ObjectiveTo explore the molecular mechanism implicated in the treatment of primary myelofibrosis （PMF） using Chinese medicinal herbs （CMH） by bioinformatics and molecular dynamics. MethodsData mining was performed to find the high-frequency CMH in treating PMF between the year of 1985 and 2024 by searching CNKI， Chinese Science and Technology Journal Database （CCD）， and China Academic Journal Database （CSPD）. TCMSP， SwissTargetPrediction and related reports were used to collect the main active ingredients of high-frequency CMH and their targets. The PMF datasets GSE44426 and GSE124281 were downloaded from GEO database， and R software was used for data normalization and differentially expressed genes （DEGs） screening. Key module hub genes were obtained by weighted gene co-expression network analysis （WGCNA） analysis. The common intersection genes of active ingredient targets， DEGs and key module hub genes of CMH were selected， and the target network was generated using Cytoscape 3.9.2 software. The core target network was generated by topological analysis， while key pathways were selected by GO and KEGG pathway enrichment analysis， and protein interaction relationships were obtained from the String database， so as to construct drug-ingredient-target network and protein interaction network （PPI） relationship diagrams. Discovery Studio 2020 software was used to perform molecular docking， and the GROMACS program was used to perform molecular dynamics simulation. ResultsA total of 21 prescriptions were collected involving 121 herbs. There were 9 herbs with a frequency ≥10 times， which were Danshen （Radix et Rhizoma Salviae Miltiorrhizae）， Huangqi （Radix Astragali）， Baizhu （Rhizoma Atractylodis Macrocephalae）， Danggui （Radix Angelicae Sinensis）， Dangshen （Radix Codonopsis）， Gancao （Radix et Rhizoma Glycyrrhizae）， Baishao （Radix Paeoniae Alba）， Fuling （Poria） and Shudihuang （Radix Rehmanniae Praeparata） from high- to low-frequency. A total of 98 active ingredients and 1125 potential targets were obtained from 9 high-frequency CMH. GSE44426 and GSE124281 data sets screened out 24 gene samples， including 14 of the healthy control group and 10 of the PMF group， and identified 319 DEGs between the two groups， including 122 up-regulated genes and 197 down-regulated genes. WGCNA screened out 24 co-expression module genes and found that the five modules closely related to the onset of PMF were MEpink， MEdarkred， MEblack， MEgrey， and MEturquoise， involving 7112 key module hub genes. The GO and KEGG enrichment analyses indicated that lipids and the atherosclerosis pathways were mainly involved in the mechanism of above high-frequency CMH in treating PMF， which included six hub protein targets： HSP90AA1， HSP90AB1， SRC， MAPK1， IL1B and IL10. From the drug-ingredient-target network， seven active ingredients of CMH targeting at these six hub targets were found， including verbascoside， verbascos isoflavone， kaempferol， luteolin， naringenin， quercetin and pachymic acid. The molecular docking and molecular dynamics analyses showed that the key CMH were Shudihuang， Huangqi， Baishao， Danshen， Gancao and Fuling， and among the seven active ingredients， calycosin had the highest binding affinity with HSP90AB1. ConclusionThe main CMH for the treatment of PMF may be Shudihuang， Huangqi， Baishao， Danshen， Gancao and Fuling， and the active ingredients include verbascoside， verbascos isoflavones， kaempferol， luteolin， naringenin， quercetin and pachymic acid. The relevant targets are HSP90AA1， HSP90AB1， SRC， MAPK1， IL-10， and IL-1β， and the most critical pathways are lipid and atherosclerosis pathways.