Jinshui Huanxian granules (JSHX) is a clinical Chinese medicine formula used for treating pulmonary fibrosis (PF). However, the effective components and molecular mechanisms of JSHX are still unclear. In this study, a combination approach using ultra-high performance liquid chromatography-Orbitrap Fusion mass spectrometry (UPLC-Orbitrap Fusion MS) integrated with network pharmacology was followed to identify the components of JSHX and the underlying molecular mechanisms against PF. UPLC-Orbitrap Fusion MS was used to identify the components present in JSHX. On the basis of the identified components, we performed target prediction using the SwissTargetPrediction database, protein–protein interaction (PPI) analysis using STRING database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using Metascape and constructed a component-target-pathway network using Cytoscape 3.7.2. Molecular docking technology was used to verify the affinity between the core components and targets. Finally, the pharmacological activities of three potentially bioactive components were validated in transforming growth factor β1 (TGF-β1)-induced A549 cell fibrosis model. As a result, we identified 266 components, including 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components. Of these, 90 validated components were predicted to act on 172 PF-related targets and they exhibited therapeutic effects against PF via regulation of cell migration, regulation of the mitogen-activated protein kinase (MAPK) cascade, reduction of oxidative stress, and anti-inflammatory activity. Molecular docking showed that the core components could spontaneously bind to receptor proteins with a strong binding force. In vitro, compared to model group, hesperetin, ruscogenin and liquiritin significantly inhibited the increase of α-smooth muscle actin (α-SMA) and fibronectin (FN) and the decrease of e-cadherin (E-cad) in TGF-β1-induced A549 cells. This study is the first to show, using UPLC-Orbitrap Fusion MS combined with network pharmacology and experimental validation, that JSHX might exert therapeutic actions against PF by suppressing the expression of key factors in PF. The findings provide a deeper understanding of the chemical profiling and pharmacological activities of JSHX and a reference for further scientific research and clinical use of JSHX in PF treatment. The main clinical symptoms are dry cough and progressive dyspnea3. Accumulating evidence shows that the important pathogenic mechanisms that promote PF, mainly involve damage of the alveolar histological structure, proliferation of fibroblasts and myofibroblasts, further leading to the deposition of extracellular matrix and lung structural remodeling4. At the molecular mechanism level, oxidative stress is one of the important mechanisms underlying PF, which mainly causes the oxidation-antioxidation imbalance generation by mitochondria and the enzyme system6. Oxidative stress affects the normal structure and physiological function of the lungs by inducing inflammation and regulating fibrosis-related cellular signaling pathways, such as mitogen-activated protein kinase (MAPK), nuclear factor kappa B (NF-κB), phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt), etc.7. At present, glucocorticoids, immune suppressants, pirfenidone (PFD) and nintedanib, are mainly used to treat PF9. However, their curative effect is limited or there are side effects10. Thus, it is necessary to find alternative drugs for PF treatment. Chinese medicine (CM) has exerted multi-target effects on PF and regulated oxidant stress, reduced the expression of inflammatory factors, and induced apoptosis of pulmonary fibroblasts, etc.12.Pulmonary fibrosis (PF) is a chronic, progressive and fatal lung disease, characterized by irreversible lung structure damage and exuberant extracellular matrix protein deposition14, which is composed of 10 herbs, including Panax ginseng C. A. Mey. (PG), Ophiopogonis Radix (OR), Radix Rehmanniae Praeparata (RRP), Fritillariae Thunbergii Bulbus (FTB), Trichosanthis Fructus (TF), Moutan Cortex (MC), Epimedii Folium (EF), Ginkgo Semen (GS), Citri Reticulatae Pericarpium (CRP) and Glycyrrhizae radix et rhizome (GR). Our previous studies showed JSHX can effectively improve lung function, reduce lung tissue injury and collagen deposition in PF rats15. A controlled trial revealed that JSHX significantly improve the inflammatory response and oxidative stress of lung injury caused by PM2.5 exposure compared with placebo16. Moreover, pharmacodynamic study clarified that JSHX could suppress the TGF-β1-induced differentiation of fibroblasts into myofibroblasts through reducing the oxidative response by upregulating Nrf2 signaling in PF rats17. However, little research has been done to elucidate the comprehensive material basis and the molecular mechanisms of JSHX, further studies are urgently needed to clarify the bioactive components in JSHX, and further clarify its medicinal and clinical application values. Chinese Medicine formula has complex components and diverse targets. We hypothesized that the bioactive components in JSHX play a role in PF treatment through the molecular mechanism of anti-inflammatory, antioxidant, inhibition of alveolar epithelial stromal and fibroblast activation.Jinshui Huanxian granules (JSHX), a clinical Chinese medicine formula, had been widely used for treating PFThus, in this study, we developed an integrative approach to clarify the effect of JSHX on PF by combining UPLC-Orbitrap Fusion MS analysis and network pharmacology. UPLC-Orbitrap Fusion MS technology was performed to characterize the chemical profile of JSHX. Network pharmacology and molecular docking were used to conduct data mining on its chemical components to explore the bioactive components, potential targets, biological processes and signaling pathways relevant to PF treatment, and the effects of bioactive components were further validated in TGF-β1-induced A549 cells. The protocol is shown in Fig. Chinese Pharmacopoeia 2020 edition. These herbs were identified by Prof. Sui-Qing Chen, Henan University of Chinese Medicine, Zhengzhou, China. The voucher specimen was stored at Scientific Research Center, Henan University of Chinese Medicine, Zhengzhou, China.HPLC-grade methanol and acetonitrile were obtained from TEDIA , MS-grade formic acid was purchased from Thermo Fisher Scientific . Ultra-pure water was prepared by a Milli-Q water purification system . All other chemicals and reagents were analytical grade. Panax ginseng C. A. Mey. (PG), Ophiopogonis Radix (OR), Radix Rehmanniae Praeparata (RRP), Fritillariae Thunbergii Bulbus (FTB), Trichosanthis Fructus (TF), Moutan Cortex (MC), Epimedii Folium (EF), Ginkgo Semen (GS), Citri Reticulatae Pericarpium (CRP) and Glycyrrhizae radix et rhizome (GR) were purchased from Ruilong Pharmaceutical Co., LTD , Standard Basis: As reference standards, 37 pure components were used. Seventeen reference standards, including Rehmannioside D (CHB180129), Ferulic Acid (CHB180206), Ginsenoside Rg1 (CHB190110), Neocnidilide (CHB1810707), Isocorynoline (CHB181012), Ginsenoside Rg2 (CHB190111), Hesperetin (CHB180524), Liquiritin (CHB180608), Cynaroside (CHB180111), Ginsenoside Rc (CHB190104), Ginsenoside Rf (CHB190108), Astragalin (CHB181115), Ginsenoside Rb2 (CHB131014), Ginsenoside Re (CHB170926), Tangeritin (CHB150806), Catalpol (CHB181129), Apigenin-7-O-Glucoside (CHB161226) were purchased from Chengdu Cloma Biological Technology Co., LTD ; Eleven reference standards, including Peimisine (must-19072508), Epimedin A (must-11062311), Epimedin B (must-11062312), Ginkgolide B (must-17073101), Hesperidin (must-16041806), Nobiletin (must-16070901), Paeonol (must-16071405), Ruscogenin (must-15021807), luteolin (must-14050411), Isoacteoside (must-19103104), Echinocystic acid (PS16091901) were all purchased from Chengdu Mansite Biotechnology Co., Ltd ; Wedelolactone (PJ0629RA13) and Epimedin C  were purchased from Shanghai Yuanye Biological Technology Co., LTD ; Seven reference standards, including Ginsenoside Rb1 , Baohuoside I , Peimine , Peiminine , Icariin , Rutinum , Quercitrin  were purchased from China Food and Drug Testing Institute . The purity of each component was ≥ 98% by HPLC analysis.Roswell Park Memorial Institute (RPMI) 1640 medium, Radio Immunoprecipitation Assay (RIPA) Lysis Buffer, Thiazolyl Blue Tetrazolium Bromide (MTT) and trypsin–EDTA were obtained from Beijing Solarbio Science & Technology Co., Ltd , Fetal bovine serum (FBS) was purchased from Zhejiang Tianhang Biotechnology Co., Ltd. , Enzyme-linked immunosorbent assay (ELISA) kits for E-cad, α-SMA, and FN were obtained from BOSTER Biological Technology co., ltd .Chinese Pharmacopoeia 2020 edition.Five herbs  were decocted twice with 12 times the amount of water for 1 h and filtered, and the filtrate was concentrated and reserved. Five herbs  were weighed and added in a round-bottom flask with 10 times the amount of 70% ethanol, reflux-heated for 1 h twice and filtered. The filtrate was concentrated and ethanol removed, and this filtrate was then combined with filtrate 1. The mixture was concentrated to thick paste with a relative density of 1.18–1.22, dried at 60 °C under reduced pressure, and crushed into a fine powder. An appropriate amount of dextrin was added and mixed well, 80% ethanol was used as a wetting agent to make grains, and the grains were dried at 60 °C to obtain JSHX. The plants research in this study complies with the JSHX (2 g) was accurately weighted and added in a 100 mL round-bottom flask with 25 mL of methanol, the mixture was weighed, reflux-heated for 1 h, cooled to room temperature, weighed again to make up for the lost weight, and filtered, the sample solutions were obtained.Accurately weigh an appropriate amount of each reference standards into a 10 mL volumetric flask, methanol was added to dissolve and dilute to the mark, single-component reference stock solutions were obtained and stored at 4 °C before use. Then, the appropriate amount of stock solution was added to a 25-ml volumetric flask, and methanol was added to reach the volumetric mark, the mixed reference solution was obtained. The solutions were filtered through 0.22 μm microporous membranes and stored at 4 °C before use.TMC18 column  was applied with a constant flow rate of 0.2 mL/min at 30 °C. The mobile phase consisted of methanol (A) and 0.1% formic acid (B) with the following gradient elution: 0–5 min (95–79% B), 5–20 min (79–55% B), 20–41 min (55–25% B), 41–48 min (25–14% B), 48–58 min (14–5% B), 58–60 min (5–0% B). The injection volume was 5 μL.An AccucoreHigh-resolution MS detection and analysis were performed on an UPLC-Orbitrap Fusion MS (Thermo Scientific) equipped with an ESI source. The acquisition parameters were set as follows: Vaporizer temperature: 275 °C; Ion Trasfer Tube temperature: 300 °C; Carrier gas (N2); Sheath gas flow rate is 35 arb; Aux gas flow rate is 5 arb; the spray voltage was set at 3.5 (positive ion mode) and 2.5 (negative ion mode) kV; the collision energy was set at 35–55 eV. The sample was analyzed in both positive and negative ion Full MS/dd-MS2 modes with the first-level full scan  and the second-level scan , and the mass range was recorded from m/z 120–1200.JSHX was identified by the optimized UPLC-Orbitrap Fusion MS method. The possible chemical composition (with an error of less than 5 ppm) was determined using Xcalibur based on map data, precise molecular weight and resulting fragment ions. The data was analyzed using Compound Discoverer software to integrate ion peak information, attribution information, ChemSpider, mzCloud, and other databases of characteristic fragments integrated with existing chemical composition information reports. The structure of components was inferred using the cracking prediction of MassFrontier and its cracking rule.https://pubchem.ncbi.nlm.nih.gov/) and saved in SDF format. These SDF documents were uploaded to the SwissTargetPrediction database (http://www.swisstargetprediction.ch/) for target prediction. For more accurate prediction of the target gene of each component, relevant parameters were set (probability ≥ 0.1). Furthermore, the biological targets related to PF were selected from the GeneCards database (https://www.genecards.org/) using “Pulmonary Fibrosis” as the keywords. Then, the intersection of the predicted targets from JSHX and the biological targets of PF was taken and the overlapping targets were screened out as potential targets.The structures of validated components present in JSHX were generated using the PubChem database (https://cytoscape.org/) was used to construct a protein–protein interaction (PPI) network. A network analyzer in Cytoscape was used to analyze relevant topological parameters. Using three parameters, degree, betweenness centrality (Bc), and closeness centrality (Cc), a topology analysis of the PPI network was performed to determine hub genes for further analysis.The potential targets identified were added to the STRING database. The screening condition used was “Homo sapiens,” and the other parameters were set by default. Cytoscape  for GO and KEGG pathway enrichment analysis to obtain the information of pathways20. The Bioinformatics Data analysis and Visualization online platform (http://www.bio-informatics.com.cn/) was adopted to perform GO and KEGG pathway analysis and visualize the bar chart in this study. During this procedure, the significance level was set as p ≤ 0.01, and the organism was selected as “Homo sapiens.”The potential targets identified were uploaded into the Metascape database . Pymol  software was used to remove water molecules and separate the primary ligand. After saving, the structure was imported into Autodock Tools 1.5.6 and saved in “pdbqt” format. Chem 3D  software was used to download the mol2 files of the top 10 core components, and they were imported into Autodock Tools  and saved in pdbqt format. Finally, docking was carried out using Autodock vina . Discovery Studio Client  was used to visualize docking results and establish a docking interaction model diagram.Molecular docking technology was used to verify the affinity between the core components and targets. The 3D structure of the core targets in the first five degrees of JSHX in PF treatment was downloaded from the RCSB PDB database . Cells were cultured in RPMI 1640 medium supplemented with 10% FBS and maintained in a humidified incubator with 5% CORuscogenin, liquiritin and hesperetin were dissolved by DMSO and diluted to appropriate concentrations before use, respectively. The cells were divided into model group: A549 cells were stimulated in complete culture medium containing TGF-β1 (7.5 ng/ml) and 10% FBS; Control group: A549 cells were cultured in complete medium supplemented with 10% FBS. Dosage group: on the basis of the model group, the dosing groups included hesperetin, ruscogenin and liquiritin.4 cells/well) were planted into a 96-well plate, and incubated at 37 °C with 5% CO2 for 24 h. Then the cells were treated with PBS or different dosages of hesperetin , liquiritin , and ruscogenin . After incubation for 24 h, 10 µl 0.5 mg/ml MTT was added to each well, and incubated for 4 h. The cell culture was replaced with DMSO (100 µl/well). The absorbance was measured at 570 nm by using a microplate reader.The cell viability was measured with MTT assay. A549 cells (1 × 106 cells/plate) were incubated in 6-well plates for 24 h, then treated with hesperetin , ruscogenin  and liquiritin , and incubated at 37 °C with 5% CO2 in a humidified atmosphere for 48 h. Supernatants were collected, centrifuged and stored at − 80 °C for testing. RIPA Lysis Buffer 300 μl/well was added to lysate cells. 10 min later, the lysate was collected, the supernatant was collected by centrifugation, and levels of E-cad, α-SMA, FN in the supernatant were determined by ELISA.A549 cells . Statistical Differences in multiple groups were performed by analysis of variance (ANOVA). A total of 266 components were identified in JSHX: 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components including quinones, esters, and organic acids, etc. Of the 266 components, 37 components were unambiguously identified via comparison with reference standards. The primary and secondary information was compared with the database for confirmation. The total ion chromatograms (TICs) of positive and negative ions were shown in Fig. 2O, CO, CO2, and CHO through collision-induced dissociation. In flavone glycosides, glycosidic bonds are easily broken, and they can lose glycosyl groups to generate aglycon ions21.Flavonoids mainly originate from EF, CRP, GS in JSHX, a total of 56 flavonoids were identified, including 7 flavone aglycones and 49 flavone O-glycosides. Flavone aglycones usually undergo Retro-Diels-Adel (RDA) reaction and lose small-molecule fragments of H39H50O20. During collision-induced dissociation, the RDA reaction occurs and fragment ions of m/z153.01817 are generated. Small molecule fragments are also lost: a CO molecule were lost to generate a fragment ion [M+H−CO] + at m/z 259.00958, as shown in Fig. 27H30O10. A glucosyl group is lost, generating a fragment ion [M+H−glu]  + at m/z 369.13293. On this basis, the B-ring is broken, and a CHO molecule and a CO molecule were lost to generate a fragment ion [M+H−CHO−CO] + at m/z 313.14344, as shown in Fig. Aglycones 177 possessed quasi-molecular ions [M+H] + at m/z 287.05496, and the chemical composition was C2O, and finally formed a structure with m/z 369 as the parent nucleus. Connecting different sugar groups produces corresponding secondary fragments, and m/z 369 may break into m/z 313, m/z 515 break into m/z 129, and m/z 129 loses H2O to get component 111, which can assist in analysis. According to these rules, 56 flavonoids were identified, including components 35, 37, 43, 50, 51, 52, 58, 59, 65, 66, 69, 73, 74, 76, 77, 79, 80, 81, 83, 88, 96, 97, 104, 107, 111, 112, 114, 115, 123, 125, 131, 135, 136, 137, 138, 140, 141, 142, 143, 146, 151, 152,153, 154, 155, 159, 164, 165, 167, 170, 171, 173, 176, 177, 185, 203.During MS information scanning, flavonoids mainly underwent deglycosylation and dehydration, characterized by the loss of glucose (162), rhamnose (146), xylose (132) and (18) H22. Triterpenoid saponins can be divided into three categories according to structure: ginsenoside diol (A), ginsenoside triol (B), and oleanolic acid (C). In saponins, glycosidic bonds are easily broken and they can lose glycosyl groups to generate aglycon ions. By removing multiple glycosyl groups-glu (162), rha (146), and ara (132) to form characteristic ions, A, B and C finally form the characteristic parent nucleus at m/z 459.3843, m/z 459.3792 and m/z 455.3530, respectively23.Saponins mainly originate from PG, OR in JSHX, include triterpenoid saponins and steroidal saponins54H92O23. During collision-induced dissociation, a glucose group was lost in turn to generate fragment ion [M−H−glu]− at m/z 945.54291, [M−H−2glu]− at m/z 783.49017, [M−H−3glu]− at m/z 621.4358, [M−H−4glu]− at m/z 459.3841, respectively. The parent nucleus of m/z 459 is the structural parent nucleus of Ginsenoside diol, and component 197 was further identified as Ginsenoside Rb1 by reference standard. Due to the presence of formic acid in the mobile phase, an [HCOO]– adjoint ion peak may be generated during cleavage. For example, the excimer ion peak of component 199 at RT = 36.92 min was m/z 945.54284 [M−H]−, and its chemical composition was C48H82O18, which is presumed to be Ginsenoside Re or Ginsenoside Rd. M/z 991 is found in the spectrum, it is presumed that it is an additive ion peak [M−H+HCOO]−, and the abundance of molecular ion peak of m/z 945.54284 [M-H]- is relatively high. At RT = 39.62, m/z 991.54761 and m/z 945.54234 are also found, which are presumed to be [M−H++HCOO]−, [M−H]− respectively. Component 199 is identified as Ginsenoside Re or Ginsenoside Rd. Since Ginsenoside Re and Ginsenoside Rd are isomers, after comparison with the reference standard, component 199 was Ginsenoside Re, RT = 36.92 min, namely component 214 was Ginsenoside Rd, RT = 39.62. Similarly, components 126, 129, 198, 208, and 209 were confirmed as Ginsenoside Rg1, Rf, Rg2, Rb2, and Rc via reference standards, respectively.Triterpenoid saponins 197 possessed quasi-molecular ions [M−H]− at m/z 1107.59566, and the chemical composition was C27H42O4. A neutral molecule C8H16O2 was lost to generate a fragment ion [M+H−C8H16O2] + at m/z 287.19971, a H2O molecule was lost in turn to generate fragment ion [M+H−C8H16O2−H2O] + at m/z 269.18863, [M+H−C8H16O2−2H2O] + at m/z 251.17883, respectively, as shown in Fig. Steroidal saponins mainly originate from OR in JSHX, the cleavage law of steroidal saponins is similar to this way. Saponins 236 possessed quasi-molecular ions [M+H] + at m/z 431.31559, and the chemical composition was C2O or CH3 during molecular cleavage, or appear as molecular ion peaks25.Alkaloids mainly originate from FTB in JSHX, which are generally found in nature as organic components with complex ring structures including nitrogen, and some alkaloid nitrogen can also be found outside the ring. Alkaloid components usually lose one molecule of H27H43NO3. A molecule H2O was lost to generate a fragment ion [M+H−H2O] + at m/z 414.33621, as shown in Fig. 27H43NO3. One molecule H2O was lost to generate a fragment ion [M+H−H2O] + at m/z 412.32117, and component 54 was identified as peiminine, as shown in Fig. For example, the ion peak [M+H] + of component 53 had an m/z of 432.34736 and its chemical composition was C3, H2O, CO, CO2, etc., which is affected by the type and number of substituents. For example, the molecular ions peak of component 116 was m/z 313.03534 [M−H]−, and the chemical composition was C16H10O7. During cleavage, a molecular methyl group was lost to generate fragment ion [M−H−CH3]− at m/z 298.01203, a CO molecule was lost in turn to generate fragment ion [M−H−CH3−CO]− at m/z 270.01692, [M−H−CH3−2CO]− at m/z 242.02215, respectively, the component 116 could be identified as wedelolactone. In the same way, the other coumarins were identified, and totally 10 coumarins were identified as components 38, 67, 71, 116, 118, 139, 150, 161, 179 and 233.Coumarins is a lactone formed by intramolecular dehydration of CIS hydroxycinnamic acid. The basic rule of coumarins cracking is to remove several CO molecules successively, thus forming a series of [M+H–CO] + fragment peaks. Specific fragment may remove CH15H22O10. A glucosyl group is lost, generating a fragment ion [M+H−glu] + at m/z 201.07575. A H2O molecule was lost to generate a fragment ion [M+H−glu−H2O] + at m/z 183.06519, as shown in Fig. Terpenoids mainly originate from RRP and GR in JSHX. A total of 12 terpenoids were identified, including 2 iridoids, 6 terpene lactones and 4 triterpenes. RDA cleavage of terpenoids often occurs, accompanied by rearrangement during cleavage, which is generally dominated by McMelloy rearrangement, and the cleavage mode is different, usually dehydration of hydroxymethyl. Iridoids 32 possessed quasi-molecular ions [M+H] + at m/z 363.12875, and the chemical composition is COther components of JSHX mainly include quinones, esters, organic acids, etc., which were identified using Compound Discoverer software integrated with ChemSpider, MzCloud and other databases, comparison of reference substances and reference literature. We identified 105 other components: 23 organic acids , 16 esters , 14 phenols , 9 quinones , 9 phenolic acids , 8 ketones , 11 alcohols , 7 phenylpropanoids , 3 amides , 2 glycosides , 1 ether (component 184), 1 amino acid (component 2) and 1 nucleotide (component 3).After removing non-target components and repeated targets, we obtained 138 components acting on 942 targets , molecular function (MF), and cellular component (CC). The top 20 terms for BP, CC and MF are shown as a bar plot in Fig. p-values. As shown in Fig. The top 20 enriched KEGG pathways presented in Fig. A network of validated components, targets and top 20 enriched KEGG pathways was constructed and shown in Fig. The top 10 core components with corresponding five core targets were simulated by molecular docking, and the docking results were analyzed. The basic information about ligands and proteins is shown in Table Both liquiritin and hesperetin had strong binding ability with five core targets. Visualization of the docking results was performed using Discovery Studio 4.5 Client software Fig. . Liquiri26. It was found that human lung cancer A549 cells were type II alveolar epithelial cells, which became a common in vitro model to study the mechanism of pulmonary fibrosis after induction with TGF-β127. More and more evidences indicate that autophagy, which maintains intracellular homeostasis, is closely related to the occurrence and development of pulmonary fibrosis28. Therefore, we selected TGF-β1 induced A549 cells as the in vitro model. According to the network pharmacology and molecular docking analysis, three bioactive components including ruscogenin, liquiritin, hesperetin had the better molecular docking results. To further evaluate the results obtained by systematic pharmacologic analyses, these components were selected from JSHX to examine the potential effects of anti-inflammatory and inhibiting the differentiation of fibroblasts by using TGF-β1 (7.5 ng/ml)-stimulated A549 cells. We employed ELISA assay for E-cad, α-SMA and FN to confirm the predicted anti-PF effects of three bioactive components.Studies have shown alveolar epithelial cells A549 are one of the mediators for PF detection. Transforming growth factor β1 (TGF β1) promotes pulmonary fibrosis, which mediates epithelial mesenchymal transformation of human alveolar epithelial cells, resulting in extracellular matrix depositionFirstly, we determined the effects of different doses of ruscogenin, liquiritin, hesperetin on the viability of A549 cells using MTT assay. As shown in Fig. In this study, we analyzed the chemical components of JSHX and explore its molecular mechanisms against PF using integrative strategy, including chemical components analysis, network pharmacology prediction, molecular docking, and experiment in vitro. Firstly, the total ion chromatograms of JSHX were obtained by UPLC-Orbitrap Fusion MS. Different mobile phase systems  and different elution gradients were investigated to optimize reliable and effective chromatography conditions. In addition, due to the different response modes of various components in the formula, positive and negative ion-scanning modes were selected for simultaneous detection line monitoring. A total of 266 chemical constituents (193 in ESI+ and 73 in ESI−) were identified or tentatively characterized by matching the MS raw data to mzCloud, Chemspider and other built-in databases using the Compound Discoverer 3.0 software, including 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components, which could be concluded that the developed UPLC-Orbitrap Fusion MS method was sensitive for identifying the chemical components in JSHX, and that the result of the study provided more chemical material basis information for further research of JSHX.30. As the predicted components, ruscogenin, liquiritin and hesperetin exerted good anti-PF effects based on TGF-β-stimulated A549 cells.Network pharmacology can reveal the relationships between targets/pathways and chemical components, and establish a “component-target-pathway” network to clarify the molecular mechanisms of drug therapy for disease. In the present study, 90 of the 266 identified components of JSHX were considered to have potential key anti-PF effects by acting on 172 related targets. To explore the key bioactive components and underlying mechanisms of JSHX against PF, a multidimensional component-target-pathway network was further constructed and visualized using Cytoscape 3.7.2. Twenty-four key bioactive components were screened based on three topological parameters, which may improve the pathological status of patients with PF by regulating fifty-seven corresponding targets, including twelve flavonoids, one terpenoid, one saponin, one coumarin, one alkaloid, and six other components, which proved that JSHX had multicomponent synergistic effect in the treatment of PF. Studies have shown that TGF-β can promote the proliferation of lung fibroblasts and collagen synthesis, which is the key cytokine leading to fibrosis, so we chose TGF-β as PF inducer32. Luteolin, and kaempferol were reported to exert therapeutic effects on PF by inhibiting the proliferation and inflammation of the lung fibroblasts in rat34. Hesperetin play a protective role in PF, which can inhibit inflammatory response by reducing the levels of TGF-β1, TNF-α, and IL-1β35. In addition, OMF, panaxytriol, ruscogenin, and brevisamide have relatively high degree values, and can be used as the main active components of JSHX against PF. Among them, ruscogenin, the main saponins in JSHX, can significantly inhibit TGF-β1-induced increase of FN and COL-I in PF model cell17. OMF is a secondary metabolite produced by penicillium pinophilum, which can induce the apoptotic pathway and inhibit cell motility and so may have potential as a naturally derived antitumor drug36. Panaxytriol shows potential anti-inflammatory activity by inhibiting the mRNA expression of proinflammatory cytokines such as TNF-α, IL-1b, and IL-637. In addition, Panaxytriol can inhibit the proliferation of lung cancer cells and induce apoptosis by downregulating ERK1/2 and mTORC1 pathways38. Studies have shown that brevisamide can improve microcirculation disturbance in the lung, promote oxygen utilization, eliminate oxygen free radicals, resist oxidation, and inhibit apoptosis39. The mechanisms of these active ingredients cover most aspects of the pathological process of PF, and the target information involved is basically consistent with our predicted results, indicating that the comprehensive pharmacological strategy has certain predictive accuracy. Among the predicted potential targets, EGFR, ESR1, AKT1, PTGS2, SRC, IL6, and VEGFA had high frequency, and were involved in several biological processes, including positive regulation of cell migration, positive regulation of kinase activity, response to oxidative stress, regulation of MAPK cascade, regulation of cell adhesion, which were closely related to the pathogenesis of PF.Among these bioactive components, flavonoids have anti-inflammatory and antioxidant activities, which are more consistent with PF treatment40, and affects the overall process of PF by activating downstream mTOR and HIF-1α, CTGF, VEGF and other factors to regulate ROS production and metabolism, extracellular matrix deposition, lung fibroblast production, and collagen production43. PI3K is an important part of the signal transduction process of the growth factor receptor superfamily. Activated PI3K can cause downstream protein phosphorylation and promote the mesenchymal transformation and extracellular matrix deposition of alveolar epithelial cells to form pulmonary fibrosis44. Akt, a direct target protein downstream of PI3K, can participate in the regulation of cell proliferation and metabolism, and promote fibrosis related gene transcription and protein synthesis45. Activated Akt activates the downstream HIF-1α signaling pathway, inhibiting alveolar surfactant production and normal repair of alveolar epithelial cells46. In addition, it can also activate NF-κB to initiate downstream inflammatory responses that produce inflammatory factors such as TNF-α and IL-6, thereby exacerbating fibrosis47. JSHX may exert anti-PF effects by inhibiting lung tissue inflammatory responses and apoptosis through regulating the expression of related genes in the PI3K-Akt signaling pathway. The HIF-1α signaling pathway is involved in apoptosis, proliferation, metabolism, growth and transformation, membrane transport, secretion and chemotaxis, and plays an important role in the pathogenesis of inflammation, tumors, metabolism, and cardiovascular diseases48. HIF-1α can mediate the transformation of bronchial and alveolar epithelial stromal cells and participate in the occurrence of PF49. The results showed that the JSHX had a certain intervention effect on the formation of PF.KEGG enrichment analysis showed that the potential targets were highly related to human disease for cancer, infectious diseases, metabolic diseases, immune system and signal transduction, including the pathways in cancer, endocrine resistance, EGFR tyrosine kinase inhibitor resistance, HIF-1 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, and VEGFA signaling pathway, etc. Of these, the PI3K-Akt signaling pathway is the key pathway, that participates in the pathophysiological process of apoptosis, proliferation and differentiationMolecular docking technology was used to verify the binding of core components and targets related with inflammation and oxidative stress. The binding free energy of components with the core targets was less than − 5.0 kJ/mol, which indicated that the ligand molecules could spontaneously bind to receptor proteins with a strong binding force. Based on the network pharmacology and molecular docking results, we test the effect of three bioactive components  presented on differentiation of fibroblast in TGF-β1-induced A549 cells. Experiment in vitro verified that the bioactive components inhibited the increase of α-SMA and FN, and the decrease of E-cad, and relieved pathological manifestation of PF. The results indicated that JSHX may play a role in PF treatment through anti-inflammatory, antioxidant and suppressing differentiation of fibroblasts into myofibroblasts synergistic effects.A valid, sensible and accessible UPLC-Orbitrap Fusion MS method was established for the chemical ingredient analysis of JSHX. Based on the identified components, network pharmacology and molecule docking were used to screen the effective components of JSHX and clarify their underlying mechanisms for PF treatment, and were further validated by experiments. The inhibitory effects of 3-O-methylfunicone, panaxytriol, ruscogenin, liquiritin and hesperetin displayed on the PI3K-Akt, HIF-1 and TNF signaling pathways might be the mechanisms of action of JSHX against PF. These findings provide an experimental basis for the scientific connotation and clinical application of JSHX against PF. However, the consistency of the components predicted by network pharmacology with those that ultimately enter the organismal circulation cannot be determined, and deeper mechanisms still need to be further demonstrated by more animal experiments, which will be the focus of our future research.Supplementary Legends.Supplementary Table S1.Supplementary Table S2.


Chimeric antigen receptor T-cell (CAR-T) therapy is a new and efficient cellular immunotherapy. The therapy shows significant efficacy, but also has serious side effects, collectively known as cytokine release syndrome (CRS). At present, some CRS-related cytokines and their roles in CAR-T therapy have been confirmed by experimental studies. However, the mechanism of CRS remains to be fully understood.Based on big data for human protein interactions and meta-learning graph neural network, we employed known CRS-related cytokines to comprehensively investigate the CRS associated cytokines in CAR-T therapy through protein interactions. Subsequently, the clinical data for 119 patients who received CAR-T therapy were examined to validate our prediction results. Finally, we systematically explored the roles of the predicted cytokines in CRS occurrence by protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis.We identified some novel cytokines that would play important roles in biological process of CRS, and investigated the biological mechanism of CRS from the perspective of functional analysis.128 cytokines and related molecules had been found to be closely related to CRS in CAR-T therapy, where several important ones such as IL6, IFN-γ, TNF-α, ICAM-1, VCAM-1 and VEGFA were highlighted, which can be the key factors to predict CRS.The online version contains supplementary material available at 10.1186/s12859-022-04917-2. Cellular immunotherapy is a new and effective treatment for tumor diseases. The representative cellular immunotherapy is chimeric antigen receptor T-cell (CAR-T) therapy. CAR-T therapy combines tumor associated antigen binding domain  with the killing mechanism of T cells through gene conduction technique . The proHowever, implementation of CAR-T therapy can be associated with serious or even fatal side effects, collectively termed cytokine release syndrome (CRS). CRS is the direct result of excessive production of inflammatory cytokines caused by immune system activation beyond the physiological level. When CAR-T cells make contact with a target antigen, their intracellular structure and co-stimulatory molecules CD28/CD137 are activated. CAR-T cells proliferate and rapidly become activated within a short period of time to secrete large numbers of cytokines including interleukins, tumor necrosis factor, and interferons . At the Specifically, CAR-T cells can induce a systemic inflammatory response in vivo and cause CRS . The actGiven the large number of cytokines involved in the immune response to CAR-T therapy and the occurrence of CRS, it is necessary to comprehensively predict the cytokines associated with CRS in the human body to understand the mechanism of CRS. Based on the known CRS-associated cytokines in CAR-T therapy, we used a semi-supervised meta-learning graph neural network model to comprehensively predict the CRS-associated cytokines based on big data for human protein interactions and the similarity of protein interactions. The clinical data of 119 patients who received CAR-T therapy were then used to verify the rationality of the analysis results. Finally, through protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis, we systematically explored the roles of the predicted cytokines in the occurrence of CRS. The results show great significance for understanding the mechanism of CRS in CAR-T therapy and designing targeted blocking measures.Meta-learning has made great progress in the field of image and text processing . The preij is the weight of the connection edge eij of node Vi and node Vj (the weight of 0 indicates no connection), X is the feature matrix, and Xi represents the characteristics of node Vi.First, a human protein interaction knowledge map is constructed based on human protein interaction big data, and the knowledge map information is expressed as G = , where V is the set of nodes in the map, E is the edge set of the relationship between nodes, A is the adjacency matrix, aThe Meta-GNN model includes two parts sharing the same GNN architecture: ‘meta-learner’ and ‘base-learner’. First, the meta-learner learns and optimizes the initial training parameters of the GNN model by a ‘learning to learn’ strategy, to ensure that the model has a good generalization ability. The final GNN model is then obtained through ‘base-learner’ training.The GNN model utilized in this study is a second-order GCN model. Each target node aggregates the node information of the second-order neighborhood. The main process is shown in Fig. Dtrain is constructed based on a few nodes  with labels. The meta-learning includes two steps: model training and model testing.The training set Dtrain and set as Si. The remaining nodes in the training set are then set as query set Qi. The meta-learning task Ti = Si + Qi is constructed. The above steps are repeated M times to generate M meta-learning tasks.In the model training stage, several nodes are randomly sampled from each class of the training set θ be the model parameter . In the implementation of the meta-learning task Ti, the objective function is the cross-entropy function with a known label node prediction value.Si. The parameters are then optimized as:p(T) is the set of meta-learning tasks and α is the learning rate.Let Qi data are used to test the model performance for the trained model parameters to avoid overfitting problems.In the model testing stage, The GCN model is trained after optimization. The GCN model uses graph convolution to aggregate the information of neighbor nodes. The essential purpose of the GCN model is to extract the spatial characteristics of the topological graph:A is the adjacency matrix of the input graph and IN is the identity matrix, jW(l) is the trainable weight, and σ(·) is the activation function. H(l) ∈ RN×D is the hidden-layer embedding of nodes on an l-th layer, initially H(0) = X denoting the feature matrix of nodes.Among them, Z denotes the output of the network, X is the feature matrix of nodes, A is the adjacency matrix, W(0) and W(1) are trainable weight matrices, and ReLU and Softmax are used as activation functions.The overall forward propagation formula of the GCN model is described as:In the training process, the cross-entropy function shown in Eq.  is also After training, the GCN model can be used to predict the unknown class nodes in the knowledge map.https://www.ncbi.nlm.nih.gov/). These data include cytokines, chemokines, and soluble receptors (collectively referred to as “cytokines”), with a total of 1769 samples. The interaction data for these cytokines were then obtained from the String database (https://string-db.org/), involving 62,576 interaction data for 1615 cytokines. We also provide a supplementary document , and positive and negative label data were put into Meta-GNN for model training. After training the model, we can obtain the probabilities for all nodes associated with CRS. Keeping the positive label data unchanged, the negative label data are conducted to the last 100 data with the minimum prediction probability, and the model training is conducted again until the prediction results converge.To avoid any influence of the initial data selection on the prediction results, 100 negative label data are randomly selected and the above prediction process is repeated. This process is repeated 1000 times. Finally, the prediction results are arranged from large to small according to the prediction probabilities associated with CRS, and the median value of the 1000 prediction results is taken as the final prediction result for each cytokine. The prediction results are shown in Fig. The clinical data for 119 patients with B-ALL who received CAR-T therapy were collected, and the details of data were described in Table XGBoost  algorithWe carried out KEGG pathway enrichment analysis and GO enrichment analysis on the predicted cytokines, collectively referred to as functional enrichment analysis , 16. ThrBased on the enrichment analysis of CRS-related cytokines, the crosstalk of CRS-related cytokines was analyzed. The analysis is based on the assumption that two pathways sharing a certain proportion of cytokines are considered to have crosstalk , 18.To describe the degree of crosstalk between a pair of pathways, two measurements can be calculated, namely the Jaccard coefficient (JC)For selection of a pair of enrichment pathways, the FDR value of each enrichment pathway should be less than 0.05 and the pathways should contain more than 5 enriched cytokines, because too few cytokines may lack sufficient biological information.The number of associated cytokines shared between the two pathways is calculated. Any pathway pair with less than 4 shared cytokines is removed.For all eligible pathway pairs, the mean value of the sum of the JC coefficient and the OC coefficient is calculated.To reduce false-positives, pathways with a mean value above 0.3 are selected for analysis.Visual screening of the pathway pairs is performed, with intuitive display of their crosstalk , 20.The specific steps are as follows:The results predicted by the Meta-GNN model are expressed in the form of probabilities. High probability means more similarity to the positive label data, indicating a closer relationship to CRS. The predicted results were shown in Table The clinical data collected from 119 patients includes four categories: cytokine expression data, coagulation test data, biochemical test data, and blood routine test data Table . Before In the process, we calculated mean values of all data from day 0 of CAR-T therapy to the last day of records which is about 7 days to analyze the relationship with the CRS levels of the patients.The XGBoost algorithm was used for classification analysis, and the contribution of each type of data to the highest level of CRS prediction was analyzed. A fivefold cross-validation was used in the research, and the results are represented by the importance of features, as shown in Fig. As shown in Fig. We also included some enzymes in the results. The enzymes are associated with organ damage. For example, high levels of LDH are often found in patients with grade 3–4 neurotoxic lymphoma and are negatively correlated with progression-free survival . These fFirst, we performed a network analysis on 128 selected cytokines and related molecules. Subsequently, a pathway analysis and GO enrichment analysis were performed. Fisher’s exact test was used to show the overlapping significance between pathways and input genes. In the analysis results, FDR values less than 0.05 were considered to indicate significant enrichment. The results showed the main pathways and biological functions of the cytokines. Finally, the cytokine pathways were extracted for crosstalk analysis and further explanation of how CRS occurs from an intuitive perspective.A network interaction diagram of the 128 cytokines and related molecules was drawn using Cytoscape Fig. . As showThe cytokines contained in each part are closely related and closely linked. Figure These six modules are not isolated from one another. Instead, they are closely related to a large module through certain cytokines.The pathways and major biological processes involving the cytokines were enriched. In the pathway analysis, a total of 49 pathways were enriched. Forty-seven important pathways with FDR values below 0.05 were selected, as shown in Fig. As shown in Fig. In Fig. To further highlight the details of the pathway enrichment and understand the interactions between pathways, we conducted pathway crosstalk analysis among the 47 pathways with significant enrichment. Among these, 45 pathways met the crosstalk analysis standard. Specifically, each pathway shared at least four genes with one or more other pathways, and had an FDR value less than 0.05. To reduce the false-positive rate, we selected the pathway pairs with mean JC and OC values above 0.3. A total of 306 pathway pairs were reserved from the 45 pathways.Based on their crosstalk, the pathways can be divided into left and right modules. Each module contains more interactions than other modules, indicating that they may participate in the same or similar biological processes. In Fig. In the past few years, through the acquisition of clinical symptoms and monitoring data for patients, researchers have understood the pathogenesis of CRS produced by CAR-T therapy. With the development of high-throughput technology, certain cytokines have been gradually identified as being related to the disease. However, the biological process for comprehensive understanding of CRS pathogenesis at the molecular level has not been established. Some related cytokines have not been found. Therefore, it is necessary to explore CRS pathogenesis and related cytokines at the system biology level , 30.The system analysis method described in our study has significant advantages. First, we collected a comprehensive set of human cytokines, providing a valuable source for further analysis. In addition, we used Meta-GNN as a neural graph network. This method was suitable for small sample size prediction in the study, because it can predict some cytokines that may be closely related to CRS based on known cytokines closely related to CRS as positive markers. Subsequently, we obtained data for 119 patients with B-lymphoblastic leukemia who received CAR-T therapy to allow feature screening. We applied the XGBoost algorithm to rank the importance of the measured features in our experimental data and verified the accuracy of the prediction results. Finally, we selected the positive labels and predicted the cytokines that may be closely related to CRS for subsequent analysis. We have provided a comprehensive and systematic framework to describe the biological processes and functional features of CRS produced during CAR-T therapy.The cytokine analysis mainly showed modules that are closely related to the cytokines, such as chemokines, Toll-like receptors, and interleukins. These cytokines play an important role in the development of CRS.The GO analysis mainly enriched the biological processes related to CRS, among which the inflammatory response was the most prominent. Examples included cytokines, chemokine-mediated responses, and cellular responses to LPS. Members of the interleukin family and chemokine family can increase the proliferation and activation of CAR-T cells, providing positive feedback for the inflammatory response. Lipopolysaccharide is considered a crucial inflammatory marker in CRS induced by CAR-T therapy , 30. BesThe pathway analysis showed that the JAK-STAT signaling pathway, chemokine signaling pathway, TNF signaling pathway, Toll-like receptors pathway, and NF-κB signaling pathway were enriched. These pathways related to inflammation were enriched, and their FDR values were low. The findings suggest that many cytokines are involved in CRS, consistent with its situation in pathological development. Some disease-related pathways may contribute to the early stage of CRS, such as inflammatory bowel disease, hepatitis B, hepatitis C, and rheumatoid arthritis. The pathway interaction module is mainly divided into two modules. One module is primarily related to inflammatory response and disease pathways, and the other module is mostly related to immune response pathways and diseases caused by these pathways. Activation of a series of inflammatory and immune signaling pathways can regulate various physiological processes in the human body and cause damage to patients.When CAR-T cells enter the human body, they directly cause the tumor cells to burn out, so that ATP, the energy molecule in the cells, is released in large quantities, and ATP can strongly activate macrophages . MacrophSubsequently, Toll-like receptors and NOD-like receptor (NLRs) play important roles in inflammation as extracellular and intracellular pattern recognition receptors, respectively. The TLR signal transduction pathway is activated by the Toll/IL-1R (TIR) domain, which activates the transcription factor NF-κB and MAPK pathway through MyD88. When CAR-T cells attack tumor cells, they cause the tumor cells to die and release antigens. When Toll-like receptors recognize pathogen-related molecular patterns (PAMPs), they activate NF-κB to initiate inflammatory reactions. Moreover, when inflammation occurs, LPS released by the inflammatory response induces a series of gene expressions through TLR4. The expression of antiviral cytokines such as CD80, CD86, and IFN-β is influenced by the MyD88-independent pathway, which participates in inflammation and the immune response. After the NOD1 and NOD2 proteins in the NOD-like receptor pathway bind to their ligands, they interact with RIP2 to phosphorylate IKB, thus activating the transcription factor NF-κB to mediate the expression of inflammatory mediators . ActivatThe TLR/NF-κB pathway is the critical link for the induction of inflammation. When LPS stimulates human umbilical vein endothelial cells, TLR4 expression is up-regulated, leading to increased TLR4 activity. The NF-κB component RELA plays an important role in the regulation. NF-κB1 also had a vital role in the TLR/NF-κB pathway. LPS activates NF-κB1 through TLR4 and subsequently produces a series of inflammatory mediators, leading to an inflammatory reaction that damages tissues and cells –39. ThesFrom the perspective of vascular endothelial cells, ICAM-1, VCAM-1, and SELE bind to ligands on the surface of leukocytes to mediate leukocyte adhesion, and further induce leukocyte aggregation and infiltration. This causes local inflammation and microvascular endothelial cell injury, and subsequently promotes expression of VWF. After vascular injury, NO free radicals are released. Abnormal NO production causes vasodilation and hypotension, as common clinical features of CRS , 20, 42.Regarding cell apoptosis, apoptosis of vascular endothelial cells is inhibited during the occurrence of CRS. Specifically, activation of the PI3K-AKT signal transduction pathway inhibits cell apoptosis, which has an important role in the pathogenesis and apoptosis mechanism underlying inflammation, tumor development, metabolism, and other diseases. When the PI3K-AKT signal transduction pathway becomes activated, activated Akt increases the content of NF-κB and activates the NF-κB pathway. The PI3K-AKT signal transduction pathway upregulates the transcription levels of TNF-α and other genes through a series of reactions. TNF-α promotes the production and release of other cytokines and finally leads to the formation of a cytokine network that expands the inflammatory chain reaction. The apoptosis induced by TNF-α, IFN-γ, and other cytokines depends on the PI3K-AKT pathway. Apoptosis plays an important role in the regression of CRS. The PI3K-AKT signal transduction pathway inhibits apoptosis of T cells and vascular endothelial cells and promotes continuation of an inflammatory state, providing powerful insights into the persistence of CRS .The cascade reaction in CRS is one of the main reasons for the occurrence of CRS. We analyzed some of the major inflammatory pathways involved in CRS and pointed out certain cytokines and proteins that closely related to CRS. We also discussed their roles in CRS occurrence, providing important insights and assistance for studies on blocking and weakening CRS in the future.The development of CRS during CAR-T therapy is complex and related to many factors. In the present study, we applied a meta-graph neural network framework, machine learning algorithm, and system biology analysis to determine the cause of CRS and identify cytokines with a probability exceeding 0.95 in the predicted results. Through PPI analysis, functional enrichment analysis, and pathway crosstalk analysis, we identified the biological processes and pathway modules related to CRS to explain the cause of CRS. Our prediction results provide meaningful inferences for CRS and have the value of identifying potentially related cytokines. The results suggest great promise for analysis of the CRS mechanism at the system biology level. With the development of high-throughput technology and increase in medical experimentation, the actual useful cytokines by experimental results will inevitably appear in the results of our prediction and analysis.Additional file 1. Cytokines used in the study.Additional file 2. Interactions between cytokines used in the study.


The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs. The complete biosynthetic pathway from most natural products (NPs) are unknown. Here, the authors report BioNavi-NP, a computational toolkit for bio-retrosynthetic pathway elucidation or reconstruction for both NPs and NP-like compounds. Remarkably, this vast chemical space of NPs is reachable from a few dozen, simple building blocks4. According to the classes of those building blocks, there are correspondingly four well-known biosynthetic pathways for major classes of NPs and their hybrids, including: (1) the AA/MA  pathway that produces fatty acids, phenols, and polyketides; (2) the MVA/MEP  pathway that generates terpenoids and steroids; (3) the CA/SA (cinnamic acid or shikimic acid) pathway that yields flavonoids, phenylpropanoids, lignans, and coumarins; and (4) the AAs (amino acids) pathway that constructs alkaloids and peptides including ribosomally synthesized and posttranslational modified peptides (RiPPs) and non-ribosomal peptide (NRPs). Unfortunately, as shown in Fig. 5, KEGG6 and MetaNetX7). That is, complete biosynthetic pathways including all intermediates are not established for most of the hundreds of thousands of known NPs. Accordingly, there is a strong desire to reveal the biosynthetic pathways from essential building blocks to target NPs (namely the native NPs biogenesis).To date, more than 300,000 natural products (NPs) have been discovered and catalogued in libraries such as Dictionary of Natural Products8. As a result, NPs play a significant role in drug discovery: more than 60% of FDA-proved small molecule drugs are NPs or their derivatives9. NPs are often the best option for seeking novel bioactive templates in human health, as many of them are critical factors for regulating intrinsic biofunctions, especially in plants11. However, obtaining commercially-relevant quantities of NPs can be a major obstacle to their therapeutic translation, since NPs are usually expressed in low abundances in natural sources, and thus conventional extraction approaches are inefficient and environmentally unfriendly in most cases. Meanwhile, many highly valuable NPs have complicated structures, making total synthesis difficult and time-consuming. As famously exemplified by the heterologous biosynthesis of the arteannuinic acid12, biosynthesis and semi-synthesis of complex natural products have become more popular and influential strategies in the past decade because of the theoretical advantages of lower cost and higher yield. Nevertheless, it remains challenging to reconstruct a heterologous biosynthetic pathway from its native pathway. Therefore, computer-aided tools are needed for the retro-biosynthesis analysis of target NPs, especially for the exploration of non-native biosynthetic pathways.NPs have been noted to exhibit larger structural diversity than fully synthetic molecules and exist in a distinct chemical space15, which can be roughly divided into two categories of methods: knowledge-based and rule-based approaches (Supplementary Table 5 and KEGG6) and rank the suggested routes through scoring functions such as chemical similarity17 and chassis18. For complex NPs, these methods are often not applicable when the reactions of their biosynthetic pathways are not included in those databases. The template- or rule-based models  match the query molecule to a collection of generalized reaction rules, i.e., subgraph patterns of molecules that highlight changes during biochemical reactions. The rules are either summarized manually by experts24 or extracted automatically from the reaction databases25. Although rule-based methods have led to promising results in retro-biosynthesis, a few challenges remain. First, their ways to formulate expert-approved rules are complicated and time-consuming25. Second, the degree of generality/specificity of curated rules may lead to invalid or incomplete proposals26. Third, they are fundamentally unable to predict reactions beyond the rule databases27.To date, many efforts have been made in the field of retro-biosynthesis28) as input into sequential models such as recurrent neural networks29 and transformer neural networks30. Such techniques have been utilized to predict the products of both organic32, and enzymatic reactions34 by giving reacting substrates as input, or the reverse for retrosynthesis prediction task36. These rule-free models have shown better performance and greater generalization potential than rule-based models in many cases35. Based on the single-step retrosynthesis prediction, the retrosynthetic pathways can be planned through searching techniques , but MCTS-based methods38 are of limited efficiency due to the required expensive online reward estimation. More recently, Chen et al.39 reported a deep learning-guided AND-OR tree-based searching algorithm called Retro*, and demonstrated improved planning efficiency and solution quality. Nevertheless, multi-step planning algorithms have not yet been applied to NPs retro-biosynthetic planning, mainly due to its much less available data, greater number of steps, and higher branching ratios in the biosynthetic pathways.Recently, the development of deep learning methods has made it possible to predict reactions without rules, where molecules could be represented as strings  through the deep learning-guided AND-OR tree-based searching algorithm, which can solve the combinatorial number of options caused by the branches of the synthetic pathway. As a result, BioNavi-NP successfully identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, demonstrating its potential for bio-retrosynthetic pathway elucidation or reconstruction. For each biosynthetic step in the multi-step bio-retrosynthetic routes, we further evaluate the plausible enzymes through enzyme prediction tools, Selenzyme42 and E-zyme 243. All outputs of the BioNavi-NP can be visualized by an interactive website (http://biopathnavi.qmclab.com/), where the predicted reaction pathways are sorted by the computational cost, length, and organism-specific enzyme.Herein, we present BioNavi-NP as a practical tool to propose NP biosynthetic pathways from simple building blocks in an optimal fashion. As depicted in Fig. 44, we evaluated the performance on the test set using top-n accuracies, defined as the percentages of correct instances among top-n predicted precursors. Considering the complexity of biosynthesis, we further expanded the training set by retrieving 62370 organic reactions similar to these biochemical reactions from USPTO45, the largest organic chemical reaction library available. This strategy was inspired by transfer learning40, where a sufficient amount of relevant data helps to improve model robustness by learning general patterns and avoiding over-fitting. The larger data set of 60 K natural product-like reactions is named USPTO_NPL (see Methods for more details).Multi-step retro-biosynthesis planning is based on the backward search performed through iterative single-step retrosynthesis predictions. Therefore, it is critical to achieve a reliable prediction of single-step precursors at each step. Our first task is to compare various architectures and training modes to determine an optimal model. To train our model, we first curated a biosynthesis data set from the public database called BioChem (see Methods), containing 33710 unique pairs of precursors and metabolites. From the dataset, we randomly selected 1000 pairs as the test set, 1000 pairs as the validation set, and the remaining as the training set. Following the previous retrosynthesis works41 can reduce the variance of different models’ random initializations and improve robustness.As summarized in Table 20, with an increase by up to 1.1% and 18.5% in terms of top-1 and 10 accuracies, respectively. This result demonstrated the power of the deep learning-based methods for the prediction of biosynthetic processes, while the higher accuracy of BioChem + USPTO_NPL than BioChem + USPTO_NPL (ses2seq) demonstrated the value of the attention mechanism by transformer. Hereafter, we would use the BioChem + USPTO_NPL (ensemble) as the basic single-step prediction model if not specially mentioned.Table 20 was set to 1000 iterations and 10 expansions following its default settings. Note that the number of expansion represents the top-N metabolites that the model will predict in every single step, i.e., top-N in Table Next, we investigated the performance of different multi-step planning strategies. We integrated the above trained single-step prediction ensemble model to navigate the multi-step bio-retrosynthesis planning and named it BioNavi-NP. To evaluate the searching performance, we randomly selected 368 diverse NPs with complete biosynthetic pathways from the BioChem dataset, and compared different methods in terms of their abilities to: (i) predict biosynthesis routes that terminate in allowable building blocks (success rate or solution rate), (ii) to correctly find the reported pathway exactly as it appears in the knowledge base (hit rate of pathways), and (iii) to correctly recover the building blocks used in the known biosynthetic pathway (hit rate of building blocks). We introduced the hit rate of building blocks because the pathway of ground truth is not necessarily unique. There may be multiple pathways between a natural product and its building blocks. Even if a candidate pathway is not the ground truth, the candidate is also meaningful as a non-native biosynthetic pathway or an inspiration for pathway reconstruction and design. To ensure a fair comparison, all deep learning models were limited to 100 iterations and 10 expansions, while RetroPathRL20 (52.7%). Meanwhile, BioNavi-NP achieved 56.0% and 24.7% for the hit rates of building blocks and pathways, remarkably outperforming RetroPathRL (4.8% and 3.8%), respectively. It should be noted that RetroPathRL contains the reaction rules extracted from MetaNetX7, while all the internal test NPs were included in MetaNetX. When changing the searching algorithm to MCTS, BioNavi-NP (MCTS) achieved a substantially lower success rate of 34.8%, indicating the importance of searching strategy to manage the highly-branched multi-step search. As users may prefer well-known or user-specific building blocks in practice, we set the building blocks of ground truth as the user-defined building blocks (UDBs), and terminated predictions only with the UDBs. As expected, the BioNavi-NP_UDB model increased both the hit rates of building blocks and pathways  while decreased the success rate (74.7%). The same trend was observed between RetroPathRL and RetroPathRL_UDB.As summarized in Table To guide the reconstruction of the biosynthetic pathway, it is important to explore more than one pathway for each target NPs. Therefore, in addition to the abovementioned hit rate of pathways, we also counted the average number of predicted pathways as a direct indicator for the tool’s practical use. When selecting the output option as top-5  from recent publications that did not appear in the training set or internal test set. Out of these, 22 cases successfully found at least one candidate pathway with a success rate of 88%, roughly the same as the 90.2% in the internal test. For the 3 remaining cases, incomplete results were still outputted for analysis. 17 cases had correctly identified at least one building block with the hit rate of building blocks of 68%, higher than the 56% in the internal test  and the pathways (hit rate of pathways). For a fair comparison, we used the same building block library (extended library) containing 437 available precursor metabolites extracted from iML1515 model46, as used in RetroPathRL. The parameters of BioNavi-NP were set to a maximum of 100 iterations and 10 expansions. As shown in Supplementary Table For a direct comparison with existing works, we tested BioNavi-NP on two benchmark datasets (namely LASER and Golden dataset) used by RetroPathRL47 did, only the structure of the target molecule is strictly required, but the default settings and list of building block can be modified as needed. The total number of output pathways is influenced by several options, especially the “pathway_top_k” (default is top-10). The length of the pathways is affected by the “Max_depth” (default is 10). One can increase the number to a maximum of 20 upon request. These default settings  and colletorin D acid (intermediate 4) to lead the fourth and fifth candidate pathways, respectively, where the fourth candidate pathway was ultimately traced back to the building block farnesyl diphosphate (3), while the fifth candidate way was a hybrid biosynthesis style originated from the acyl CoA (5), malonyl CoA (6), and dimethylallyl diphosphate (7). Both the fourth and fifth candidates are confirmed biosynthetic pathways according to the previous studies53. This case shows us that BioNavi-NP is capable of dealing with complex structures including those derived from the hybrid pathway and to trace them back to essential building blocks.Sterhirsutin J is a sesquiterpenoid derivative firstly isolated from the culture of 54. Herein using BioNavi-NP, the predicted third candidate pathway belongs to one of the lysine (8) degradation pathways55, and the seventh one is highly similar to an experimentally reconstructed pathway for glutaconate production56, both of which are already existed in our training set, so it is not surprising that BioNavi-NP can predict those two pathways which have been constructed in E. coli for glutarate production58. The fourth candidate is not contained in our training set, and it takes a successive decarboxylation strategy from homoisocitrate (9), which is predicted to be originating from the basic building block pyruvate (10). Interestingly, Wang et al.59 recently established a glutarate biosynthetic pathway from α-ketoglutarate by incorporation of a “+1” carbon chain extension and α-keto acid decarboxylation, which covers the fourth candidate pathway predicted herein. This case study is a typical example to show the capability of BioNavi-NP in exploring the biosynthetic pathways beyond the known biogenesis. Thus, it is useful for biosynthetic pathways redesign.Glutarate  is an important raw material for the chemical industry, but biobased production of glutarate suffers from low titers62 to aid experimental biosynthesis.Herein, we proposed a practical retro-biosynthesis protocol for navigating biosynthetic pathways to complex NPs from simple building blocks. In contrast to rule-based models, BioNavi-NP is a fully data-driven model that is constructed based on curated biosynthetic and organic reaction data without the need for cumbersome or heuristic extraction of reaction templates. Comprehensive evaluations on internal and external test sets demonstrate that our method achieves a high success rate  when generating biosynthetic routes to complex NPs that recapitulate known pathways and building blocks, particularly in comparison to other methods at a comparable computational time. By combining an existing enzyme prediction tool, we further provide a user-friendly server that can not only predict biosynthetic pathways but also rank the biological feasibility of these pathways according to the estimated preference of species and enzymes. This is a valuable feature considering that template-free methods often predict new reactions outside of current knowledge. We have validated the potential of this webserver to predict native biosynthetic pathways as well as redesigning pathways for complex NPs, although we acknowledge that the pathway scoring function is not perfect due to the limited data availability and inherent limitations of the search algorithm. Therefore, extra modules that link the predicted intermediates and reactions to the known data sources were added to the webserver to offer annotation and facilitate the re-ranking of pathways. In this sense, we have improved the reproducibility of the reported biosynthesis routes and the enumeration ability of alternative biosynthetic pathways, provided an easy operational and visualizable webserver with some key options for users to balance accuracy and efficiency in practical use, and also supplemented multiple criteria for judging the rationality of the predicted pathways and building blocks. Future improvements can be made by providing further information, such as the promiscuity, fidelity, and diversity of enzyme catalysts, and introducing advanced enzyme prediction and design tools64 into the rule-free models will allow better mass-balance for the single-step retro-biosynthesis predictions.There still remain certain challenges for the refinement of the BioNavi-NP. For example, at present, it could not correctly predict the biosynthetic starting materials for some complex NPs involving many building blocks or reaction steps, even though a much longer pathway could be found compared to some other methods Table . AlternaIn summary, this work combines transformer models and the Retro* search algorithm to develop a leading-edge biosynthesis navigator (BioNavi-NP), which can enumerate diverse biosynthetic pathways and trace natural products back to biologically-plausible building blocks. BioNavi-NP achieves a high success rate at generating biosynthetic routes and searching building blocks for NPs. Therefore, it is promising for native biogenesis analysis, biosynthetic pathway reconstruction, and rational design.7 integrated a number of major resources of metabolites and biochemical reactions including MetaCyc5, KEGG6, The SEED65, Rhea66, BiGG67 and so on. However, the information regarding reaction directionality and compartmentalization were generally disregarded, which are important to predict the precursors of the NPs. So we first extracted reactions directly from MetaCyc (version 23.5) pathway ontology and KEGG  pathway maps, where the “irrelevant” agents such as cofactors and minor substrates or products were removed. These reactions were excluded from MetaNetX (version 4.1) dataset, and following the previous work34, we derived pairs of precursors and metabolites where the maximum common substructure exceeds 40% of the atoms of the metabolite for the rest of the reactions. Then, multi-product reactions were decomposed to multiple mono-product reactions with the same substrates and the cofactors were removed. Compounds containing Coenzyme A (CoA) play an important role in the biosynthesis of natural products especially polyketides and lipids. Although CoA is a complex structure that contains 84 heavy atoms, it is usually not involved in the reaction center, so the CoAs in the molecules were replaced by “*” in the SMILES format and restored when the results were output. Considering that the stereochemistry is often poorly annotated in the structure databases, we firstly checked the structures in our reaction dataset. The potential stereochemical centers exist in 15,372 out of 20,710 structures, and 10,909 (71.0%) of them are completely annotated with the stereochemistry, 12,763 (83.0%) are annotated with at least half of the stereochemistry. Thus the stereochemistry was preserved in our dataset. The resulting BioChem dataset consists of 33,710 unique pairs of precursors and metabolites, 1000 of which were selected as the test set and another 1000 as the validation set  between any component (reactants and/or products) of each reaction in the USPTO database and the NPs from the DNP1 database (version 27.2), resulting in 60,000 chemical reaction equations. The USPTO_NPL dataset was merged with the BioChem dataset to train the single-step model.MetaNetX version 3.5 pathwTo obtain a set of target natural products for the quantitative evaluation of a multi-step model that contains different scaffolds, structures from the BioChem dataset were clustered according to their biosynthetic building blocks. Then 368 target molecules with complete biosynthetic pathways  were randomly extracted from the different clusters, which can be further divided into five broad categories . Moreover, 25 unseen natural products  whose biosynthetic pathways are unclear or not included in the data source were chosen to evaluate the generalization of our model.4.Given an input of a target molecule, our task is to recursively decompose the molecule into available biogenetic precursors following the biosynthesis scheme. In this study, a biogenetic reaction was described by a variable-length string containing one pair of SMILES notations representing the reactants and target compound  on the training set, saving one checkpoint every 10,000 steps and averaging the last five checkpoints. Hyperparameters are selected based on the performance of the model on the validation set. A beam search procedure72 was then used to infer multiple precursors candidates on the test set. We used the best performance model to infer the candidate sequences of precursors with a beamwidth of 10. As a result, the top10 candidate sequences ranked by total probability were retained. The training details can be seen Supplementary Method The single-step prediction was made by the transformer neural networks39  and the total cost is the sum of all steps scores along the pathway.For multi-step planning, we adopt Retro*Each time a full pathway is found during the tree expansion, the pathway is returned, and an additional bonus is received by the node, to allow for biasing toward similar successful pathways. At the end of the search, the most visited pathway is returned (i.e. the best one), and all pathways are returned ranked in order of decreasing cost. When performing multi-step evaluation on the internal set, we set the beam size of the transformer, max route, iteration, max depth as 10, 5, 100 and 10, respectively. It is worth noting that increasing the width, depth of the search and the number of iterations will certainly improve the accuracy, but the time consumption will also increase exponentially.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


After the World Health Organization declared coronavirus disease 2019 (COVID-19), as a global pandemic, global health workers have been facing an unprecedented and severe challenge. Currently, a mixturetion to inhibit the exacerbation of pulmonary inflammation caused by COVID-19, Fuzheng Yugan Mixture (FZYGM), has been approved for medical institution mixturetion notification. However, the mechanism of FZYGM remains poorly defined. This study aimed to elucidate the molecular and related physiological pathways of FZYGM as a potential therapeutic agent for COVID-19. Active molecules of FZYGM were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), while potential target genes of COVID-19 were identified by DrugBank and GeneCards. Compound-target networks and protein-protein interactions (PPI) were established by Cytoscape_v3.8.2 and String databases, respectively. The gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Finally, a more in-depth study was performed using molecular docking. Our study identified 7 active compounds and 3 corresponding core targets. The main potentially acting signaling pathways include the interleukin (IL)-17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation, and coronavirus disease-COVID-19. This study shows that FZYGM can exhibit anti-COVID-19 effects through multiple targets and pathways. Therefore, FZYGM can be considered a drug candidate for the treatment of COVID-19, and it provides good theoretical support for subsequent experiments and clinical applications of COVID-19. COVID-19 has a relatively long incubation period and highly contagious. Up to Octuber 22, 2021, more than 4.9 million fatalities and 242.3 million infections have been recorded, making it one of the most widespread pandemics in history. The main clinical manifestations of COVID-19 are including fever, cough, respiratory distress, severe gastrointestinal distress and acute respiratory distress. The consequences of COVID-19 also severely affect the prognosis of patients. In China, the spread of the epidemic has been effectively controlled and effective treatment has been carried out for patients with COVID-19. Among them, traditional Chinese medicine (TCM) has made an important contribution to the prevention and control of the epidemic, such as anti-inflammatory, improving immunity, and stimulating their own resistance to disease and recovery ability.The coronavirus disease 2019 (COVID-19) pandemic is caused by the rapid international spread of a coronavirus called SARS-CoV-2.Hedysarum Multijugum Maxim, Saposhnikoviae Radix, Atractylodes Macrocephala Koidz, licorice, Lonicerae Japonicae Flos, Pogostemon Cablin (Blanco) Benth and Perilla Frutescens.Fuzheng Yugan Mixture (FZYGM) is a hospital preparation of Tongde Hospital of Zhejiang Province. This mixture has now been passed by Zhejiang Provincial Drug Administration and was approved for medical institution preparation filing (NO. Z20200043001) for the prevention and control of pneumonia epidemic with COVID-19 infection. According to the theory of Chinese medicine, “the body is full of positive energy, so that the pathogenic agents hardly invade the body.” To prevent influenza and pneumonia, it is advisable to start with benefiting the Qi and strengthening the body. This mixture is based on the classical mixture Yu Ping Feng San and composed of 7 herbs, namely Hedysarum Multijugum Maxim possesses detoxifying properties and exhibits bioactivities such as immunomodulatory and anti-inflammatory effects.Saposhnikoviae Radix has been used for the treatment of common cold and cough, which demonstrated antimicrobial, antiviral and antioxidant activities.Atractylodes Macrocephala Koidz is a strengthening spleen drug for eliminating phlegm and dampness in China.licorice has been used to relieve coughing and alleviate pain since ancient times.Lonicerae Japonicae Flos is a commonly used medicine to treat phyma, sore and fever.Pogostemon Cablin (Blanco) Benth and Perilla Frutescens have been used for respiratory symptoms, such as asthma, cold, fever, and stuffy nose.,11 Among them, Perilla Frutescens also has been used for some intestinal disorders. However, there are no comprehensive analysis of pharmacological mechanisms about this mixture. Therefore, the objective of this study is to provide essential functional chemical compounds and associated physiological pathways of FZYGM.,13 This network involved in the analysis has laid the foundation of active ingredients and mechanism of Chinese herbal compound. Molecular docking technology can predict the binding stability between drug and receptor by predicting the interaction mode and binding energy between receptor and drug molecules, which can be used as an important tool for drug screening and design.,15 In this study, our main objective is to explore the potential mechanism of FZYGM for the treatment of COVID-19 using molecular docking and network pharmacology approaches, providing a theoretical basis to support this mixture. We will also identify the active ingredients that play a role in the mixture. The detailed workflow diagram of the study is shown in Figure Network pharmacology can integrate Chinese medicine, Chinese medicine ingredients and component targets to build the herbal-component-target network.https://old.tcmsp-e.com/tcmsp.php), the active ingredients in FZYGM Chinese medicine and the target proteins corresponding to the active ingredients were screened with the oral bioavailability greater than or equal to 30%–18 and drug-likeness greater than or equal to 0.18 as the conditions. The data filtered by the TCMSP database were processed and the relationships established between the selected herbs and their active ingredients were imported into the software Cytoscape_v3.8.2 for visualization and analysis, and a network relationship diagram between them was generated.Using the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) database (https://www.genecards.org/) and DrugBank (https://www.drugbank.ca/), using the keyword “COVID-19.”Search for disease-related genes in 2 major databases, GeneCards (https://www.uniprot.org/), the gene names of the target proteins corresponding to the active ingredients of traditional Chinese medicine were searched, and the FZYGM gene database was set up to join the originally constructed database. The gene databases of FZYGM and COVID-19 were imported into Venny 2.1.0 online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) for cross-tabulation analysis, and the common genes of FZYGM and COVID-19 were derived.Using the protein database Uniprot  is a database for building functional protein association networks. Select the function module “Multiple Proteins,” import the cross-targets of FZYGM and COVID-19, select the organisms “Homo sapiens,” and derive the PPI network of common targets. Export the PPI network to a “tsv” file.STRING  for GO and KEGG pathway enrichment analysis, from which bubble charts or histograms can be drawn for visual analysis. Select the latest KEGG database, set P < 0.01 and the size of the final output image, and choose the top 20 results with the highest enrichment. Finally export the “xlsx” file and picture file. Hiplot, as a visualization-based statistical analysis tool, is essentially a simplified use of the R language. The R package “clusterProfiler” is applied in the enrichment tool we use. In the histogram, the smaller the P value, the higher the degree of enrichment; the longer the bar, the more enriched genes.In order to reflect the biological processes (BP), molecular functions, cell composition (CC) and pathways of FZYGM in the treatment of COVID-19, the core genes were introduced into the visualization-driven bioinformatics analysis online website Hiplot (https://www.kegg.jp/) to create signaling pathway database. The database was imported into Cytoscape_v3.8.2 for visual analysis. In the established FZYGM database, the core genes enriched by signaling pathway were searched and the corresponding active ingredients were found to establish the relation table for molecular docking.After selecting the required signaling pathways from the KEGG enriched signaling pathways, the gene IDs enriched in each signaling pathway were queried in the exported “xlsx” worksheet, then retrieved and converted to gene names using the KEGG database (https://pubchem.ncbi.nlm.nih.gov/) in SDF format and then converted from SDF to MOL2 format using Chem 3D software. The protein structure was downloaded from the RCSB database in the PDB format. PyMOL software was used to modify the protein molecules obtained from the PDB database, and the “remove waters” command was used to remove the water of crystallization from the protein structure. At the same time, use the “GetBox” plug-in, select the ligand to obtain the coordinates of the active pocket at the time of docking, use the “remove” command to remove the ligand, and then export the protein molecule in pdb. format to obtain the original ligand of the target protein. The original ligand of the target protein can also be obtained. Using the “Edit” module of the GUI tool in AutoDock Tools, each protein is assigned a polar hydrogen atom, a charge, and an atom type, while the “Torsion Tree” is used to determine. After completion, the target proteins and small molecules are exported in pdbqt. format for molecular docking. The target proteins and small molecules in pdbqt. format obtained after pretreatment were used as objects for molecular docking using AutoDock Vina software. Each molecule was docked from 10 conformational angles, and the binding free energy resulting from the receptor-ligand interaction obtained in each orientation during docking was calculated to determine the optimal conformation of the complex. Finally, Discovery Studio 2020 software was used for the constitutive relationship analysis.Molecular docking can explain the interaction mechanism and binding activity of the active component with the target protein to some extent. The structure of the compound was downloaded from the Pubchem database  Benth and Perilla Frutescens) contained a total of 167 active ingredients. Figure Based on the ADME model, we screened 7 herbal medicines  were selected to construct the circle diagram shown in Figure P-value < 1.0e-16.The 43 core targets derived from Cytoscape_v3.8.2 analysis were imported into the STRING online database, and the PPI graph was constructed as shown in Figure The results of GO enrichment can be seen in Figure Among the top 20 signaling pathways enriched by KEGG, interleukin (IL)-17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, toll-like receptor (TLR) signaling pathway, Th17 cell differentiation, and Coronavirus disease - COVID-19 5 signaling pathways were analyzed, which included 29 core genes. Then the corresponding active ingredients were collected from the FZYGM database, and 60 active ingredients related to them were found, and there were 6 kinds of related traditional Chinese medicines. These results were imported into Cytoscape_v3.8.2 for visualization and analysis, and the results were shown in Figure The 29 core genes were molecularly docked with their corresponding active ingredients to verify the effects of the active ingredients, and the docking results are shown in Table We found that MAPK14 showed good stability in binding to other proteins, including (2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyranochromen-4-one with MAPK14 at a binding energy of −10.2 kcal/mol, forming hydrogen bonds via Thr106 and hydrophobic interactions with Phe169, Leu75, Leu171, Ile84, Val38, Leu108, Ala51, and Val30  screening, and a total of 83 cross-targets were obtained by PPI network -2-[4-hydroxy-3-(3-methylbut-2-enyl) phenyl]-8,8-dimethyl-2,3-dihydropyrano chromen-4-one, Phaseolinisoflavan, Glabrene, quercetin, shinpterocarpin, irisolidone and kaempferol have good binding activity with MAPK14, STAT1 and RELA targets  was reported by Saccani et al MAPK14 is one of the 4 p38 MAPKs which confirm a crucial role in the cascades of cellular reactions induced by an extracellular stimulus such as proinflammatory cytokines. MAPK14 pathway regulates the translational level of TNF which can further stimulate NF-κB non-canonically.,50 Hence, increased activation of MAPK14 and cross-talk activation of NF-κB pathway might be the reason for the thrombosis, inflammation and vasoconstriction in COVID-19. Anomalous upregulation of STAT1 is found in B and T cells and monocytes from mild and severe COVID-19 patients compared to controls, While the phosphorylation of STAT1 is enhanced in severely affected COVID-19 patients. RELA, also named p65, is an important member of the NF-κB that play an essential role in inflammation,–55 while excessive activation of the NF-κB pathway has been thought to be associated with the pathogenesis of the severe/critical COVID-19 phenotype.To validate the KEGG enrichment results, we molecularly docked the core targets in the above 5 signaling pathways with the corresponding Chinese herbal active ingredients.We revealed the pharmacological targets and signaling pathways of FZYGM for the treatment of COVID-19 by using network pharmacology and molecular docking techniques. Our results indicate that FZYGM can act at multiple cellular sites and participate in multiple biochemical responses, acting through mechanisms of inflammation, cell necrosis, and immune-related signaling pathways. Based on the validation results of molecular docking, we also know that the pharmacological effects of FZYGM in treating COVID-19 may be achieved by modulating the core targets of its related signaling pathways. These results suggest that FZYGM may exert anti-inflammatory effects through multiple pathways to treat COVID-19. It should be noted that due to the limitations of computer prediction methods, network pharmacology and molecular docking are only a preliminary exploration of the potential mechanism of drug action, and further experimental studies are still needed to validate it subsequently. However, due to the limitation of the number of patients, the therapeutic effect for COVID-19 still needs to be confirmed in future clinical work.In this study, network pharmacology and molecular docking were used to harvest the pivotal biotargets and potential therapeutic mechanisms of FZYGM in the treatment of COVID-19. Our results indicate that FZYGM can achieve intervention of COVID-19 by regulating the level of inflammatory cytokines and immunomodulation. Molecular docking showed that FZYGM could initiatively bind to the receptor and had strong binding energy, suggesting multi-dimensional mechanism. We also identified active ingredients (2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl) phenyl]-8,8-dimethyl-2,3-dihydropyrano chromen-4-one, Phaseolinisoflavan, Glabrene, quercetin, shinpterocarpin, irisolidone and kaempferol in FZYGM that may play a key role in the action of FZYGM on COVID-19. Conclusion of this study provides better support for subsequent experiments and pharmacological mechanism in the treatment of COVID-19.The authors are thankful to Tongde Hospital of Zhejiang Province for providing assistance with the pharmaceutical mixturetion of this study. At the same time, particular thanks to Hua Yan from Wenzhou University for her help in improving the language quality of this article.Conceptualization: Jie Zhou, Tianyue Wang.Funding acquisition: Jie Zhou.Methodology: Tianyue Wang.Project administration: Jie Zhou.Software: Xinyu Jiang.Supervision: Tianyue Wang.Validation: Zhongming Yu, Xueya Gu.Visualization: Xinyu Jiang, Yanmin Ruan.Writing – original draft: Xinyu Jiang, Jie Zhou, Tianyue Wang.Writing – review & editing: Jie Zhou, Ying Lu, Tianyue Wang.