PropertyValue
?:abstract
  • Objective: To investigate the mechanism of Farfarae Flos (FF) in Qingfei Paidu Decoction against coronavirus disease 2019 (COVID-19) based on network pharmacology and molecular docking. Methods: Based on our previous study, the main compounds in FF were selected. The potential targets of FF were searched by Swiss Target Prediction and BATMAN-TCM database. GenCLiP 3 and GeneCard were used to predict and screen the therapeutic targets of COVID-19, and then Cytoscape 3.7.1 was used to build the compound-target-disease network. The String database was used to build the target PPI network. Gene ontology (GO) function enrichment analysis and KEGG pathway enrichment analysis were performed in the DAVID database. Molecular docking was performed based on the above compounds and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3CL hydrolase and angiotensin converting enzyme II (ACE2). Results: The compound-target-disease network contained 14 compounds, 104 targets and four diseases. GO function enrichment analysis revealed 444 GO items (P < 0.05), including 325 biological process (BP) items, 44 cell composition (CC) items and 75 molecular function (MF) items. A total of 94 signal pathways (P < 0.05) were screened out by KEGG pathway enrichment analysis. The results of molecular docking showed that the affinity of 3,4-dicaffeoylquinic acid and 4,5-dicaffeoylquinic acid with proteins were better than Remdesivir. Conclusion: The compounds in FF can bind with SARS-CoV-2 3CL hydrolase and ACE2, and then act on many targets to regulate multiple signaling pathways, thus exerting the therapeutic effect on COVID-19.
is ?:annotates of
?:creator
?:journal
  • Chin._Trad._Herbal_Drugs
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • Mechanism of Farfarae Flos in Qingfei Paidu Decoction against COVID-19 based on network pharmacology and molecular docking/ 基于网络药理学和分子对接技术的款冬花在清肺排毒汤治疗新型冠状病毒肺炎(COVID-19)中的作用分析
?:type
?:who_covidence_id
  • #681930
?:year
  • 2020

Metadata

Anon_0  
expand all