In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, and congenital disorder of metabolism. 207 KEGG pathways were enriched with the targets, and the main pathways were metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, and pathways in cancer. In conclusion, in silico target fishing and network analysis revealed therapeutic activities of the phytochemicals, demonstrating that Ethiopian indigenous aloes exhibit polypharmacology effects on numerous genes and signaling pathways linked to many diseases. Natural products’ bioactive components have been widely used for treating a broad spectrum of diseases for centuries and are considered as promising alternative therapeutic agents due to their potential healing effects2. Over the past three decades, there has been a significant increase in the usage of natural product medicines, with 80% of people globally relying on them for some aspect of basic healthcare3. The use of plants as a source of natural products and as the basis for traditional medicine systems across many different cultures has been extensively documented for thousands of years5.Healthcare systems around the world are encountering increased levels of pandemics, chronic illnesses, population aging and escalating healthcare costs. Patients and healthcare providers alike are demanding that healthcare services be revitalized. This includes improving access to traditional, complementary, and alternative medicine (TCAM) practitioners, products, and services, particularly in primary healthcare6. According to literature records, 74% of the uses of the genus Aloe were as medicine, and exudate was frequently used in these applications7. Aloe leaves have been used for thousands of years as a primary remedy for skin conditions, burns, and constipation8. Additionally, there are numerous commercial products that contain aloe species, such as laxative medications, health beverages and tonics, after-shave gel, mouthwash and toothpaste, hair tonic and shampoo, and skin-moisturizing gels10. Aloes were important in Ethiopia for their therapeutic properties as well as for securing livelihoods, fostering economic growth, and protecting biodiversity9. With 46 known aloe species and three subspecies, Ethiopia is one of the likely centers of aloe biodiversity. Of these species, 67.3% are indigenous to the nation12. In Ethiopia, three regional centers of endemism have been identified10: (1) North and central highlands and west of the great rift valley have endemic species and infraspecific taxa of Aloe adigratana, A. ankoberensis, A. benishangulana, A. camperi, A. clarkei, A. debrana, A. elegans, A. monticola, A. percrassa, A. pulcherrima, A. schelpei, A. sinana, A. steudneri, A. trigonantha, and A. weloensis; (2) Eastern highlands and lowlands contains endemic species of Aloe bertemariae, A. harlana, A. mcloughlinii, A. megalacantha subsp. alticola, A. pirottae; A. pubescens; and A. trichosantha subsp. Longiflora; and (3) Southern highlands, lowlands and rift valley with species Aloe elkerriana, A. friisii, A. ghibensis, A. gilbertii subsp. gilbertii, A. gilbertii subsp. megalacanthoides, A. jacksonii, A. kefaensis, A. otallensis, A. tewoldei, A. welmelensis, and A. yavellana.Plants of the genus Aloe are the most common traditional medicinal plants in recorded history 8. However, major active ingredients, associated diseases, and their mode of action, particularly for indigenous Ethiopian aloes, are not well understood. This hampered their widespread usage in the development of drugs.A great deal of effort is being done to identify the aloes’ phytochemical compositions because of their extensive medicinal uses for long period of time. Aloe species' leaves and roots are reservoirs for a variety of intriguing secondary metabolites, including alkaloids, anthraquinones, pre-anthraquinones, anthrones, bianthraquinoids, chromones, flavonoids, coumarins, and pyrones13. Furthermore, researchers are constantly investigating new methods to increase the efficiency of the drug discovery process14. Utilizing computer-aided drug design (CADD), often known as the in silico method, is one strategy to improve the effectiveness of novel drug development. Through the use of molecular modeling that enables virtually screening a large number of compounds for drug-likeness properties and their interaction with pharmacological targets, CADD reduces of the drug development time and overall research expenses13. Compound in silico activity profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects, and drug repurposing15. It can be applied in many different fields including drug repositioning, natural product profiling and multitarget approaches16. Studies on the new uses of already established drugs are being carried out in addition to classical drug development. In silico profiling of old drugs can help to reposition such old drugs for new uses18. Compounds that emerge from phenotypic-based screening including empirical knowledge have usually a completely unknown mechanism of action (MoA). Pharmacophore-based activity profiling of natural products enables to predict their mechanism of action and guides in proper planning of in vivo and in vitro experiments. In addition, previously unknown lead therapeutic natural products with interesting chemical scaffolds can be profiled16. This will help to prioritize compounds based on their predicted target profile. The understanding and the prediction of the synergistic role of polypharmacology is also one of the main applications of in silico target profiling19.Finding bioactive compounds to aid in the treatment of diseases is the goal of the drug development process. In the past, the new drug discovery process begins with random screening and empirical observations of the effects of natural products for known diseases. Currently, this process is improved by high throughput screening (HTS), which enabled the screening process of many thousands of compounds against a molecular target or cellular assay very quickly20. Structurally distinct molecules with similar pharmacophore patterns are likely to be recognized by the same binding site of a biological target and thus have similar biological profiles21. Pharmacophore models are typically used when several drugs (active compounds) have been identified, but the three-dimensional (3D) structure of the target protein or receptor is unknown. Drugs are superimposed to determine their common properties, providing a pharmacophore model that describes ligand-receptor binding22. Any atom or group in a molecule with specific properties related to molecular recognition can be reduced to pharmacophore properties23. These molecular patterns could be hydrogen bond donors or acceptors, cationic, anionic, aromatic or hydrophobic, and any possible combination24. For a set of active molecules, pharmacophore identification involves two steps: analyzing the molecule to identify its pharmacophore properties, that is, atoms that can interact with a receptor, and aligning the active conformations of the molecules to find the best overlay of the corresponding features22.Pharmacophore model-based in silico profiling for the compounds from the Ethiopian indigenous aloes was done in this study. A pharmacophore is an abstract description of the molecular features (both steric and electronic) required for the molecular recognition of ligands by biological macromolecules, influencing their biological processes25, and network analysis of the predicted targets were investigated. To the best of our knowledge, no computerized comprehensive target fishing approach has been used to identify the pharmacological potential of Ethiopian indigenous aloes’ phytochemicals.In this study, ADMET profiling and pharmacophore model-based in silico target prediction of the phytochemicals from Ethiopian indigenous aloes was performed. To identify related diseases and reveal the mechanism underlying the therapeutic effects of aloes' phytochemicals, gene ontology (GO), KEGG pathway27. Utilizing physicochemical properties that were compiled from the PubChem database28 and SwissADME webserver29, the phytochemicals' drug-likeness was assessed. ADMET-related properties of phytochemicals from Ethiopian indigenous aloes were predicted using admetSAR web server—a database which contains ADMET-associated properties curated form a large number of diverse literatures27. Prior to in silico profiling of the phytochemicals, we excluded potentially harmful substances that are AMES mutagens, carcinogens, and hERG inhibitors using the data from admetSAR.Physicochemical properties affect pharmacodynamics and pharmacokinetics of molecules. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of a molecule are important factors in successful drug and other industrial chemicals development30 and Veber’s rule31 to assess the drug-likeness properties of phytochemicals from Ethiopian indigenous aloes. However, it should be noted that many of the most successful drugs do not fit these guidelines32. For oral drugs from natural products or natural product derivatives, 2 or 3 rule of five violations are common33. As shown in Table We used Lipinski’s rule of 5P), topological polar surface area (TPSA), water solubility, gastrointestinal absorption (GIA), and blood–brain barrier (BBB) permeability were presented in Table 2, which is benchmarked for marketed drugs35. TPSA has a positive correlation with mass and the molecules with a mass higher than 500 g/mol were observed to have TPSA beyond the range of 0–14035. TPSA values and GIA for the majority of the phytochemicals from indigenous Ethiopian aloes were acceptable. The Log p value and Abbott bioavailability score of greater than zero indicate that the phytochemicals have a substantial bioavailability and cross the cell membrane efficiently36. GIA and BBB permeability are important properties of a drug that is intended for widespread use. There have been numerous prior researches to improve GI permeation of molecules for orally delivered, poorly absorbed medicines37. A significant majority of the phytochemicals from indigenous Ethiopian aloes exhibited acceptable GIA and cross the blood–brain barrier.ADME-related properties such as molecular weight (MW), octanol–water partition coefficients  influences the ADMET properties of many xenobiotics , and it is important to investigate the interaction between the transporter protein and the drug molecule41.Cytochrome P450s (CYPs) represent a large class of heme-containing enzymes that are necessary for the detoxification of foreign chemicals and the metabolism of drugs. Cytochrome P450 enzymes can be inhibited or induced by drugs, resulting in clinically significant drug-drug interactions that can cause unanticipated adverse reactions or therapeutic failures. Inhibitors block the metabolic activity of one or more CYP450 enzymes whereas inducers increase CYP450 enzyme activity by increasing enzyme synthesis. Hence, cautions should be taken when prescribing a drug known to be a CYP450 inhibitor or inducer. The target drug may need to be substituted or the dose adjusted to account for a potential decrease or increase in metabolismThe profiling results were shown in two HTML tables designated MoleculeFits and PharmacophoreFits. The two measures utilized to assess the ligand and pharmacophore’s fitness were fit-value and shape similarity. Targets were chosen from the ligand profiler using a fit-value criterion of equal to or higher than 0.5.42 network was employed for further analysis. A complete list of the 1,885 phytochemical-target pairs with target short name, gene and protein IDs, UniProt-ID is presented in , 213 molecular functions (MFs), and 101 cellular components (CCs) related to the 82 predicted target proteins/genes were identified  activity were the major MFs related to A. adigratana Reynolds’ phytochemicals. Similarly, 23 targets were predicted for phytochemicals found in A. camperi Schweinf. These targets were enriched in KEGG pathways of steroid hormone biosynthesis, chemical carcinogenesis, Th17 cell differentiation, circadian rhythm, progesterone-mediated oocyte maturation, cellular senescence, p53 signaling pathway, small cell lung cancer, and IL-17 signaling pathway and so on. The main enriched BPs for this aloes’ phytochemicals targets included: steroid metabolic process, steroid biosynthetic process, protein phosphorylation, transcription initiation from RNA polymerase II promoter, phosphorylation, DNA-templated transcription initiation, glucocorticoid biosynthetic process, glucocorticoid metabolic process, and regulation of cellular carbohydrate metabolic process. Furthermore, the main MFs associated with this aloes’ phytochemicals were: protein serine/threonine kinase activity, steroid dehydrogenase activity, heme binding, ketosteroid monooxygenase activity, oxidoreductase activity, steroid hydroxylase activity, and oxysterol binding. In a similar fashion, phytochemicals from other aloe species have related KEGG pathways, BPs and CCs as shown in Supplementary Figure SIn addition, we performed KEGG pathways, GO and network analysis for each of the 10 aloe species used in this study. The figures are presented in Supplementary Figure SBy comparing the top 20 biological processes and pathways enrichment data, 42 targets were found in common and the top ten pathways related to these targets were steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, IL-17 signaling pathway, pathways in cancer, Th17 cell differentiation, progesterone-mediated oocyte maturation, prostate cancer, kaposi sarcoma-associated herpesvirus infection, and human T-cell leukemia virus 1 infection.According to pharmacological profiling, phytochemicals from Ethiopian indigenous aloes were linked to cancers (66 targets), endocrine diseases (16 targets), metabolic diseases (15 targets), immune system diseases (5 targets), nervous system diseases (11 targets), congenital disorders of metabolism (3 targets), musculoskeletal diseases (3 targets), digestive system diseases (3 targets), hematologic diseases (2 targets), infectious diseases (3 targets), mouth and dental diseases (1 targets). Some of these pharmacological activities are related to traditionally reported medicinal uses of the aloes.From our analysis, we found that around 10 cancer types namely adenocarcinoma, breast carcinoma, glioma, malignant neoplasm of breast, malignant neoplasm of prostate, mammary neoplasms, prostatic neoplasms, squamous cell carcinoma of esophagus, leukemia, and melanoma were related to 65 targets that were linked to 34 compounds from all of the 10 aloe species used in this study. Supplementary Table SA. adigratana Reynolds phytochemicals were predicted to have therapeutic effect against several cancers , endocrine and metabolic diseases , immune system diseases , digestive system disease (ulcer), and infectious diseases (ulcer and colitis). In northern Ethiopia, A. adigratana Reynolds was traditionally used to treat a variety of diseases, including wounds, infections, and inflammation50. Previous in vivo and in vitro studies also support the use of A. adigratana Reynolds in traditional medicine by confirming its anti-inflammatory properties, which were at least partially related to the presence of aloin A/B and microdontin A/B in the leaves50.A. camperi Schweinf. phytochemicals were predicted to have therapeutic effect on cancers , and endocrine and metabolic diseases . Although there is little information on the traditional utilization of the plant, it is claimed to be used for wound healing and malaria treatment51.The potential therapeutic activity of the active phytochemicals inferred in this study should be further investigated using in vivo and in vitro scientific experiments on the predicted diseases.There were a total 17,358 protein nodes in the CODA PPI network Fig. .Figure 5p ≤ 0.05) binding energies compared to the docking scores of random compounds from PubChem to random targets from PDB, confirming the interactions predicted by pharmacophore models in this study. The binding affinity energies, interacting amino acid residues, docking center and docking size between selected phytochemicals and targets are shown in Table The docking results showed successful binding of the ligands to the target proteins with significantly lower  with target protein CDK2. MD trajectories over 100 ns for the protein—ligand complex was analyzed to investigate the stability of the proteins with bound ligand using root-mean-square deviation (RMSD) parameter. In order to examine the conformational variation of target protein upon interaction with ligand, RMSD for protein—ligand complex, ligand and target protein with respect to the starting structure was calculated and depicted as shown in Fig. 52. To determine whether the protein was compact, the systems' gyration radii were measured, and a graph showing how they are related to the simulation time was created53 as shown in Fig. An excellent analysis of the dynamics of protein collapse is provided by the time development of the radius of gyration (Rg)54. The three-dimensional (3D) structure data files (SDF) of the phytochemicals were collected from PubChem database55. The physicochemical properties of the phytochemicals were retrieved from PubChem database and SwissADME webserver29. The phytochemicals were filtered for toxicity using admetSAR. Then, we used PharmaDB pharmacophore and target databases of Discovery Studio56 to perform target fishing for the phytochemicals. The hit pharmacophore models were picked out according to the threshold of a predetermined fit value (0.5). After analysis of the hit targets and their associated pathways and diseases, as well as the interactions between the phytochemicals and the targets, an action network of Ethiopian indigenous aloes phytochemicals was constructed. Furthermore, Molecular docking was carried out to refine the binding modes and to verify the findings. Finally, molecular dynamics (MD) simulation was performed for the best binding interaction of the protein—ligand complex.In this study, pharmacophore model-based ligand profiling was used in target fishing to predict potential targets of compounds from Ethiopian indigenous aloes. Figure 54 and the literature. Accordingly, we compiled 35 compounds from 10 Ethiopian indigenous aloes after removing potentially toxic compounds and evaluating their ADMET and drug-likeness properties. And then, targets were successfully predicted for the phytochemicals using computational target fishing. All 35 phytochemicals’ 2D chemical structures and compound classes are listed in Fig. 57 into 9 compound classes as anthracenes, benzene and substituted derivatives, fatty acyls, organooxygen compounds, phenol esters, prenol lipids, saturated hydrocarbons, steroids and steroid derivatives, and unsaturated hydrocarbons.The phytochemical constituents of Ethiopian indigenous aloes were collected from our own database28 and SwissADME webserver29. SwissADME is a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. We used PubChemPy—a python library used to interact with PubChem database to retrieve information related to phytochemicals from PubChem database.Physicochemical properties of the phytochemicals were retrieved from PubChem database58. These molecular properties are related to some basic structural or physicochemical properties such as log p (partition coefficient), molecular weight (MW), topological polar surface area (TPSA), hydrogen bond acceptors and donors count in a molecule. There are several sets of criteria to select ligands for a potential drug. Most notably, Lipinski30 identified common properties that are frequently observed in approved compounds. The authors presented a “rule of 5” for drug-like molecules  molecular weight, and less than 5 log p (partition coefficient between organic and aqueous phases)). In addition, Veber’s rule31 suggested that compounds which meet only the two criteria of 10 or fewer rotatable bonds and polar surface area equal to or less than 140 Å2 have a high probability of good oral bioavailability in rat. However, many of the most successful drugs do not fit these guidelines, and care should be taken in the application of these guidelines32. In addition, most of compounds from natural products do not always follow the rules which are commonly applied in modern drug design including Lipinski's “Rule of Five ”21.We used compounds with two or more Rule of 5 violations which are used in drug development by medicinal chemists and industries59 as a reference for drug-likeness evaluation of the phytochemicals in this research.The drug-likeness properties of the phytochemicals were predicted using the physicochemical properties. Drug-likeness is related to molecular properties affecting pharmacodynamics and pharmacokinetics of the molecules27 from the compounds’ Simplified Molecular Input Line Entry System (SMILES) string as input using BeautifulSoup (a Python library for pulling data out of HTML and XML file). We removed potentially toxic compounds that were AMES mutagens, carcinogens and human Ether-a-go-go-Related gene (hERG) inhibitors. The inhibition of the human ether-a-go-go (hERG) ion channel may cause cardiotoxicity and hence, it is necessary to screen all potential drug candidates for risk against blocking the hERG channel60. The Ames test is a sensitive tool in screening for potential genotoxic carcinogens61.ADMET properties were predicted from admetSAR web server16. A pharmacophore models used in this study were derived from protein–ligand 3D complex structures as well as structural data on small bioactive organic molecules62. Pharmacophores can be used as low-dimensional targets in target prediction tasks63. We performed pharmacophore-based prediction of the potential targets of aloes’ phytochemicals using BIOVIA Discovery Studio56. Discovery Studio is an interactive computational chemistry and molecular modeling package with a broad variety of features for modeling and simulation56. We used “Ligand Profiler” protocol which is an automated ligand profiling in BIOVIA Discovery Studio. The BIOVIA Discovery Studio provides automated pharmacophore-based activity profiling and reporting16. Discovery Studio 4.5 is equipped with PharmaDB pharmacophore database which contains about 68,000 pharmacophores derived from 8000 protein–ligand complexes from the sc-PDB dataset, which itself is a collection of 3D structures of binding site found in PDB64. The binding sites were extracted from crystal structures in which a complex between a protein cavity and a small molecule ligand could be identified15.A pharmacophore model represents a target-ligand binding site that triggers a desired pharmacological effect65. Further information about the protein including the protein name, gene name, organism, sequence information, taxonomy, family and domains, and related molecular functions and biological processes can be obtained using the UniProt ID from UniProt website (http://www.uniprot.org/). This database can also link to other databases by searching based on the PDB ID, KEGG ID and other information. In this work, for each target, the target name, UniProt entry name and UniProt ID information were collected from the PDB64 and UniProt databases65.As for parameters setting during ligand profiling with Input PharmaDB Pharmacophores, all the pharmacophore models with the shape of the binding pocket were selected for the virtual screening with default settings. The RIGID mode was used as the molecular mapping algorithm. A conformer database (3D SDF file format) was generated using BEST settings of the Conformation Generator component for each query ligand. All molecular features were allowed while mapping these ligands to the pharmacophore models to increase selectivity. The minimal interfeature distance was set at 0.5 Å. For every query ligand, targets were ranked by decreasing fit-value. The targets were nominated according to their UniProt ID. UniProt is an abbreviation for the Universal Protein Database program, which consists of the Swiss-Prot, TrEMBL and PIR-PSD databases and is the largest database containing the most informative data resources and protein structures42 were performed to obtain diseases related to each predicted targets. Network propagation was conducted with Random-Walk with Restart (RWR) algorithm in the heterogeneous biological networks of CODA. CODA is an integrated database which contains diverse types of biological interactions from molecular level to phenotypic level. We constructed a heterogeneous network using all types of biological interactions in CODA to see converging information flow into disease nodes in the network starting from the targets. After running the algorithm, we ranked disease nodes in the network by their RWR scores. Then the disease was mapped to high-level disease category using KEGG Application Programming Interface (API)—a REST-style API to the KEGG database resource.To investigate the therapeutic effect of Ethiopian indigenous aloes, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene ontology (GO) of the 82 predicted targets were analyzed. In order to identify the complex relationship between compounds, targets, pathways and biological processes, we conducted enrichment test by using enrichr function in gseapy python library with internal gene set named ‘KEGG_2021_Human’. Network analysis in the Context-Oriented Directed Associations (CODA)42, which contains a total of 17,358 protein nodes. Among molecular relations in the CODA, proteins with both left and right entity type of gene product were selected. Then all the nodes were represented with gene symbol for better interpretation. To get hub targets in the PPI network, two types of network centralities  were used. There are also another two more representative network centrality measures such as closeness centrality and betweenness centrality. However, closeness and betweenness centrality were not considered in this study because they focus on the overall robustness in the network than relationship between neighboring nodes66. Because of this, we consider hub targets as the nodes within the top 10% degree and eigenvector centrality values in the PPI network.A protein–protein interaction (PPI) network was constructed from CODA67. In this study, we employed blind docking since we do not have information regarding the binding mode of the phytochemicals with the predicted targets. The PDB files of five randomly chosen proteins from hub proteins in PPI network  were retrieved from Protein Data Bank (PDB), and the ligands’ 3D SDF file was obtained from PubChem database. The molecular docking was done using the CB-Dock2 web server (http://clab.labshare.cn/cb-dock/php/index.php), which is a server for protein–ligand blind docking, integrating cavity detection, docking and homologous template matching. CB-Dock automatically identifies the binding sites, calculates the center and size, customizes the docking box size according to the query ligand and then perform the molecular docking with AutoDock Vina68. Then, the 3D and 2D poses of the protein—ligand complexes were analyzed and visualized using Discovery Studio Visualizer.Additionally, molecular docking studies were performed to confirm the protein–ligand interaction that the pharmacophore models predicted. Blind docking was used for the detection of possible binding sites and modes of peptide ligands by scanning the entire surface of protein targetsP ≤ 0.05).Furthermore, we compared the docking score of our selected target and protein with the docking scores of random compounds from PubChem docked with random targets from PDB using one-sample, one-sided t-test to statistically show that the bindings were significant 71 for ligand topology preparation. The solvation method used was a triclinic box of default Simple Point Charge (SPC) water model where explicit solvent periodic boundary conditions were applied. Charge neutralization using sodium and chloride ions was carried out for the solvated complexes. The systems were subjected to energy minimization to resolve any steric clashes or inappropriate geometry employing the steepest descent method through 5000 steps. System equilibration was also manipulated to ensure a reasonable starting structure using NVT; equilibration under constant number of particles, volume, and temperature (NVT) for 100 picoseconds (ps) using a Berendsen thermostat72. Also, re-equilibration was performed for another 100 ps under constant pressure  ensemble) using the Parrinello-Rahman barostat using a time step of 2 femtoseconds (fs) for each equilibration round73. Finally, MD production phase was carried out for 100 ns using a time step of 2 fs at a constant temperature of 300 K and constant pressure of 1 atm. For MD trajectories analysis, the complex was re-centered and re-wrapped within the unit cells using the “trjconv” function of GROMACS. Then, the trajectories were analyzed using RMSD of the protein, ligand and protein–ligand complex referenced to its initial position and the radius of gyration (Rg). GRaphing and Advanced Computation and Exploration of data (Grace) tool was used for data plotting (https://plasma-gate.weizmann.ac.il/Grace/).Among the selected phytochemicals for molecular docking, the ligand homonataloin displayed the best binding interaction and free energy with the Cyclin Dependent Kinase 2 (CDK2) target protein. Accordingly, it was chosen to be subjected to 100 nanoseconds (ns) molecular dynamics (MD) simulation against the target protein to confirm its stable binding affinity. MD simulations were performed using GROningen MAchine for Chemical Simulations (GROMACS) 2021.4 software packageThe BIOVIA Discovery Studio software program were installed on Windows 10. Profiling of the phytochemicals were carried out in an automatic fashion on a Windows 10 computer , and 4 Logical Processor(s)). The configuration of the RAM was: 4 slots, 3 of them each having 8 GB RAM and 1 slot 4 GB RAM giving a combined 28 GB, DDR3 and speed of 1600 MHz. Network analysis , KEGG pathway enrichment and GO analysis were performed on a server CentOS computer, Intel Xeon CPU E5-2680 v3 @ 2.50 GHz, 48 cores. The configuration of the RAM was: 16 slots with 32 GB RAM, DDR4. MD simulation was done using: Linux system (Ubuntu 2021.4–2) computer  / llvmpipe ).In silico profiling provides a rapid approach for identification of active compounds, prediction of bioactivities of compounds, and to guide the rationalization of the use of traditional medicines. Computational target fishing is a new approach in pharmaceutical research that helps to examine the relationship between drugs and diseases. Using the admetSAR and SwissADME online servers, the ADMET and drug-likeness characteristics of phytochemicals from indigenous aloes in Ethiopia were assessed. Then, using in silico pharmacophore models, potential therapeutic targets were predicted and a wide range of associated therapeutic potential was revealed.A. debrana Christian and A. megalacantha Baker were linked cancer and endocrine disease; A. gilbertii Sebsebe and Brandham, A. monticola Reynolds, and A. schelpei Reynolds were linked with congenital disorders of metabolism; A. adigratana Reynolds and A. elegans Tod were related to digestive system disease; and so forth. This information indicated that these aloes were predicted to be effective in treating the aforementioned diseases. In order to confirm these effects, the phytochemicals’ pharmacological effects should be applied to further controlled experimental research.The findings suggested that the phytochemicals in indigenous Ethiopian aloes are potential therapeutic molecules with 82 associated targets that were enriched in a number of KEGG pathways and GO terms. The results demonstrated that the phytochemicals have a broad range of therapeutic targets and the potential to regulate important biological pathways, such as the metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, IL-17 signaling pathway, Th17 cell differentiation, and pathways in cancer, among others. Furthermore, this study revealed that the phytochemicals may be used as a potential therapeutic agent for a number of cancers, and diseases of the endocrine, metabolic, immune, nervous, digestive, musculoskeletal, and hematologic systems, as well as congenital disorder of metabolism, infectious diseases, and mouth and dental diseases. Particularly, the aloe species Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Video 1.


Purpose: Fermented camel milk from Xinjiang is rich in probiotics and has immunomodulatory effects as an important source of bioactive peptides. However, it is not clear whether it is the probiotic or the bioactive peptide that acts. The present study aimed to extract and identify bioactive peptides from fermented camel milk in Xinjiang and investigate their immunomodulatory effects and mechanisms based on network pharmacology and molecular docking.Methods: Four probiotic bacteria were used to ferment the fresh camel milk and the bioactive peptides were extracted and isolated by ultrafiltration and column chromatography. Network pharmacology predicts targets and pathways of action. GeneCards and OMIM-GENE-MAP database were used in order to search disease target genes and screen common target genes. Then we used STRING web to construct a protein-protein interaction (PPI) interaction network of the common target protein. The key targets were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis through the David database. The "drug (bioactive peptide)-disease-targets-pathway" network was established and molecular docking was used for prediction.Results: Two fractions were obtained by UV spectrophotometer; whey acidic protein, α-lactalbumin, and peptidoglycan recognition protein 1 were the main protein-like components of Xinjiang fermented camel milk-derived bioactive peptides. The repeat sequence of peptidoglycan recognition protein 1 was selected and then seven bioactive peptides were obtained. Bioactive peptides had 222 gene targets, anti-inflammatory diseases had 2598 gene targets, and immune regulation had 866 gene targets, the intersection of which was 66 in common gene targets. Gene ontology and KEGG analysis reveals that bioactive peptides mainly play a vital role in the signaling pathways of lipid and atherosclerosis, pathways in cancer. The molecular docking results showed that the seven bioactive peptides bound well to the top four scoring proteins.Conclusion: The immunomodulatory and anti-inflammatory effects and mechanisms of Xinjiang fermented camel milk-derived bioactive peptides were initially investigated by network pharmacology and molecular docking, providing a scientific basis for future studies. The immune system is a critical defense system for the organism . ImmunomSmall molecule drugs and targeted biologic are the mainstays of treatment . The incdb/db mice   . BioactiThe fresh camel milk was centrifuged at 3,000 r/min for 30 min and repeated three times, the upper-fat layer was discarded and pasteurized. The fermented camel milk was centrifuged at 3,000 r/min for 15 min repeated three times, and collected whey. The whey was ultrafiltered using a nanofiltration membrane with a molecular retention value of 30 ku, and the filtrate was concentrated and freeze-dried for 24 h to obtain the crude lyophilized powder. The crude lyophilized powder was separated by Sephadex-50 gel column chromatography, and one tube was collected every 3 ml. The absorption value was detected at 214 nm  using a https://prediction.charite.de/) database. Then the GeneCards (https://www.genecards.org/) and OMIM-GENE-MAP (https://omim.org/search/advanced/geneMap) databases were then screened using “immunomodulatory”" and “anti-inflammatory” as keywords to obtain disease-related targets (set Score > 10 in GeneCards for both keywords). Gene targets at the intersection of bioactive peptide immunomodulation and anti-inflammatory diseases were obtained through the online platform of Venn diagram mapping (http://bioinformatics.psb.ugent.be/webtools/Venn/).The 2D structures of the identified bioactive peptides were drawn using MarvinSketch 22.11 software  and the https://www.string-db.org), set to “"homo sapiens” species, and the network relationship data of the target protein interactions were obtained, and the PPI was mapped using Cytoscape 3.9.0 software. Network diagrams were created using Cytoscape 3.9.0 and key targets were screened using the Network Analyzer tool (The intersecting gene targets were entered into the String web (zer tool .https://david.ncifcrf.gov/), set at p < .05, to obtain the functions and pathways of key targets.GO and KEGG enrichment analyses of key targets were performed through the David database -disease-target-pathway” network.http:/www.rcsb.org/pdb). Then the operations such as removal of water molecules, hydrogen addition, and setting the small molecule ligand flexible bonds to rotatable were performed by AutoDockTool 1.5.7  database  database , cellular component (CC) and molecular function (MF). A total of 144 biological processes or pathways were obtained by GO enrichment analysis, set ar chart .p < .05) and the main pathways are Lipid and atherosclerosis, Pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, etc. The top 20 pathways were filtered from smallest to largest p-value and plotted in a bar chart  were imported into Cytoscape 3.9.0 and constructed the “drug (bioactive peptide)-disease-target-pathway” network diagram .The top four proteins STAT3, HSP90AA1, CASP3, and PIK3CA were selected as the receptors and bioactive peptides for molecular docking using Autodock vina 1.1.2 and visualized using Pymol 2.4.0 . GeneralThe molecular docking results show that almost all of the seven bioactive peptides interact with STAT3, partly with CASP3, and the bioactive peptide 2/STAT3 and bioactive peptide 5/CASP3 have the best binding energy. Thus bioactive peptide 2/STAT3 and bioactive peptide 5/CASP3 are selected to perform molecular dynamics simulation.The Root Mean Square Deviation (RMSD) can reflect the composite motion process, with large or small fluctuations representing strong or stable motion . The resBioactive peptides are considered to be a preferred alternative to drugs, being present mainly in natural foods and with low toxic effects . MoreoveIn this study, the KEGG study shows lipid and atherosclerosis, as well as pathways in cancer, are the two most enriched pathways. Atherosclerosis is a chronic inflammatory disease that is prevented and treated through the interplay of lipid metabolism and inflammatory responses . The devThis study explored the immunomodulatory and anti-inflammatory mechanisms of bioactive peptides extracted from fermented camel milk in Xinjiang based on network pharmacology and molecular docking. By speculating on the targets and pathways of action of bioactive peptides extracted from fermented camel milk in Xinjiang, this paper provides theoretical support for future experiments to be conducted. It also provides some references for the future development of fermented camel milk in Xinjiang.


To investigate the differential expression of exosomal miRNAs in the bone marrow tissue of Modified Qing’ E Formula (MQEF) on steroid-induced ischemic necrosis of the femoral head (INFH) model.Steroid hormones were used to establish the INFH model and treated with MQEF. After successful modeling, femoral tissue exosomes were isolated for miRNA sequencing to obtain femoral tissue exosomal differential miRNAs. By GO analysis and KEGG analysis of the differential genes in both groups, the major exosomal miRNAs of MQEF exerting anti-INFH as well as the major signaling pathways were identified. Next, a quantitative metabolomic validation of MQEF with broad targeting was performed to obtain the main active components of MQEF and to perform biological analysis and signaling pathway prediction of the active components by network pharmacology. Finally, the sequencing results were validated by using RT-qPCR. The results of miRNA sequencing were verified by double examination of network pharmacology and RT-qPCR, and the exosomal miRNAs regulated by the anti-INFH effect of MQEF and the specific signaling pathway of the effect were clarified.A total of 65,389 target genes were predicted in the exosomes of two groups of mice, and 18 significant differentially expressed miRNAs were obtained, of which 14 were up-regulated and 4 down-regulated. GO enrichment analysis showed that these predicted target genes were enriched in 12371 biological processes, 1727 cell components, and 4112 molecular functions. KEGG analysis showed that the predicted miRNA target genes were annotated to 342 signal pathways, in which the highly enriched pathways closely related to bone metabolism were PI3K-Akt signal pathway, MAPK signal pathway, and Wnt signal pathway. The most significantly up-regulated miRNAs were miR-185-3p and miR-1b-5p and the most significantly down-regulated miRNAs were miR-129b-5p and miR-223-5p, of which the targeted genes were closely related to the PI3K-Akt signal pathway. MQEF aqueous decoction extract targeted metabolomics quantitatively combined with network pharmacology predicted targets also closely related to PI3K-Akt signaling pathway. Real-time quantitative PCR validation showed that miR-185-3p was up-regulated 7.2-fold and miR-129b-5p was down-regulated 2.2-fold in the treatment group, and the difference was significant (P < 0.05).MQEF can regulate exosomal miRNA expression in steroid-induced INFH models, miR-185-3p or miR-129b-5p/PI3K-Akt signal axis may be part of the mechanism of MQEF against steroid-induced INFH. Ischemic necrosis of the femoral head (INFH) is a devastating disease with tremendous harm, causing a huge physical, psychological, and financial burden to the patients. Beside trauma, the inappropriate use of corticosteroids is considered to be the most common cause of non-invasive INFH , 2. For MicroRNAs (miRNAs) are a class of small non-coding RNAs about 22 nucleotides (nt) in length. They play important regulatory roles by targeting messenger RNA (mRNAs) through degradation or inhibiting the protein translation. MiRNAs regulate about 60% of the mammalian genes and are involved in a series of biological processes such as cell proliferation, differentiation, apoptosis, neurogenesis, and tumor development . TherefoExosomes are small vesicles of 40–200 nm in size that are secreted by cells. They can perform the function of intercellular communication and transfer of substances. They also target and regulate cell differentiation and proliferation through delivering proteins and RNA. Many studies have shown that exosomes can play a protective role in various tissues and organs , 8. SimiModified Qing’ E Formula (MQEF) has been used for more than 1300 years in China as a treatment for lumbodynia. It is now being incorporated in the guidelines for the treatment of postmenopausal osteoporosis and non-traumatic INFH. Current studies have shown that MQEF can improve the microstructure of bone trabeculae and bone biomechanics , improveEucommia ulmoides (960 g), Fructus psoralease (480 g), Semen juglandis (300 g), and Allium sativum (240 g). All herbs were washed and placed in a multifunctional extractor. The herbs were immersed in double distilled water (five-folds of their volume) for 2 h followed by boiling for 2 h. After filtering of the boiled decoction, the herbal residues were repeatedly boiled twice for 1 h each time and the resulting boiled decoction was filtered, combined with the first filtrate, and condensed to a thick paste (100%), which was then added in 95% ethanol (three-folds of its volume) under stirring. After standing for 24 h, the solution was filtered to recover the ethanol fraction, which was then concentrated to a decoction of 5 g/mL. The mice received a daily intragastric MQEF dose of 1.7 g/kg body weight (b.w.) diluted with distilled water. The MQEF dose calculation was done in accordance with the guidelines correlating the dose equivalents between humans and laboratory animals, based on the ratio of the body surface area.According to the original formula in the Dictionary of Chinese Medical Formulas, the MQEF consists of Sixteen male SPF C57BL/6 mice aged eight weeks  were used to establish the steroid-induced INFH model according to the literature . The modg for 10 min at 4 °C. The supernatant was transferred into a centrifuge tube (10 ml) for ultracentrifugation and centrifuged at a speed of 12,000 g for 30 min at 4°C. Then, the supernatant was transferred into another centrifuge tube (10 ml) and ultracentrifuged  at a speed of 120,000 g for 70 min at 4°C. The transparent precipitate was retrieved and resuspended with 50 µl of PBS. The resulting exosome solution was stored at -80°C.Exosomes from the bone marrow fluid were extracted by gradient ultracentrifugation , 16. TheThe quality control of the extracted exosomes is essential. The International Society for Extracellular Vesicles suggests three levels of exosome identification: Scanning Electron Microscopy or Transmission Electron Microscopy to identify morphological features, Nanoparticle Tracking Analysis (NTA) to identify particle size and concentration, and protein markers.therefore, we have performed NTA  equipped with a green laser (520 nm). Nanoparticles illuminated by the laser and their movement under Brownian motion was captured. Videos were analyzed using the NTA software to provide particle concentrations and size distribution profiles. Triplicate measurements were recorded for each sample. Size distribution and concentration profiles were averaged across replicates to derive the representative size distribution profiles.Isolated exosomes were lysed by RIPA Lysis Buffer  with the addition of Phenylmethanesulfonyl fluoride . Protein loading buffer was added and protein denaturation was carried out at 95 °C for 15 minutes. The protein samples were separated by 10% or 12% twelve alkyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto a PVDF membrane . The PVDF membrane was blocked with 5% milk in TBST powder for 1 h and incubated with the primary antibody  at 4°C overnight. The membranes were washed with TBST three times, then incubated with HRP conjugated secondary antibody at room temperature for 1.5 h. After being washed with TBST for three times, membranes were incubated with ECL solution , and the images were obtained with iBright CL750 imaging system .A total of 12 samples were measured in this study using the DNBSEQ platform and each sample generated an average of 39.76 M of data with an average matching rate of 91.4% of the reference genome among the samples. A total of 65,389 genes were detected, including 64,406 known genes and 983 unknown genes  content. These reads needed to be removed before data analysis to ensure the reliability of the results. The quality indexes of filtered reads are shown in the quality statistics of reads after filtering  were counted, and the length distribution of sequences of sRNAs was calculated  equipped with QTRAP 6500 Plus . Samples injected in the ACQUITY UPLC HSS T3 column  were eluted for MRM monitoring with the following LC gradient: 95% mobile phase A (Waters with 0.1%FA) and 5% mobile phase B (Acetone with 0.1%FA) at 0 min to 5% mobile phase A and 95% mobile phase B in 22 min with the flow rate at 0.3 mL/min and the column temperature was set at 40°C. For the QTRAP 6500 Plus equipped with an EST Turbo Ion-Spray interface, the source parameters were set as follows: source temperature: 500°C; ion spray voltage (IS): 4500 V (positive mode) or -4500 V (negative mode); Ion source gas I (GS1), gas II (GS2), and curtain gas (CUR) were set at 40, 40, and 20 psi, respectively. MRM methods were set at schedule mode with MRM transitions, collision energy (CE), declustering potential energy (DP), and retention time for target metabolites  and two small steel balls. Then, samples were crushed using a tissuelyser for 5 min at 50 Hz. Samples were then stored at 4°C, vortexed 3 times every 10 minutes, and then incubated overnight at 4°C. Vortexing and centrifugation at 13,000 The top 50 ingredients  were analyzed in order of content. Firstly, the Canonical SMILES numbers of 50 compound ingredients were obtained through the PubChem database, and the Canonical SMILES numbers were entered into the Swiss ADME web tool. The potential active chemical ingredients (top 50 in content) of the compounds were evaluated according to Lipinski’s “Rule of Five.” Thirty-four active chemical ingredients were obtained and the molecules which were not suitable to be drugs were excluded by screening. The Canonical SMILES numbers of the 34 active chemical components were entered into the Swiss Target Prediction database and their potential action targets against Homo sapiens were predicted according to their chemical structures. A total of 597 predicted targeted genes were obtained after de-duplication. Next, KEGG enrichment analysis was carried out on the 597 predicted targeted genes.The expression profiling by array of steroid-induced INFH (GSE123568) was downloaded from Gene Expression Omnibus (GEO) database. We used the R package of GEO query (version 2.54.1) for the data download, limma (version 3.42.2) for differential analysis, umap (version 0.2.7.0) for UMAP analysis, ggplot2 (version 3.3.3) for visualization, and ComplexHeatmap (version 2.2.0) for heatmap visualization. Based on data from 30 patients with INFH and 10 patients with non-INFH, gene expression profiles were detected by microarray analysis. Then, the list of candidate gene biomarkers for INFH was identified by integrating differential expression data analysis and gene signaling transduction network analysis.PPI networks were constructed using the String database (v 11.5) for each of the 62 targets involved in the PI3K-Akt signaling pathway and 354 pathway targets. The degree values were calculated using Network Analysis (a Cytoscape plugin). The nodes with the top 20 degree values in the 62 targets involved in the PI3K-Akt signaling pathway were called core targets, which were then matched with the rank in the network of 354 pathways. Then, the targets of 34 active chemical ingredients of MQEF were intersected with the 62 targets involved in PI3K-Akt signaling pathway to explore which ingredients were involved in regulating PI3K-Akt signaling pathway in MQEF. Finally, a total of 23 active ingredients targeted at the 62 targets were involved in the pathway.First, the CAS number and Canonical SMILES number of the top 50 ingredients in MQEF were obtained from the PubChem database and the molecular weight (MV) was also acquired. Then, the TCMSP database was used to retrieve the top 50 MQEF ingredients and their intestinal absorption rate, Caco2, and Drug Likeness. Some ingredients were not included in the TCMSP database. Therefore, the input intestinal absorption rate and bioavailability score were evaluated by Swiss ADME database. Caco2 permeability or intestinal absorption rate were used to assess the possibility of ingredients becoming drugs. Finally, we used Pro-ToxII database to estimate the median lethal dose (LD50) of 50 ingredients.−ΔΔCt method and β- actin was used as a reference to normalize the expression and calculate the relative expression of each group. The miRNA sequences are as follows: mmu-miR-185-3p: (5’→3’): F: GAGGCTGGAGCTCTCAGGCCACCTGCCCAGGGCGACTCCC; R: GGGAGTCGCCCTGGGCAGGTGGCCTGAGAGCTCCAGCCTC; mmu-miR-129b-5p: (5’→3’): F: CTTTTTGCGGTCTGGGCTTGC; R: AGTGCAGGGTCCGAGGTATT.The exosomal miRNA was extracted with RNA Isolate  and Trichloromethane . MiRNAs were obtained through the MiPure miRNA Column according to the manufacturer’s protocol . The miRNA 1st Strand cDNA Synthesis Kit (by stem-loop)  was used for genomic DNA removal and cDNA acquisition. Then, the cDNA was amplified using the fluorescence quantitative PCR instrument . Amplification reactions were performed according to the manufacturer’s protocol  under the reaction conditions used for amplification. The calculation is based on 2All data were presented as means ± SEM of three independent experiments and analyzed using one-way ANOVA combined with Tukey’s multiple comparison tests. P < 0.05 was considered statistically significant.There were 18 significant differentially expressed miRNAs in the exosomes of femoral head bone marrow tissue in the treatment group and the control group, of which 14 were up-regulated and 4 were down-regulated. We defined the significant difference threshold as log2FC>=1 and Q-value<=0.05. Based on the statistical analysis, the statistically significant results obtained are presented in the figure as follows  to predict their potential targets of action against Homo sapiens based on their chemical structures. After summarization and de-duplication in an Excel spreadsheet, a total of 597 active ingredient targets were obtained  Figure 9in vitro and have good stability.INFH caused by the inappropriate use of corticosteroids currently presents many therapeutic challenges. Therefore, exploring the mechanisms that can regulate bone metabolism and bone homeostasis can provide the theoretical basis and frontier discoveries for steroid-induced INFH . The twoExosomes deliver various molecules, among which miRNAs are the first to enter exosomes and are preferentially transported and delivered by exosomes. MiRNAs regulate various physiological and pathological processes by targeting mRNAs for degradation or inhibiting protein translation. By targeting mRNAs and proteins that regulate bone metabolism, miRNAs exert regulatory effects on the complex signaling pathways involved in bone metabolism through these molecules, thereby further regulating cartilage and osteogenic differentiation. Many “star” molecules and pathways involved in bone metabolism, bone homeostasis, and bone reconstruction, such as BMP2, RUNX2, and Wnt signaling pathways, are regulated by miRNAs . For exaIn China, MQEF is used for the treatment of postmenopausal osteoporosis and non-traumatic femoral head necrosis and has received positive responses . The useIn this study, we first conducted exosome isolation and miRNA sequencing in an animal model of steroid-induced INFH with MQEF intervention. Through transcriptomic studies, we clarified that one of the mechanisms by which MQEF exerts its therapeutic effects on steroid-induced INFH is by targeting exosomal miRNAs and through up-regulation and down-regulation of some miRNAs, which regulate multiple signaling pathways involved in the upstream and downstream of bone metabolism, such as The predicted target genes of MQEF-regulated exosomal miRNAs were annotated to a total of 342 signaling pathways, and the highly enriched pathways were PI3K-Akt signaling pathway, MAPK signaling pathway and Wnt signaling pathway in order. There were 328 predicted target genes annotated to PI3K-Akt signaling pathway, 276 predicted target genes annotated to MAPK signaling pathway, and 152 predicted target genes annotated to Wnt signaling pathway. Some of the predicted target genes regulated by the differentially expressed miR-185-3p, miR-486-3p, and miR-542-3p were annotated to PI3K-Akt signaling pathway, MAPK signaling pathway, and Wnt signaling pathway, therefore it is speculated that miR-185-3p, miR-486-3p and miR-542-3p in the local exosome of bone tissue miR-542-3p may play an important role in the pathological process of MQEF intervention in INFH. We also analyzed the active ingredients of MQEF aqueous decoction extract for the first time by metabolomics and clarified the distribution and proportion of small and medium molecule compounds in MQEF. Further, we verified the sequencing results by network pharmacology, and found that the PI3K-Akt signaling pathway was highly correlated with INFH, and the MQEF active ingredients significantly affected the PI3K-Akt signaling pathway and acted on the key targets of the PI3K-Akt signaling pathway.Next, we selected two key differentially expressed miRNAs and investigated how miRNAs regulate hormonal INFH through key signaling pathways of bone metabolism, especially PI3K-Akt signaling pathway, Wnt signaling pathway, and MAPK signaling pathway. We also searched for specific effective components in MQEF that play a role in the treatment of steroid-induced INFH at the molecular level to provide strong theoretical evidence for exploring the modern pharmacological mechanism and standardizing the use of MQEF. In addition, MQEF is composed of many small molecules, and although its clinical efficacy in the treatment of steroid-induced INFH is confirmed, yet it is unknown which specific small molecules are responsible for the effect and the specific mechanism, which deserves further investigation. Furthermore, many small molecule compounds in MQEF have disadvantages such as poor oral bioavailability, thus limiting their availability. However, to date, few studies have been conducted and no feasible solutions have been proposed. In order to use MQEF in a more standardized and scientific way in the future, great efforts are needed to optimize the ratio, dosage form, and delivery method of MQEF.In conclusion, this study clarified that one of the mechanisms by which MQEF exerts its therapeutic effect on steroid-induced INFH is through targeting exosomal miRNAs to regulate multiple signaling pathways such as PI3K-Akt signaling pathway, MAPK signaling pathway, and Wnt signaling pathway which is upstream and downstream of bone metabolism and bone reconstruction by up-regulating and down-regulating some miRNAs. In addition, this study analyzed for the first time the active ingredients of MQEF aqueous decoction extract by metabolomics to clarify the distribution of small and medium molecule compounds in MQEF in addition to clarifying their distribution pattern and proportion of small and medium molecules in MQEF. Together, bioinformatics analysis of the active ingredients in MQEF was conducted to clarify the possible targets and specific pathways of MQEF, which has a positive impact on the pharmacological research and molecular mechanism of MQEF. Further clinical and basic experimental studies are needed in the future.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The animal study was reviewed and approved by the Institutional Animal Care and Use Committee at Tongji Medical College, Huazhong University of Science and Technology. [2020] IACUC Number: 2695.BS and WZ conceived the idea for the study and provided critical revision of the manuscript. BS, WZ, JL, FZ, and CM performed the experiments. JL, FZ, and CM participated in study design, data acquisition and data analysis. BS, WZ, and JL participated in writing and drafting of the manuscript. All authors read and approved the final manuscript.This study was partially funded by the National Natural Science Foundation of China .www.editage.cn) for English language editing.The authors would like to thank GLW (Beijing Genomics Institute) for his expertise and guidelines for performing the transcriptome sequencing. The authors would like to thank Editage (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Alzheimer's disease (AD) is becoming a more prevalent public health issue in today's culture. The experimental study of Coptidis Rhizoma (CR) and its chemical components in AD treatment has been widely reported, but the principle of multi-level and multi-mechanism treatment of AD urgently needs to be clarified.This study focuses on network pharmacology to clarify the mechanism of CR's multi-target impact on Alzheimer's disease.The Phytochemical-compounds of CR have been accessed from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Symmap database or HPLC determination. The values of Oral Bioavailability (OB) ≥ 30% and Drug Like (DL) ≥ 0.18 or blood ingredient were used to screen the active components of CR; the interactive network of targets and compounds were constructed by STRING and Cytoscape platform, and the network was analyzed by Molecular Complex Detection (MCODE); Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) and metabolic pathway enrichment of targets were carried out with Metascape, the Database for Annotation, Visualization and Integrated Discovery (DAVID) and MetaboAnalyst platform; Based on CytoHubba, the potential efficient targets were screened by Maximal Clique Centrality (MCC) and Degree, the correlation between potential efficient targets and amyloid β-protein (Aβ), Tau pathology was analyzed by Alzdata database, and the genes related to aging were analyzed by Aging Altas database, and finally, the core targets were obtained; the binding ability between ingredients and core targets evaluated by molecular docking, and the clinical significance of core targets was assessed with Gene Expression Omnibus (GEO) database.19 active components correspond to 267 therapeutic targets for AD, of which 69 is potentially effective; in module analysis, RELA, TRAF2, STAT3, and so on are the critical targets of each module; among the six core targets, RELA, MAPK8, STAT3, and TGFB1 have clinical therapeutic significance; GO function, including 3050 biological processes (BP), 257 molecular functions (MF), 184 cellular components (CC), whose functions are mainly related to antioxidation, regulation of apoptosis and cell composition; the HIF-1 signaling pathway, glutathione metabolism is the most significant result of 134 KEGG signal pathways and four metabolic pathways, respectively; most of the active components have an excellent affinity in docking with critical targets.The pharmacological target prediction of CR based on molecular network pharmacology paves the way for a multi-level networking strategy. The study of CR in AD treatment shows a bright prospect for curing neurodegenerative diseases. Alzheimer's disease (AD) is usually characterized by cognitive impairment, whose two typical pathological features are extracellular amyloid plaque and intracellular neurofibril entanglement. The number of AD patients is increasing with the advent of the aging era, and it has become one of the significant public health problems in the world  in treating diseases is so long as ancient Chinese history, and it has improved the quality of life of the people . It has a significant therapeutic effect on bacillary dysentery, typhoid, tuberculosis, and other diseases , named Huang Lian, is the dried rhizome of In this study, the blood components of CR were selected as the research object, combined with the data of blood metabolomics and brain transcriptomes. From the view of the component-gene-metabolism level, the mechanism was expounded by utilizing network pharmacology to analyze CR in AD treatment, as shown in https://tcmsp-e.com/) is a unique platform for the systematic pharmacology of TCMs, capturing the relationship between drugs, targets, and diseases  provides artificially selected information about the chemical, disease, and genetic relationships   network construction for common targets with STRING version 11.5 (string-db.org)  to draw Enrichment Plot.The standard targets were inputted into the DAVID online platform, and the “Homo sapiens” species were selected for KEGG pathway enrichment. Select the path in the first 15 of the https://www.metaboanalyst.ca/) combines non-targeted metabonomics with transcriptome for functional analysis at the metabolite-gene level , Degree, and so on. Take the targets that meet the requirements of MCC and Degree as potentially essential targets.http://www.alzdata.org/) database makes an entire collection of current high-throughput omics data for AD , and then the docking site was obtained by Pymol 2.4.0. After being treated with AutoDockTools 1.5.6  Browser  3,050, cellular component (CC) 184, and molecular function (MF) 257. Only the first 20 BP were shown in p-value .p-value were shown underneath corresponding network plots in PPI shows the interaction between targets and illustrates an interrelated information transmission network, where a PPI network diagram of node 256 and edge 6713 is shown in In KEGG enrichment, 134 results were obtained. Many disease pathways are involved in this enrichment, such as Non-alcoholic fatty liver disease (NAFLD), Hepatitis B, Chagas disease (American trypanosomiasis), Tuberculosis, Pertussis, Influenza A, and so on, suggesting that there are similar mechanisms in different conditions. In the following To explore the joint-pathway of whole blood markers in patients with dementia, 33 differential metabolites and 267 targets of blood markers in patients with dementia were analyzed by MetaboAnalyst . The resFrom the “component-target-pathway” network diagram , we can Taking the intersection of MCC and Degree screening targets as the following research object, 49 targets were obtained. By AlzData analysis, 10 targets were significantly positively correlated with Aβ, two were significantly negatively correlated with Aβ, Eleven targets were significantly positively correlated with Tau, and two were significantly negatively correlated with Aβ . AD is aThe core target information collected by Uniprot is shown in The GSE122063 dataset included the brain transcriptional data of vascular dementia (VaD), AD, and Controls, and one sample was repeated twice  regulates protein expression in biological processes, such as neurogenesis, glucose metabolism, erythropoiesis, and angiogenesis  is the most abundant non-protein mercaptan in cells and an important antioxidant. Compared with healthy older adults, the levels of GSH in the hippocampus of patients with mild cognitive impairment (MCI) and AD are significantly reduced  can be found in the article/The authors declare that the procedures followed were by the relevant clinical research Ethics Committee's regulations and those of the Code of Ethics of the World Medical Association (Declaration of Helsinki).X-wY integrated the data and wrote the manuscript. S-qC and L-jX executed the literature search. X-fX and H-lW implemented corrections in the manuscript. X-rL conceptualized and designed the experimental plan. All authors contributed to the article and approved the submitted version.This study was supported by the National Key Research and Development Program of China (No. 2019YFC1711500) and the National Natural Science Foundation of China (No. 81973480).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately. Under certain conditions, the abnormal life activity processes caused by the disturbance of homeostasis after the damage of the disease cause the disease4. Traditional Chinese medicine (TCM) has been utilized to treat diseases for thousands of years7. Traditional Chinese medicine is a kind of material with the function of rehabilitation and health care, which could be utilized to prevent, treat and diagnose diseases under the guidance of TCM theory11.Internal and external etiology can lead to self-stable regulation disorder, which could change a series of metabolisms, functions and structures. Abnormal life activity processes are manifested as abnormal symptoms, signs and behavior13. The most important feature of traditional Chinese medicine in treating diseases is to pay attention to the adjustment of the functions of viscera and organs, and the balance and coordination between them. The focus of traditional Chinese medicine treatment is not that the human body is infected with the specific bacteria, virus and other pathogenic factors, but the specific reaction of the human body after these pathogenic factors act on the human body15. The purpose of treatment is to enhance the disease resistance and recovery ability of human body. To kill bacteria and relieve symptoms are mainly achieved by enhancing the body's own functions. In recent years, traditional Chinese medicine has certain advantages in the treatment of pneumonia16, shock17, convulsion18, hemorrhage19, acute respiratory failure20, renal failure21, heart failure22, cerebrovascular accident23, etc. it is not only effective, but also safe and simple, with few adverse reactions.Traditional Chinese medicine mainly comes from natural medicine and its processed products, including plant medicine, animal medicine, mineral medicine and some chemical and biological products28. Lu et al. utilized network pharmacology and molecular docking technology to study the mechanism of Shaoyao Decoction in the treatment of ulcerative colitis, and found that Shaoyao decoction can improve the pathological damage of colon29. Liu et al. collected the main active components of Portulacae Herba, constructed interaction network of target proteins of liver cancer, and found that ketones may be the main material basis of its anti-liver cancer, which is related to the regulation of MAPK signaling pathway30. Liu et al. utilized network pharmacology to screen 102 active components of Danzhi Xiaoyao Powder, 147 corresponding targets and 52 intersecting targets with insomnia, and obtained the key components, key targets and key pathways of Danzhi Xiaoyao Powder in the treatment of insomnia31. Yang et al. presented network pharmacology to analyze the potential anti-tumor mechanisms of the main active components of Prunella vulgaris systematically at the molecular level32. Shen et al. discussed the possible mechanism of Wuling Powder in the treatment of diabetic nephropathy by network pharmacology, and found that Wuling Powder may reduce renal cell damage by regulating apoptosis related proteins, such as Caspases family protein and BCL2 Protein family33.In the past decade, with the rapid development of sequencing technology, a large number of genomics data such as genomics, proteomics, metabonomics and so on, have been generated, which has led to the changes in the research of traditional Chinese medicine for diseases. Network pharmacology has been proposed, which was developed on the basis of the rapid development of systems biology and computer technology, generating the "disease-gene-target-drug" interaction network. Through network analysis, we can systematically and comprehensively observe the intervention and influence of drugs on the disease network, reveal the mystery of the synergistic effect of multi branch drugs on the human body, and find out the multi-target new drugs with high efficiency and low toxicity. Network pharmacology of traditional Chinese medicine has become a new idea for drug mechanism research and new drug development33. Ren et al. utilized data mining methods to screen out 47 prescriptions, and found out 14 core drugs and 7 new prescriptions in order to search the medication rules and mechanism of TCM in the treatment of carotid atherosclerosis (CAS)34. Ga et al. utilized data mining method to select the top five active components of each Tibetan medicine with high frequency and network pharmacology was utilized to analyze the mechanism of Tibetan medicine in the treatment of high altitude polycythemia35. In order to study the medication rule of TCM intervention in iron death, Ou et al. constructed target-compound, compound-TCM, target-compound-TCM network, and frequency statistics was utilized to show that bitter and pungent herbs were the main herbs that could interfere with iron death, while cold herbs were the main ones, which mainly belonged to liver and lung meridians36. Pan et al. reprocessed a large number of Chinese medicine prescriptions for the treatment of primary liver cancer, and by analysis of data mining and network pharmacology medication regularity of effective traditional Chinese medicine prescriptions in the treatment of primary liver cancer was obtained37. Zheng et al. presented four classifiers to infer compound-target interaction network in the process of network pharmacology analysis38.In the recent years, data mining methods have been applied to extract useful information from lots of TCM dataIn order to better mine omics data and construct "disease-gene-target-drug" interaction network, deep learning model was utilized in this paper. Taking acute lung injury (ALI) disease as an example, we selected two ALI-related target genes (REAL and SATA3), which have been verified biology experiment. The active compounds are collected from BindingDB database for two key target genes as positive samples. The inactive compounds are generated from DUD-E as the negative samples. The different molecular descriptors and molecular fingerprints are utilized to characterize each compound, which form the full feature set and contain 374 features. With full feature set collected, forest graph embedded deep feed forward network is trained, which is utilized to identify the compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) for the treatment of acute lung injury.39.Step 1: feature graph constructionForest graph-embedded deep feedforward network (forgeNet) is a novel machine learning algorithm, which has been successfully applied to solve classification problem with TCGA RNA-seq data. The flowchart of forgeNet is depicted in Fig. The flowchart of feature graph construction is depicted in Fig. Step 2: deep neural networkTo integrate the directed graph set 12. Every layer of GEDFN is introduced as followed.The feature graph obtained the previous step are embedded into this part. With the processed features graph-embedded deep feedforward networks (GEDFN) is used to train and make the classification for the unknown data40. Then the BindingDB database (http://www.bindingdb.org/bind/index.jsp) is searched for the known active compounds of these two key target genes38. The active ligands are screened with the condition that IC50 < 5000 nmol L−1. The collected active compounds are labeled as positive samples. In order to collect the negative samples, 20% of the active ligands are randomly selected and uploaded to DUD-E database (http://dude.docking.org/) to generate the inactive ligands41. Active and inactive compound sets form the dataset. The structure of each compound is Simplified Molecular Input Line Entry System (SMILE), so the molecular descriptors and molecular fingerprints of each compound must be obtained as the feature vectors. In this paper, RDKit package is utilized to create the molecular descriptors and molecular fingerprints of each ligand. Molecular descriptors contains 208 features, such as topological polar surface area (TPSA) descriptor, number of valence electros, number of radical electrons, charge information and number of Aliphatic Carbocycles. MACCS fingerprints contains 166 molecular characteristic sites, such as Atom Pairs, topological torsions.Data preparation. Two key target genes: signal transducer and activator of transcription 3 (STAT3), and nuclear transcription factor- κ B/p65  were proved to be mainly involved in the key pathways related to acute lung injury (ALI), and losely related to ALI diseases in the literatureModel training. According to the collected data, the feature vector of each ligand is used as input for forgeNet. After training phase, the unknown compounds are screened for the target disease.In order to construct "disease-gene-target-drug" interaction network more accurately, an ALI-related compound identification based on deep learning model and target genes is proposed. The flowchart is depicted in Fig. 42, RF43, logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest44) are utilized to identify the compounds associated with diseases. Five evaluation criteria of classifier performance are utilized, which are SN, SP, Kappa, MCC and F1, respectively.In this section, active and inactive ligands of two key target genes: REAL and SATA3 about ALI disease are collected. For REAL, 966 ligands are collected, which contain 146 positive samples and 820 negative samples (Data1). For SATA3, 193 active ligands and 1210 inactive ligands are collected (Data2). Molecular descriptors and molecular fingerprints of each ligand could be obtained, which contains 374 features. In order to better reflect the effectiveness of forgeNet, three classical classifiers  of eight methods with Data1 and Data2 by threefold cross validation, fivefold and tenfold cross validation methods are listed in Tables Receiver operating characteristic (ROC) and Precision-Recall (PR) curves are two important curves to evaluate the performance of machine learning algorithm. ROC curve is based on false positive rate (FPR) and true positives rate (TPR). PR curve is based on Recall and Precision. Area under curve (AUC) is defined as the area under the ROC curve or PR curve surrounded by the coordinate axis. PR and ROC curves of eight methods with Data1 and Data2 by threefold cross validation, fivefold and tenfold cross validation methods are depicted in Figs. d-Glucopyranoside, Gancaonin N, Chrysin -6-C-glucoside-8-C-arabinoside, Chrysin-6-C-arabinoside 8-C-glucoside Liquiritin, Baicalin, Isomer of Baicalin, Oroxylin A-7-O-β-d-glucuronide, Chrysin-7-O-glucuronide, Isoliquiritin, Wogonoside, Liquiritigenin, Baicalein, Isoliquiritigenin, Wogonin, Oroxylin A, and Glycyrrhetinic acid) in EhD can dock with ALI related target genes and have high potential biological activity, which have been proved in the reference39. Dexamethasone (DXMS) is used as control drug. Molecular descriptors and molecular fingerprints are also utilized to obtain the features of 20 chemical compounds. Data1 and Data2 are utilized as the training sets in order to predict 20 chemical compounds, respectively. SVM, RF and gcForest are selected as comparison methods. The prediction ranks are listed in Table 39. Thus the results reveal that forgeNet could screen the chemical compounds more accurately than SVM, RF and gcForest. We also analyze the mechanism of action of the highly ranked compounds for treatment of ALI. In the highly ranked compounds, Glycyrrhizin has a protective effect on acute lung injury through the activation and increase of Nrf2 nuclear translocation45. Baicalin plays a role in regulating the inflammatory response of ALI by stimulating regulatory T cells and inhibiting the release of IL6 and interleukin-23, which could lead to the decrease of Th17 (T helper cell 17) cells in order to affect the immune balance between Th17 and Treg response46. Baicalein can down regulate the mRNA expression of STAT3 and STAT4 in T cell JAK STAT signal pathway in order to promote T cell proliferation, and play an immune and anti-inflammatory role.Erhuang decoction (EhD) is a traditional heat clearing and detoxifying prescription, which is composed of Radix Scutellariae, Rhizoma Coptidis and licorice. 19 active chemical compounds  and Dexamethasone (DXMS) and the results reveal that forgeNet could infer the compounds of disease related more accurately. We also test the influence of different feature sets on the identification results and find the feature set based on molecular descriptors and molecular fingerprints could improve the compound identification accuracy of methods.Network pharmacology has become a frontier and hot spot in the field of traditional Chinese medicine research. This research method can effectively predict the effective components, target and side effects of drugs, and is conducive to the process of modernization of traditional Chinese medicine. In order to construct "disease-gene-target-drug" interaction network more accurately, forest graph embedded deep feed forward network is utilized to infer "disease-compound" network in this paper. According to acute lung injury, two ALI-related target genes (REAL and SATA3) are selected, and the active and inactive compounds of the two corresponding target genes are collected, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. By threefold, fivefold and tenfold cross validation methods, the experimental results show that forgeNet has the better performance than SVM, RF, LR, NB, XGBoost, LightGBM and gcForest in terms of In the further we will apply the method to prioritize the compounds in other ALI-related and other diseases related TCM prescriptions.


To investigate the potential of ginsenosides in treating osteoporosis, ginsenoside compound K (GCK) was selected to explore the potential targets and mechanism based on network pharmacology (NP). Based on text mining from public databases, 206 and 6590 targets were obtained for GCK and osteoporosis, respectively, in which 138 targets were identified as co-targets of GCK and osteoporosis using intersection analysis. Five central gene clusters and key genes  were identified based on Molecular Complex Detection (MCODE) analysis through constructing a protein–protein interaction network using the STRING database. Gene Ontology (GO) analysis implied that phosphatidylinositol-related biological process, molecular modification and function may play an important role for GCK in the treatment of osteoporosis. Function and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that the c-Fms-mediated osteoclast differentiation pathway was one of the most important mechanisms for GCK in treating osteoporosis. Meanwhile, except for being identified as key targets based on cytoHubba analysis using Cytoscape software, MAPK and PI3K-related proteins were enriched in the downstream of the c-Fms-mediated osteoclast differentiation pathway. Molecular docking further confirmed that GCK could interact with the cavity on the surface of a c-Fms protein with the lowest binding energy , and their complex was stabilized by hydrogen bonds (Thr578 (1.97 Å), Leu588 , Ala590  and Cys 666 (1.93 Å)), van der Waals and alkyl hydrophobic interactions. Summarily, GCK could interfere with the occurrence and progress of osteoporosis through the c-Fms-mediated MAPK and PI3K signaling axis regulating osteoclast differentiation. Osteoporosis is a metabolic and systemic bone disease characterized by a loss of bone mass and the micro-architectural deterioration of bone tissue, susceptibly resulting in bone fragility and fracture ,2. Osteoof Panax ginseng C.A. Meyer, has been used as a tonic remedy for more than 2000 years in Asia [Natural products are important and reliable resources for the prevention and treatment of osteoporosis because they have fewer side effects and are more suitable for long-term application, compared with chemosynthetic medicines . Ginseng in Asia . Ginseno in Asia . Ginseno in Asia ,12,13,14 in Asia . GCK is  in Asia ,16. TherNetwork pharmacology (NP) has recently been proposed as a promising approach to integrating database mining, bioinformatics analysis, topological analysis and molecular simulation, and is widely applied to discover potential medicinal ingredients from herbal medicines and to predict their possible pharmacology mechanism at the molecular level . Given tA total of 206 and 6590 molecular targets were obtained for GCK and osteoporosis in this study, respectively. The overlapping targets of GCK and osteoporosis were considered as the potential targets of GCK-treated osteoporosis. Based on the intersection analysis, a total of 138 molecular targets were identified as co-targets of GCK and osteoporosis, and are shown in a Venn diagram .p ≥ 0.900), which contained 95 nodes and 365 edges  are shown in GO and KEGG analysis were further used to investigate the BP, CC, MF and signaling pathways of the co-targets of GCK and osteoporosis. The results indicated that 210, 55 and 66 terms were enriched for BP, CC and MF, respectively. The top 20 BP, CC and MF terms , Leu588 , Ala590  and Cys 666 (1.93 Å) (666 (4.45 Å), Leu785 , Ala800(4.52 Å) and Arg801 of the c-Fms protein  a. The co(1.93 Å) b,c, resp protein b. The vad Asp806 b. TherefOsteoporosis is a considerable clinical and public health burden because of its association with age-related fractures . With th−6 cm/s [578 (1.97 Å) and Ala590 (2.16 Å and 2.84 Å) were located in the juxta-membrane domain (which functions as an autoinhibitory region) and nucleotide-binding loop, respectively. Additionally, this was similar to quinolone  were significantly enriched in KEGG analysis ,20,21,22−6 cm/s ,24. Ther−6 cm/s ,26. The −6 cm/s . c-Fms i−6 cm/s . Previou−6 cm/s ,28. AddiOf the osteoclast differentiation-related pathways, the PI3K-AKT and MAPK signaling pathways were just to the downstream of c-Fms signaling. In addition, we found that phosphatidylinositol-related bioprocesses, proteins and signaling played an important role for GCK in the treatment of osteoporosis based on GO and KEGG enrichment analysis. This was especially true for PI3K-related signaling because subunit proteins of PI3K were included in the topped cluster of the PPI network and key genes which were verified with MCODE analysis and key gene analysis. Although it has not been reported for PI3K-related signaling in other studies about ginsenoside-treated osteoporosis, the PI3K/AKT pathway was considered to participate in anti-osteoporosis by promoting the proliferation of osteoblast precursors and the osteoblastic differentiation of BMSCs , as well as autophagy and the differentiation of osteoclasts ,29,30,31In addition to phosphatidylinositol-related bioprocesses, proteins and signaling, GRB2-ERK was the other signaling pathway of c-Fms-mediated osteoclast differentiation. ERK is a member of the MAPK family which transduces extracellular stimuli to alter gene expression and has been shown to play a role in diverse cellular events ranging from proliferation and differentiation to apoptosis . It was Summarily, GCK has been verified to be potential beneficial in the treatment of osteoporosis based on NP in this study. Moreover, GCK could influence the occurrence and progress of osteoporosis through interacting with 138 potential target proteins at least, of which 16 targets were enriched in the osteoclast differentiation pathway based on KEGG analysis, and c-Fms-mediated osteoclast differentiation signaling may be one of the most important mechanisms for GCK in treating osteoporosis. Meanwhile, it was proven that GCK could bind to the cavity on the surface of c-Fms proteins with the lowest binding energy  based on molecular docking. Additionally, PI3K and MAPK-related proteins were not only identified as important targets, but were also enriched in the downstream pathways of c-Fms-mediated osteoclast differentiation signaling. Therefore, we proposed that the c-Fms-mediated MAPK and PIK3 signaling axis may be the potential mechanism for GCK in the treatment of osteoporosis by interfering with osteoclast differentiation , Similarity Ensemble aApproach (SEA)  and SwissTarget Prediction  databases under the condition of Homo sapiens. Gene names and organisms were standardized through manual retrieval based on the Uniprot database  to avoid the over-annotation of similar proteins such as paralogs and putative products of pseudogenes.The targets of GCK were predicted and obtained from PharmMapper , Online Mendelian Inheritance in Man (OMIM) , Therapeutic Target Database (TTD) , the Human Phenotype Ontology (HPO) , DisGeNET , DigSee  and home-for-researchers . The duplicate and redundant proteins or genes were deleted.The osteoporosis-related target proteins were obtained through online searches for “osteoporosis”, “fragile bones” and “bone fragility” under the condition of Homo sapiens in the following databases: GeneCards . The conditions of the PPI network construction were limited to “Homo sapiens” with the highest confidence score > 0.9. Furthermore, six kinds of topological properties  and EcCentricity) of the PPI network were calculated to screen the key genes based on the topological importance using Cytoscape software (ver. 3.8.0) and the Cytoscape plugin cytoHubba [The intrinsic relationships between these putative targets of GCK-treated osteoporosis were analyzed further based on the STRING database  with p < 0.05. GO terms included three categories: biological process (BP), molecular function (MF), and cellular component (CC).Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to reveal the potential biological mechanism of GCK in the treatment of osteoporosis. GO and KEGG enrichment were conducted through the online analysis tool of The Database for Annotation, Visualization and Integrated Discovery  . The 2D structure of GCK was downloaded from the PubChem database  and its conformation of minimum energy was obtained through ChemOffice software (ver. 10) [Molecular docking was used to validate the potential mechanism of key proteins in osteoporosis-related pathways using Auto Dock software (ver. 1.5.6)  in this ver. 10) . The pro