scholarly journals Mechanism of Fei-Xian Formula in the Treatment of Pulmonary Fibrosis on the Basis of Network Pharmacology Analysis Combined with Molecular Docking Validation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiao-Li Chen ◽  
Cheng Tang ◽  
Qing-Ling Xiao ◽  
Zhong-Hua Pang ◽  
Dan-Dan Zhou ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virus Infection ◽  
Pulmonary Fibrosis ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Core Network ◽  
Active Ingredients ◽  
Molecular Docking Study ◽  
The Core ◽  
Target Network

Objective. This study aimed to clarify the mechanism of Fei-Xian formula (FXF) in the treatment of pulmonary fibrosis based on network pharmacology analysis combined with molecular docking validation. Methods. Firstly, ingredients in FXF with pharmacological activities, together with specific targets, were identified based on the BATMA-TCM and TCMSP databases. Then, targets associated with pulmonary fibrosis, which included pathogenic targets as well as those known therapeutic targets, were screened against the CTD, TTD, GeneCards, and DisGeNet databases. Later, Cytoscape was employed to construct a candidate component-target network of FXF for treating pulmonary fibrosis. In addition, for nodes within the as-constructed network, topological parameters were calculated using CytoHubba plug-in, and the degree value (twice as high as the median degree value for all the nodes) was adopted to select core components as well as core targets of FXF for treating pulmonary fibrosis, which were subsequently utilized for constructing the core network. Furthermore, molecular docking study was carried out on those core active ingredients together with the core targets using AutoDock Vina for verifying results of network pharmacology analysis. At last, OmicShare was employed for enrichment analysis of the core targets. Results. Altogether 12 active ingredients along with 13 core targets were identified from our constructed core component-target network of FXF for the treatment of pulmonary fibrosis. As revealed by enrichment analysis, the 13 core targets mostly concentrated in regulating biological functions, like response to external stimulus (from oxidative stress, radiation, UV, chemical substances, and virus infection), apoptosis, cell cycle, aging, immune process, and protein metabolism. In addition, several pathways, like IL-17, AGE-RAGE, TNF, HIF-1, PI3K-AKT, NOD-like receptor, T/B cell receptor, and virus infection-related pathways, exerted vital parts in FXF in the treatment of pulmonary fibrosis. Conclusions. FXF can treat pulmonary fibrosis through a “multicomponent, multitarget, and multipathway” mean. Findings in this work lay foundation for further exploration of the FXF mechanism in the treatment of pulmonary fibrosis.

scholarly journals Mechanism of Action of Bu-Fei-Yi-Shen Formula in Treating Chronic Obstructive Pulmonary Disease Based on Network Pharmacology Analysis and Molecular Docking Validation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Longchuan Wu ◽  
Yu Chen ◽  
Jiao Yi ◽  
Yi Zhuang ◽  
Lei Cui ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Mechanism Of Action ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Chronic Obstructive ◽  
Biological Functions ◽  
Active Ingredients ◽  
Obstructive Pulmonary Disease ◽  
The Core ◽  
Target Network

Objective. To explore the mechanism of action of Bu-Fei-Yi-Shen formula (BFYSF) in treating chronic obstructive pulmonary disease (COPD) based on network pharmacology analysis and molecular docking validation. Methods. First of all, the pharmacologically active ingredients and corresponding targets in BFYSF were mined by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the analysis platform, and literature review. Subsequently, the COPD-related targets (including the pathogenic targets and known therapeutic targets) were identified through the TTD, CTD, DisGeNet, and GeneCards databases. Thereafter, Cytoscape was employed to construct the candidate component-target network of BFYSF in the treatment of COPD. Moreover, the cytoHubba plug-in was utilized to calculate the topological parameters of nodes in the network; then, the core components and core targets of BFYSF in the treatment of COPD were extracted according to the degree value (greater than or equal to the median degree values for all nodes in the network) to construct the core network. Further, the Autodock vina software was adopted for molecular docking study on the core active ingredients and core targets, so as to verify the above-mentioned network pharmacology analysis results. Finally, the Omicshare database was applied in enrichment analysis of the biological functions of core targets and the involved signaling pathways. Results. In the core component-target network of BFYSF in treating COPD, there were 30 active ingredients and 37 core targets. Enrichment analysis suggested that these 37 core targets were mainly involved in the regulation of biological functions, such as response to biological and chemical stimuli, multiple cellular life processes, immunity, and metabolism. Besides, multiple pathways, including IL-17, Toll-like receptor (TLR), TNF, and HIF-1, played certain roles in the effect of BFYSF on treating COPD. Conclusion. BFYSF can treat COPD through the multicomponent, multitarget, and multipathway synergistic network, which provides basic data for intensively exploring the mechanism of action of BFYSF in treating COPD.

scholarly journals The Potential Effect of Rhizoma coptidis on Polycystic Ovary Syndrome Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liyun Duan ◽  
De Jin ◽  
Xuedong An ◽  
Yuehong Zhang ◽  
Shenghui Zhao ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Polycystic Ovary ◽  
Enrichment Analysis ◽  
Potential Effect ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Core Network ◽  
Active Ingredients ◽  
Rhizoma Coptidis ◽  
The Core

Background. Rhizoma coptidis (RC) showed a significant effect on PCOS, but its mechanism in PCOS remains unclear. Methods. The components of RC were searched by TCMSP. The Smiles number of the active ingredients was queried through PubChem, and the predicted targets were obtained from the SwissTargetPrediction database. The DrugBank, GeneCards, and DisGeNET databases were retrieved to acquire the related targets of PCOS. Then, the network of compound-target was constructed. The core targets were analyzed using protein-protein interaction (PPI) analysis, and the binding activities were verified by molecular docking. The enriched pathways of key targets were examined by GO and KEGG. Results. 13 components and 250 targets of RC on PCOS were screened. The core network was filtered based on topological parameters, and the key components were palmatine, berberine, berberrubine, quercetin, and epiberberine. The key targets included DRD2, SLC6A4, CDK2, DPP4, ESR1, AKT2, PGR, and AKT1. Molecular docking displayed that the active ingredients of RC had good binding activities with potential targets of PCOS. After enrichment analysis, 30 functional pathways were obtained, including neuroactive ligand-receptor interaction, dopaminergic synapse, and cAMP signaling pathway. Conclusion. In summary, this study clarified the potential effect of RC on PCOS, which is helpful to provide references for clinical practice. It is also conducive to the secondary development of RC and its monomer components.

scholarly journals Network pharmacology and molecular docking study on the active ingredients of qidengmingmu capsule for the treatment of diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mingxu Zhang ◽  
Jiawei Yang ◽  
Xiulan Zhao ◽  
Ying Zhao ◽  
Siquan Zhu
Keyword(s):  
Diabetic Retinopathy ◽  
Molecular Docking ◽  
Molecular Mechanisms ◽  
Hypoxia Inducible Factor ◽  
Enrichment Analysis ◽  
Docking Study ◽  
Network Pharmacology ◽  
Biological Processes ◽  
Active Ingredients ◽  
Molecular Docking Study

AbstractDiabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Qidengmingmu Capsule (QC) is a Chinese patent medicine used to treat DR, but the molecular mechanism of the treatment remains unknown. In this study, we identified and validated potential molecular mechanisms involved in the treatment of DR with QC via network pharmacology and molecular docking methods. The results of Ingredient-DR Target Network showed that 134 common targets and 20 active ingredients of QC were involved. According to the results of enrichment analysis, 2307 biological processes and 40 pathways were related to the treatment effects. Most of these processes and pathways were important for cell survival and were associated with many key factors in DR, such as vascular endothelial growth factor-A (VEGFA), hypoxia-inducible factor-1A (HIF-1Α), and tumor necrosis factor-α (TNFα). Based on the results of the PPI network and KEGG enrichment analyses, we selected AKT1, HIF-1α, VEGFA, TNFα and their corresponding active ingredients for molecular docking. According to the molecular docking results, several key targets of DR (including AKT1, HIF-1α, VEGFA, and TNFα) can form stable bonds with the corresponding active ingredients of QC. In conclusion, through network pharmacology methods, we found that potential biological mechanisms involved in the alleviation of DR by QC are related to multiple biological processes and signaling pathways. The molecular docking results also provide us with sound directions for further experiments.

scholarly journals Exploring Molecular Mechanism of Traditional Chinese Medicine Euphorbiae Semen on Reversing of Multidrug Resistance in Leukemia Based on Network Pharmacology Strategy and Molecular Docking Technology

2020 ◽  
Author(s):  
Xiao Song ◽  
Fei Guo ◽  
Xiao-Chen Sun ◽  
Shu-Yue Wang ◽  
Yao-Hui Yuan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Biological Functions ◽  
Active Compounds ◽  
The Core ◽  
The World ◽  
Target Network ◽  
Compound Target

Abstract Background: Leukemia was listed by the World Health Organization as one of the five most intractable diseases in the world. The multi-drug resistance (MDR) of leukemia cells limits the efficacy of anti-tumor drugs and is the major reason for the chemotherapy failure and recurrence of leukemia chemotherapy. Some studies have shown that Euphorbiae semen (ES) possesses the characteristics of new therapeutic drugs for MDR. However, the molecular mechanisms and active compounds have not yet been fully clarified. Therefore, there is a need for explore its active compounds and demonstrate its mechanisms through network pharmacology and molecular docking technology.Method: First, the TCMSP database was searched and screened the active compounds of the ES, supplemented with compounds verified by literature, so as to further identify the core compounds in the active ingredient. Simultaneously, the TCMSP and Swiss database were searched to the targets of active compounds, and the targets of reverses leukemia multidrug resistance (RL-MDR) were screened in the relevant databases, such as GeneCards and DrugBank. Then, the targets of active compounds were intersected with RL-MDR targets to obtain potential targets of ES acting on MDR. The compound–target network was constructed by Cytoscape. The target protein–protein interaction network was built using STRING and Cytoscape database. Second, the R language and DAVID database were used to analyse Gene Ontology (GO) biological functions analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways enrichment. Finally, molecular docking method was utilized to investigate the binding activity between the core targets and the active compounds of ES.Results: Compound–target network mainly contained 22 compounds and 81 corresponding targets. Finally, seven components in ES were selected and 10 core targets were identified; Key targets contained JUN, CASP3, MAOA, AR, PPARG, DRD2, ADRA2A, CHRM2, PTGS2 and MAPK14. GO enrichment analysis indicated the main biological functions of potential genes of ES in the treatment of MDR. KEGG pathway enrichment analysis showed the main pathways, mainly including apoptosis, pathways in cancer, p53 signaling pathway, VEGF signaling pathway, TNF signaling pathway and PI3K–Akt signaling pathway. Finally, we chose the top 10 common targets for molecular docking with the 7 active compounds of ES. The results of molecular docking indicated that the compounds of ES, which had good affinity with targets. Conclusion: The molecular mechanism of ES in the treatment of MDR showed the synergistic reaction of multi-compound, multi-target, and multi-pathway of traditional Chinese medicine, which provided ideas for further clinical research.

scholarly journals A Network Pharmacology Approach and Molecular Docking Technology to Uncover the Potential Molecular Mechanism of Astragalus -Scorpion on Prostate Cancer

2021 ◽  
Author(s):  
Litong Wu ◽  
Ying Chen ◽  
Mingjing Chen ◽  
Yueqin Yang ◽  
Zuzhao Che ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Docking ◽  
Active Ingredient ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Disease Network ◽  
The Core ◽  
Target Disease ◽  
The Common

Abstract Objective: To investigate the molecular mechanism of Astragalus-Scorpion in the treatment of prostate cancer by network pharmacology and molecular docking technology.Methods: Using TCMSP, BATMAN-TCM, TCMID and Swiss TargetPrediction Databases to retrieve the active ingredients and corresponding targets of Astragalus-Scorpion. The targets related to prostate cancer were retrieved through GeneCards, so as to obtain the common targets of Astragalus-Scorpion and prostate cancer. The common targets were input into the STRING database for protein interaction analysis. Cytoscape software was used to construct the active “ingredient-target-disease” network, and GO and KEGG enrichment analysis were performed on the common targets. To screen the core ingredients and targets that play therapeutic roles, Autodock software was used for molecular docking verification. Results: 27 active ingredients, 340 potential targets related to active ingredients, 898 targets related to disease and 122 common targets were screened from Astragalus-Scorpion totally. The core targets of PPI network were JUN, AKT1, IL6, MAPK1 and RELA, while the core active ingredients in the active ingredient-target-disease network were quercetin, kaempferol, formononetin, 7-o-methylisomucronulatol and calycosin.762 GO entries and 154 pathways were obtained by enrichment analysis totally. The molecular docking results showed that quercetin binds to AKT1 and RELA, kaempferol to AKT1, and formononetin to RELA, all of which were stable. Conclusion: Quercetin, kaempferol and others in the Astragalus-Scorpion can regulate multiple signaling pathways such as PI3K-Akt signaling pathway by binding to targets such as AKT1 related to prostate cancer, inhibit the proliferation of tumor to play a role for prostate cancer.

scholarly journals Exploring the Biological Mechanism of Huang Yam in Treating Tumors and Preventing Antitumor Drug-Induced Cardiotoxicity Using Network Pharmacology and Molecular Docking Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hui Zhang ◽  
Wenchao Dan ◽  
Qingyong He ◽  
Jianbo Guo ◽  
Shuang Dai ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Antitumor Drugs ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Ingredients ◽  
Drug Induced ◽  
Anticancer Properties ◽  
The Core

Drugs for the treatment of tumors could result in cardiotoxicity and cardiovascular diseases. We aimed to explore the anticancer properties of Huang yam as well as its cardioprotective properties using network pharmacology and molecular docking technology. The cardiovascular targets of the major chemical components of Huang yam were obtained from the following databases: TCMSP, ETCM, and BATMAN-TCM. The active ingredients of Huang yam were obtained from SwissADME. The cardiovascular targets of antitumor drugs were obtained using GeneCards, OMIM, DrugBank, DisGeNET, and SwissTargetPrediction databases. The drug-disease intersection genes were used to construct a drug-compound-target network using Cytoscape 3.7.1. A protein-protein interaction network was constructed using Cytoscape’s BisoGenet, and the core targets of Huang yam were screened to determine their antitumor properties and identify the cardiovascular targets based on topological parameters. Potential targets were imported into the Metascape platform for GO and KEGG analysis. The results were saved and visualized using R software. The components with higher median values in the network were molecularly docked with the core targets. The network contained 10 compounds, including daucosterol, delusive, dioxin, panthogenin-B, and 124 targets, such as TP53, RPS27A, and UBC. The GO function enrichment analysis showed that there were 478 items in total. KEGG enrichment analysis showed a total of 140 main pathways associated with abnormal transcription of cancer, PI3K-Akt signaling pathway, cell cycle, cancer pathway, ubiquitination-mediated proteolysis, and other pathways. Molecular docking results showed that daucosterol, delusive, dioxin, and panthogenin-B had the highest affinity for TP53, RPS27A, and UBC. The treatment of diseases using traditional Chinese medicine encompasses multiple active ingredients, targets, and pathways. Huang yam has the potential to treat cardiotoxicity caused by antitumor drugs.

scholarly journals A Network Pharmacology Approach and Molecular Docking Technology to Uncover the Potential Molecular Mechanism of Astragalus -Scorpion on Prostate Cancer

2021 ◽  
Author(s):  
Litong Wu ◽  
Ying Chen ◽  
Minjing Chen ◽  
Yueqin Yang ◽  
Zuzhao Che ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Molecular Docking ◽  
Active Ingredient ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Disease Network ◽  
The Core ◽  
Target Disease ◽  
The Common

Abstract Objective: To investigate the molecular mechanism of Astragalus-Scorpion in the treatment of prostate cancer by network pharmacology and molecular docking technology. Methods: Using TCMSP, BATMAN-TCM, TCMID and Swiss TargetPrediction Databases to retrieve the active ingredients and corresponding targets of Astragalus-Scorpion. The targets related to prostate cancer were retrieved through GeneCards, so as to obtain the common targets of Astragalus-Scorpion and prostate cancer. The common targets were input into the STRING database for protein interaction analysis. Cytoscape software was used to construct the active “ingredient-target-disease” network, and GO and KEGG enrichment analysis were performed on the common targets. To screen the core ingredients and targets that play therapeutic roles, Autodock software was used for molecular docking verification. Results: 27 active ingredients, 340 potential targets related to active ingredients, 898 targets related to disease and 122 common targets were screened from Astragalus-Scorpion totally. The core targets of PPI network were JUN, AKT1, IL6, MAPK1 and RELA, while the core active ingredients in the active ingredient-target-disease network were quercetin, kaempferol, formononetin, 7-o-methylisomucronulatol and calycosin.762 GO entries and 154 pathways were obtained by enrichment analysis totally. The molecular docking results showed that quercetin binds to AKT1 and RELA, kaempferol to AKT1, and formononetin to RELA, all of which were stable. Conclusion: Quercetin, kaempferol and others in the Astragalus-Scorpion can regulate multiple signaling pathways such as PI3K-Akt signaling pathway by binding to targets such as AKT1 related to prostate cancer, inhibit the proliferation of tumor to play a role for prostate cancer.

scholarly journals Bicuculline From Corydalis Species As A Natural Anti-COVID-19 Drug

2020 ◽  
pp. 1-6
Author(s):  
Shaodong Chen ◽  
Keyword(s):  
Molecular Docking ◽  
Hydrolytic Enzyme ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Literature Mining ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Target Network

Objective: To perform molecular docking of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) 3CL hydrolytic enzyme (3CLpro) and Angiotensin-Converting Enzyme II (ACE2) receptors, and to seek potential natural anti-COVID-19 drugs using computer virtual screening technology. Methods: In this study, the Autodock Vina software was first used to achieve the molecular docking of the targets, namely, sars-cov-2 3CL hydrolase and ACE2. Then, the herbals acting on 3CLpro and ACE2 receptors were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the active ingredients were also selected. After that, the chemical-target network was constructed based on the network pharmacology, and the functional enrichment analysis of Gene Ontology (GO) and the pathway enrichment analysis of Kyoto Gene and Genome Encyclopedia (KEGG) were carried out by DAVID to speculate about the mechanism of action of the core drug. Results: A total of six potential anti-COVID-19 active ingredients were selected from natural herbs. They were evaluated by the “ADME” and "Lipinski” rules and their content in the natural herbs were determined by the literature mining method. Finally, Bicuculline was selected as the anti-covid-19 candidate drug. Conclusion: Bicuculline has a stronger ability to combine with 3CLpro and ACE2 than chemical drugs recommended in the clinical practice. Internet pharmacological analysis confirms that Bicuculline can effectively resist COVID-19 pneumonia through multiple pathways.

Discussing the Mechanism of Dahuang Huanglian Xiexin Decoction in the Treatment of Type 2 Diabetes Mellitus via Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Xi Cen ◽  
Yan Wang ◽  
LeiLei Zhang ◽  
XiaoXiao Xue ◽  
Yan Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Potential Mechanism ◽  
Active Components ◽  
The Core ◽  
Core Genes

Abstract BackgroundType 2 diabetes mellitus (T2DM) is regarded as Pi Dan disease in traditional Chinese medicine (TCM). Dahuang Huanglian Xiexin Decoction (DHXD), a classical TCM formula, has been used for treating Pi Dan disease in clinic, its pharmacological mechanism has not been elucidated. MethodsThis study used network pharmacological analysis and molecular docking approach to explore the mechanism of DHXD on T2DM. Firstly, the compounds in DHXD were obtained from TCMSP and TCMID databases, the potential targets were determined based on TCMSP and UniProt databases. Next, Genecards, Digenet and UniProt databases were used to identify the targets of T2DM. Then, the protein-protein interaction (PPI) network was established with overlapping genes of T2DM and compounds, and the core targets in the network were identified and analyzed. Then, the David database was used for GO and KEGG enrichment analysis. Finally, the target genes were selected and the molecular docking was completed by Autodock software to observe the binding level of active components with target genes.ResultsA total of 397 related components and 128 overlapping genes were identified. After enrichment analysis, it was found that HIF-1, TNF, IL-17 and other signaling pathways, as well as DNA transcription, gene expression, apoptosis and other cellular biological processes had the strongest correlation with the treatment of T2DM by DHXD, and most of them occurred in the extracellular space, plasma membrane and other places, which were related to enzyme binding and protein binding. In addition, 42 core genes of DHXD, such as VEGFA, TP53 and MAPK1, were considered as potential therapeutic targets, indicating the potential mechanism of DHXD on T2DM. Finally, the results of molecular docking showed that HIF-1 pathway had strong correlation with the target genes INSR and GLUT4, quercetin and berberine had the strongest binding power with them respectively.ConclusionThis study summarized the main components of DHXD in the treatment of T2DM, identified the core genes and pathways, and systematically analyzed the interaction of related targets, trying to lay the foundation for clarifying the potential mechanism of DHXD on T2DM, so as to carry out further research in the future.

scholarly journals Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ping Yang ◽  
Haifeng He ◽  
Shangfu Xu ◽  
Ping Liu ◽  
Xinyu Bai
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Target Prediction ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Core Network ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

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