 3D i.ac.jp)  for moleMolecular docking was conducted using AutoDockTools 1.5.6. software with a genetic algorithm and default parameters. Docking mode visualization and file format conversion were implemented using PyMOL 4.4 software and Open Babel 1.1 software.The compositions of volatile and nonvolatile components in GWK were separately detected by ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and gas chromatography-mass spectrometry (GC-MS).Formic acid, methanol, acetonitrile, and leucine-enkephalin of LC-MS grade were purchased from Sigma-Aldrich . Ethanol (HPLC grade) was purchased from RCI Labscan Limited (Thailand). All other reagents and chemicals were of analytical grade. Milli-Q water was prepared using a Milli-Q system . Samples of volatile and nonvolatile extracts of GSW were provided by Jiaheng  Pharmaceutical Technology Co., Ltd.μm)  maintained at 35°C. Elution was performed with a mobile phase of A (0.1% FA in water) and B (0.1% FA in ACN) under a gradient program: 0-25 min, 5%–50% B, and 25-45 min, 50-100% B. The flow rate was 0.4 mL/min, and the injection volume was 5 μL.The nonvolatile components in GWK were analyzed on a Waters ACQUITY-UPLC CLASS system  with an ACQUITY UPLC HSS T3 column  mode, in which parent ions and fragment ion mass spectral data were collected for each detected analyte in a single chromatographic run.Mass spectrometric detection was carried out using a quadrupole time-of-flight (QTOF) SYNAPT G2Si High-Definition Mass Spectrometer  with an electrospray ionization (ESI) interface. The positive and negative ion modes were used with the mass range setting at m/z 50-1500 Da. The optimized ionization conditions were set as follows: capillary voltage of 3.0 kV, source temperature of 120°C, desolvation gas flow of 900 L/h, desolvation temperature of 450°C, cone voltage of 40 V, cone gas flow of 10 L/h, the trap collision energy of 4.0 V, transfer collision energy of 2.0 V, and collision energy ramp of 30 eV to 60 eV. A scan time of 0.2 s with an interval scan time of 0.1 s was used throughout the detection process. Moreover, leucine-enkephalin  solution, at a concentration of 200 ng/μm) of Agilent J&W GC DB-1  by the Thermo Trace 1300 gas chromatography system coupled with Thermo ISQ LT single quadrupole mass spectrometer . The program was as follows: the column temperature was increased with a rate of 10°C/min from 50°C to 80°C (held for 2 min), 5°C/min to 140°C (held for 1 min), and 20°C/min to 280°C (held for 1 min) and then reduced with a rate of 50°C/min to 50°C (held for 2 min). The injector temperature was maintained at 280°C with splitless injection (25 : 1) and helium (purity 99.99%) as the carrier gas at a flow rate of 1.0 mL/min. The mass spectrometer was performed at a scan range of 50-500 m/z with 70 eV ionization energy and applied 280°C and 200°C of ion source and quadruple temperature, respectively. Identification of the detected peaks was conducted by comparing the mass spectrum with the individual peak to those in the NIST MS Search 2.2 standard spectral library.The volatile components in GWK were analyzed on a column , including 497 upregulated genes and 632 downregulated genes  were identified . A TPT network was constructed based on 146 enriched targets (χ2) test  result indicates an association between disease-related targets and module 2 as well. Different modules in the TPT network present diversified functions in the KEGG pathway enrichment analysis . Spearman's test showed a significant correlation between NED values and literature frequencies associated with CHB , which is better than the results of centrality analysis . Thus, CA2, NFKB1, RELA, AKT1, JUN, CA1, CA6, IKBKG, FOS, EP300, CREB1, STAT1, MMP9, CDK2, ABCB1, and ABCG2 were identified as key targets.Eight herbs of GWK were correlated with 972 compounds, of which 330 compounds with positive oral bioavailability and 199 targets were predicted by the SEA approach Table . KEGG pa targets . Five mo targets , while d targets . The chianalysis . HBV andanalysis  and centanalysis . There wThe CTP network was constructed based on HBV-related pathways: “Hepatitis B,” “MAPK signaling pathway,” “NF-kappa B signaling pathway,” “Toll-like receptor signaling pathway,” and “JAK-STAT signaling pathway.” Thirty-three correlated targets and 68 compounds were included . The impcis-ferulic acid   , wogonincal/mol) , oleanolcal/mol) , chromoncal/mol) , 3,4-metcal/mol) , formonecal/mol) , and 7-mcal/mol) . AccordiUltraperformance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and gas chromatography-mass spectrometry (GC-MS) was adopted to analyze the volatile and nonvolatile oil compositions of GWK.The UPLC-QTOF/MS chromatograms of nonvolatile components in GWK were identified under positive ion mode and negative ion mode . Based oα-pinene, (7) sabinene, (8) β-pinene, (9) o-cymene, (10) 1,2,6-dimethylnonane, (11) β-terpinyl acetate, (12) γ-terpinene, (13) (Z)-1,4-dimethylcyclooctane, (14) terpinen-4-ol, (15) 1,3-di-tert-butylbenzene, (16) 4,6-dimethyldodecane, (17) 2-isopropyl-5-methyl-1-heptanol, (18) (5E)-5-icosene, (19) 2-hexyl-1-decanol, (20) 2,4-di-t-butylphenol, (21) 4βH,5α-eremophila-1(10),11-diene, (22) γ-elemene, (23) trichloroacetic acid, (24) atractylon, and (25) 2,2-dimethoxy-2-phenylacetophenone. The detailed component information of UPLC-QTOF/MS and GC-MS is shown in the supplementary file  usually present the characteristics of multicomponents, multitargets, and comprehensive curative effects, of which the mechanisms are difficult to completely clarify by canonical pharmaceutical research methods. However, network analysis turns out to be an effective approach to investigate the pharmacological mechanism complexities of TCM , 52. Theβ-amyrenyl acetate, formonentin, aristololactam IIIa, and 7-methoxy-2-methyl isoflavone. Detailed information on the key activate ingredients and targets is listed in Tables http://www.swissadme.ch) [http://www.genecards.org) [In this study, we focused on the molecular mechanism of the exclusive proprietary Chinese medicine Ganweikang (GWK) tablet in the treatment of chronic hepatitis B (CHB). Based on network analysis, the key targets were identified as CA2, NFKB1, RELA, AKT1, JUN, CA1, CA6, IKBKG, FOS, EP300, CREB1, STAT1, MMP9, CDK2, ABCB1, and ABCG2, and the key activate ingredients were ferulic acid, oleanolic acid, ursolic acid, tormentic acid, 11-deoxyglycyrrhetic acid, dibenzoyl methane, anisaldehyde, wogonin, protocatechuic acid, psoralen, caffeate, dimethylcaffeic acid, vanillin, adme.ch) . The infrds.org) .β/Smad signaling pathway [β-induced apoptosis in Hep3B cells. UA almost blocked all the tumorigenic activities mediated by HBx, while OA presented partial inhibitory effects. The liver protection effects of UA and OA were imposed through the activation of the MAPK signaling pathway in western blotting results [Most of the key active ingredients in GWK have been reported to be associated with CHB, HBV, or hepatopathy, mainly correlated with antivirus and liver protection. Ferulic acid, which exists as a phenolic acid in nature, has been reported to have liver protection effects. The antioxidant and anti-inflammatory effects of ferulic acid were revealed to protect acute hepatocyte injury induced by carbon tetrachloride (CCl4) in mice via NOX4/P22PHOx/ROS-JNK/P38 MAPK signaling pathway . Also, f pathway . The nat results . Dibenzo results . Other c results  and 11-d results .κB, and MAPK, the antiviral effects of wogonin have been widely reported and studied. Besides HBV, other viruses, such as the replication of herpes simplex virus types 1 and 2 (HSV-1 and HSV-2), were sensitive to wogonin as well [α and HNF1α are members of the hepatocyte nuclear factor (HNF) family, which play important roles in the transcription of HBV-related biological targets. Previous studies found that PCA suppressed HBV antigen secretion and HBV DNA replication in HepG2.2.15 cells. Anti-HBV mechanism studies of PCA indicated that PCA inhibited HBV replication by activating the ERK1/2 pathway and subsequently downregulating HNF4α and HNF1α in HepG2.2.15 cells [With regard to active antivirus ingredients, the natural flavonoid wogonin has been reported to have anti-inflammation, antitumor, antivirus, neuroprotection, and antianxiety effects. Wogonin displayed inhibiting actions on HBV surface antigen (HBsAg) secretion and directly reduced HBV levels in human HBV-transfected HepG2.2.15 liver cells and mouse model . Involve as well . Protoca15 cells . Furan c15 cells . Formono15 cells  and nona15 cells . The ant15 cells . Also, s15 cells , 69.β-amyrenyl acetate with RELA , artemisinin with ABCB1 , and scopoletin with CA6  could be referred to Figure Seven of the predicted active ingredients of GWK in the treatment of CHB were also detected in the chemical profiling. The representative docking affinities, such as 3,4-methylenedioxy with CDK2, formononetin with ABCB1, and 7-methoxy-2-methylisoflavone with CA1, were stronger than the positive controls with the same active pockets. The binding modes are demonstrated in Figures κB signaling pathways. Moreover, some of the key targets in our study, such as the AP1 transcription factors JUN and FOS, were associated with the molecular mechanism of HBx protein in HBV infection. HBx protein, acting as a transactivator of HBV X antigen (HBxAg), not only interacts with nuclear transcription factors such as C-Jun, NF-κB, and AP-1 to influence cellular activities at the transcription level but also participates in the regulation of multiple intracytoplasmic signaling pathways like the C-RAF, P53, and JAK-Stat pathways [These results suggest that GWK may exert its anti-CHB effects through both antiviral protection and liver protection with multitargets CA2, NFKB1, RELA, AKT1, JUN, CA1, CA6, IKBKG, FOS, EP300, CREB1, STAT1, MMP9, CDK2, ABCB1, and ABCG2 and multipathways such as MAPK and NF-pathways , 71. TheCertainly, several limitations should be noted in this study. First, the protein target prediction tool SEA is based on the chemical similarity of the two-dimensional (2D) structure of compounds (SMILES information), which means that the isometric compounds would be treated the same, and duplicate targets would be predicted. Second, further experimental verifications should be conducted based on the key active ingredients and targets in GWK.β-amyrenyl acetate, formonentin, aristololactam IIIa, and 7-methoxy-2-methyl isoflavone. Using computational and experimental methodologies, this study provides a scientific basis for further mechanism elucidation of GWK tablets in the treatment of CHB.In conclusion, GWK may exert anti-CHB effects through antiviral and liver protection. The key targets include CA2, NFKB1, RELA, AKT1, JUN, CA1, CA6, IKBKG, FOS, EP300, CREB1, STAT1, MMP9, CDK2, ABCB1, and ABCG2. The related key active ingredients are ferulic acid, oleanolic acid, ursolic acid, tormentic acid, 11-deoxyglycyrrhetic acid, dibenzoyl methane, anisaldehyde, wogonin, protocatechuic acid, psoralen, caffeate, dimethylcaffeic acid, vanillin,


Use network pharmacology combined with molecular docking to study the effects of Simiao-Yongan Decoction (SMYAD) intervenes in Knee Osteoarthritis (KOA) related targets and signaling pathways, and explores the molecular mechanism of SMYAD in treating KOA. The active ingredients and targets of SMYAD, which concluded 4 traditional Chinese medicines, were screened in TCMSP, and the related gene targets of KOA were screened in the disease databases GeneCards, MalaCards, DisGeNET, and Comparative Toxicogenomics Database, and their intersection data were obtained after integration. And used Cytoscape 3.9.1, the software topologies the network diagram of “compound—drug-active ingredient-target protein-disease.” Obtains the protein-protein interaction network diagram through STRING, and enriches and analyzes the obtained core targets. Carry out molecular docking matching verification on the main active ingredients and key targets of the drug. 106 active ingredients and 175 targets were screened from SMYAD to intervene in KOA, 36 core targets were obtained through protein-protein interaction screening, and 10 key targets played an important role. The enrichment results showed that the biological process of gene ontology mainly involved positive regulation of gene expression, negative regulation of apoptosis process, and positive regulation of apoptosis process. KEGG signaling pathway mainly involves AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, hypoxia-inducible factor-1 signaling pathway, IL-17 signaling pathway. The pathway of Reactome mainly involves interleukin-4 and interleukin-13 signaling, cytokine signaling in immune system, immune system, apoptosis. Molecular docking showed that the mainly effective components of SMYAD can fully combine with TNF, IL1B, IL6, and CASP3. The results show that the main active ingredients and potential mechanism of action of SMYAD in the treatment of KOA have the characteristics of multiple targets and multiple pathways, which provides ideas and basis for further in-depth exploration of its specific mechanism. After entering the 21st century, because of the increase in life expectancy and increase in body mass index due to the increase of population of old people, the prevalence of KOA has increased significantly and tended to be younger. Currently, KOA accounts for 85% of the global burden of osteoarthritis, and the incidence of symptomatic KOA is 8.1%. The main clinical manifestations of KOA are inconvenient movement of the limbs, difficulty in moving, and pain and swelling around the joints, which are more obvious after waking up in the morning, and can lead to paralysis in severe cases in the later stage. Inflammation is the most common cause of joint pain and patient disability, and no effective treatment has been found to delay the progression of KOA, so most patients will undergo total joint replacement to relieve symptoms and improve physical performance, but it is expensive and traumatic large, slow recovery in the later period.–6 Therefore, it is crucial to understand the pathogenic process of KOA and the cellular and molecular mechanisms involved. The current process of osteoarthritis of the knee contains 2 basic aspects: the action of different joint elements and posttraumatic osteoarthritis formation.,8 On one hand, in nontraumatic circumstances, the development of knee osteoarthritis is a degenerative pathology associated with aging. Injuries to the components of the knee joint, such as synovitis, ligament, meniscus, free bodies, popliteal cysts, and chondromalacia of the patella, can also result in the development of knee osteoarthritis. In addition, knee osteoarthritis can be brought on by putting on too much weight, adopting bad gait patterns, squatting for extended periods of time, and exposing the knee joint to the cold.,9 On the other hand, posttraumatic osteoarthritis development, a progressive form of arthritis in the knee following trauma, is a significant contributor to osteoarthritis of the knee. It has clinical symptoms with osteoarthritis, but it also has a clear history of trauma, such as transarticular fractures.,11 At present, the most common manifestation of KOA is pain, and the effective drugs for pain relief are nonsteroidal antiinflammatory drugs. However, such drugs are only suitable for short-term pain relief, and cannot control the wear and tear of articular cartilage, and cannot solve the fundamental problem. Moreover, such drugs may cause gastrointestinal discomfort such as nausea and vomiting, and increase the risk of gastrointestinal bleeding. Therefore, it is imperative to develop a drug that can maintain the curative effect of the drug and reduce side effects as much as possible.Knee osteoarthritis (KOA) is the most common joint inflammatory disease in orthopedics. The main feature is the gradual degeneration and loss of articular cartilage, accompanied by changes in joint structure and function. and there have been clinical research reports, SMYAD has a definite curative effect on gouty arthritis, but there is no relevant research on the active substances and specific molecular mechanisms of SMYAD.Traditional Chinese medicine (TCM) attributes KOA to the categories of “Bi Syndrome” and “Bone Bi.” TCM has a long history of treating KOA, and the basic theoretical system of “arthromyopathy” was established as early as in the “Huangdi Neijing.” TCM has the advantages of definite curative effect and high safety in the treatment of KOA. It has a good clinical effect on the treatment of KOA and plays a vital role in the diagnosis and treatment of KOA. Many doctors in modern times use Simiao-Yongan Decoction (SMYAD) to treat arthritis. The results of modern pharmacological research also show that SMYAD has antiinflammatory, analgesic, and microcirculation improvement functions, In this study, using the methods of network pharmacology and molecular docking, starting from the chemical substance basis of SMYAD, the important targets and potential molecular mechanisms of drug regulation on KOA are more systematically and deeply explored, to better serve the treatment of KOA. It provides a theoretical reference for the clinical treatment of KOA and provides new ideas for the development of new drugs for the treatment of KOA.Network pharmacology is a new discipline formed by the fusion of pharmacology with multidisciplinary methods, which can comprehensively reflect the molecular mechanism of action of drugs on disease networks.http://tcmspw.com/tcmsp.php) database. Taking the oral bioavailability ≥30% and drug similarity (DL) ≥0.18 as the standard, the effective active ingredients of the drug were screened, and the targets corresponding to the active ingredients were queried, targets to obtain effective targets. In GeneCards (https://www.genecards.org/), MalaCards , DisGeNet database (https://www.disgenet.org/), and Comparative Toxicogenomics Database  obtain KOA-related targets.,21 The target of the obtained compound was intersected with the KOA target, and the visual connection of “compound-drug-active ingredient-target protein-disease” was constructed. The raw data used in our investigation can be replicated and were taken from the relevant databases. Our tests’ outcomes are essentially unaffected by any disparities in the data, despite the fact that the databases are updated dynamically.Jin Yinhua (or honeysuckle), Xuan Shen (or Genuine Ginseng), Dang Gui (or angelica), and Gan Cao (or licorice) were collected through TCMSP (http://string-db.org/), and the species was restricted to “Homo sapiens” with a confidence value >0.4. The PPI network was constructed by Cytoscape software (version 3.9.1). In addition, using the plug-in CytoHubba algorithm of Cytoscape software, the targets with a maximum degree greater than or equal to half of the maximum degree were selected as core targets, and the top 10 targets in descending order of degree were selected as key targets.Protein-protein interactions underlie most BPs in living cells and are critical for understanding cell physiology in both normal and disease states. In this study, the protein-protein interaction (PPI) network analysis was performed on the obtained intersection gene sets using the string database (https://hiplot.com.cn/) and Reactome database (https://reactome.org/),,25 and conduct pathways with humans as the research object enrichment analysis. Among them, Gene Ontology (GO) analysis includes 3 categories: biological process (BP), cellular component (CC), and molecular function, and according to the P. adjust. The top 20 enriched pathways of Reactome were selected according to the P. adjust. To select the top 20 annotated by KEGG, according to P value. The results are displayed in column charts, bubble charts, and network correlation diagrams.Import the obtained intersection targets into the HiPlot database (www.rcsb.org/). Add all hydrogen atoms in AutodockTools 1.5.6 to open, calculate the Gasteiger charge, combine nonpolar hydrogen, define it as an acceptor, and save it as a pdbqt file. Determine the coordinates and box size of the Vina molecular docking, set the exhaustiveness of the parameters to 15, and take the default values for other parameters. Autodockvina 1.1.2 was used for semiflexible docking, and the conformation with the best affinity was selected as the final docking conformation.The 3D structure of the proposed docking target in mol2 format from the Pubchem database, open the small ligand molecule with AutodockTools 1.5.6, perform hydrogenation, charging, detection of the ligand root, search and define rotatable bonds, and then save it as a pdbqt file. Download the core 3-dimensional structure of the target protein as a docking protein from the RCSB protein database . The keyword “Knee Osteoarthritis” was searched in the disease database, and the corresponding targets were obtained after screening, and the union was taken, and the KOA-related targets were obtained after deleting duplicate items . Among them, BP mainly involves positive regulation of gene expression, positive regulation of transcription, DNA-templated, positive regulation of pri-miRNA transcription from RNA polymerase II promoter, response to drug, negative regulation of apoptotic process, positive regulation of apoptotic process, lipopolysaccharide-mediated signaling pathway, positive regulation of transcription from RNA polymerase II promoter, response to estradiol, positive regulation of smooth muscle cell proliferation. CC mainly involves macromolecular complex, extracellular space, Caveola, transcription factor complex, nucleoplasm, cytosol, cytoplasm, nucleus, mitochondrion, extracellular region. MM mainly includes enzyme binding, transcription factor binding, identical protein binding, protein phosphatase binding, protein kinase binding, RNA polymerase II sequence-specific DNA binding transcription factor binding, transcription cofactor binding, transcription factor activity, sequence-specific DNA binding, transcript coactivator binding, histone deacetylase binding , and the activation of RAGE is involved in key signaling pathways related to KOA. On the one hand, AGEs induce inflammation by activating NF-κB and MAPK in various cell types including chondrocytes. On the other hand, AGEs may increase the levels of PGE and NO through the MAPK pathway to enhance the inflammatory response of OA chondrocytes. In addition, AGEs stimulated endoplasmic reticulum stress in human chondrocytes through RAGE, leading to phosphorylation of eIF2α and p38 MAP kinases and activation of NF-κB, which in turn increased COX-2 expression and PGE2 production. IL-17 is one of many inflammatory cytokines, mainly produced by T cells. The cells affected by IL-17 in joints are mainly chondrocytes. Imaging studies have shown that images of OA lesions are positively correlated with elevated levels of IL-17 in serum and synovial fluid. In addition, IL-17 can induce chondrocytes and synovial fibroblasts to release chemokines and induce chondrocytes to synthesize IL-1, thereby promoting the pathological progression of OA. Hypoxia-inducible factor (HIF) is a transcription factor and a key regulator of cellular responses to hypoxia. HIF-1 is an important factor in maintaining chondrocyte homeostasis and allowing cell differentiation. HIF-1 is a heterodimer composed of 2 subunits: HIF-1α and HIF-1. A study showed that HIF-1α plays an important role in the prevention of OA. HIF-1α downregulates MMP-13 by inhibiting β-catenin transcriptional activity in chondrocytes, thereby alleviating OA development. Lack of HIF-1α exacerbates cartilage catabolism. In addition, HIF-1 is also involved in the process of KOA synovial fibrosis. Molecular docking showed that the important active compounds quercetin, luteolin, kaempferol, and beta-sitosterol could fully combine with key proteins TNF, IL6, IL1B, and CAPSE3. This may be the key molecular mechanism of SMYAD in treating KOA.One hundred twenty active ingredients of SMYAD were screened out from the TCMSP database. The top 8 active ingredients ranked according to degree value are quercetin, luteolin, kaempferol, beta-sitosterol, naringenin, 7-methoxy-2-methyl isoflavone, formononetin, licochalcone a. Therefore, it is speculated that these may be the key active ingredients of SMYAD in the treatment of KOA. Quercetin is a polyhydroxy flavonoid compound, which has biological activities such as antivirus, antitumor, and liver protection.Even while our research has clearly shown the beneficial efficacy and mechanism of SMYAD in the treatment of KOA, our medication is safer for clinical use when compared to the side effects of conventional NSAIDs, such as peptic ulcers. Nonetheless, there are still certain issues that require improvement. First of all, SMYAD is a complex formula with a wide range of effects from each of its constituent parts. Therefore, a review of the drug’s plasmapheresis analysis is necessary to further elucidate the effects of each component. The few active chemicals we detected may not be sufficient. Second, until date, we have only verified theoretical partial molecular docking in a thorough and scientific manner. We plan to enhance the associated experiments in the future.In summary, this study preliminarily verified and predicted the molecular mechanism of SMYAD in the treatment of KOA, and found that the active ingredients and mechanism of SMYAD in the treatment of KOA present the characteristics of multi-component, multi-target, and multi - pathway.We would like to thank Sichuan Vocational College of Health and Rehabilitation for helpful discussions on topics relevant to this work.Conceptualization: Lan Chen.Project administration: Junwei Wang.Software: Haixu Chen.Validation: Xiangyu Pan.Writing – original draft: Ying Wang.