Method for designing Chinese herbal medicine feed additive based on network pharmacology

Abstract

The invention discloses a method for designing a Chinese herbal medicine feed additive based on network pharmacology. The method comprises the following steps: acquiring a target spot of a target biological process of livestock and poultry; carrying out PPI protein interaction analysis on the obtained target spot to obtain a core target spot; obtaining related components corresponding to the core target and screening active components from the related components; searching corresponding Chinese herbal medicines through the active components; acquiring a node degree value of a core target corresponding to the Chinese herbal medicine; calculating proportioning parameters of the Chinese herbal medicines to form the Chinese herbal medicine feed additive formula. The invention provides a systematic theoretical basis for the design of Chinese herbal medicine feed additives, and provides technical support for exploring wide application of Chinese herbal medicines in the field of feed additives.

Description

Technical Field

[0001] The present invention relates to the technical field of feed additives, and in particular to, but not limited to, a method for designing a Chinese herbal medicine feed additive based on network pharmacology. Background Art

[0002] Chinese herbal feed additives are products of deep processing of Chinese herbal medicines. They are formed by using medicinal plants as raw materials alone or in mixed proportions. They play an important role in animal health care, improving livestock and poultry production performance, improving livestock and poultry product quality, and improving forage quality. As the birthplace of Chinese herbal medicine, China has a long history of producing and using Chinese herbal medicines. Since the 1980s, research on Chinese herbal feed additives has been widely conducted, and certain experimental results have been achieved. my country is a major country in wild and artificially cultivated Chinese herbal medicines, with more than 5,000 kinds of medicinal materials in conventional use, about 12,000 kinds of medicinal plants, and about 2,500 kinds of plants used in livestock and poultry production. There are even more types of compound preparations.

[0003] Network pharmacology is based on multidisciplinary theories such as systems biology, genomics, and proteomics. It uses high-throughput omics data analysis, computer simulation, and network database retrieval to reveal the network relationship of drug-gene-target-disease interactions, predict the mechanism of action of drugs through network relationships, evaluate the efficacy of drugs, and design and develop new drugs. The research ideas of network pharmacology are highly consistent with the scientific concepts of the integrity and systematicity of traditional Chinese medicine theory. With the development of technologies such as big data and cloud computing, it provides technical support for the specialized application of Chinese herbal medicines and the analysis of their mechanisms of action.

[0004] At present, the design of Chinese herbal feed additives only relies on simple adjustments of some empirical prescriptions, or simple additive combinations of single Chinese herbal medicines. There is a lack of systematic research design ideas and theoretical support, and there is a lack of research on the mechanism of action of Chinese herbal feed additives. This leads to the lack of pertinence and specificity of Chinese herbal feed additives on the market in terms of their functional effects on specific species and targets. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method for designing Chinese herbal feed additives based on network pharmacology, which solves the problems of the current Chinese herbal feed additives lacking species specificity and poor specificity of action effects.

[0006] The technical solution of the embodiment of the present invention is achieved as follows:

[0007] The embodiment of the present invention provides a method for designing a Chinese herbal feed additive based on network pharmacology, the method comprising:

[0008] Obtain targets of the target biological process of livestock and poultry; perform PPI protein interaction analysis on the acquired targets to obtain core targets; obtain relevant components corresponding to the core targets and screen out active components therefrom; find corresponding Chinese herbal medicines through the active components; obtain the node degree values ​​of the core targets corresponding to the Chinese herbal medicines; calculate the ratio parameters of the Chinese herbal medicines to form a formula for Chinese herbal feed additives.

[0009] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0010] In an embodiment of the present invention, a method for designing Chinese herbal feed additives is established based on network pharmacology, aiming to effectively combine network pharmacology with the theoretical system of traditional Chinese medicine, and to target the key targets that affect the biological processes in livestock and poultry and the corresponding active ingredients and Chinese herbal medicines, thereby solving the problems of the current Chinese herbal feed additives lacking species targeting and poor specificity of effects. At the same time, in the design process, ratio parameters are proposed to solve the technical problem of determining the proportion of each component of Chinese herbal feed additives. The present invention provides a systematic theoretical basis for the design of Chinese herbal feed additives and provides technical support for exploring the wide application of Chinese herbal medicines in the field of feed additives. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0012] Figure 1 A schematic diagram of a process for designing a Chinese herbal feed additive based on network pharmacology provided by an embodiment of the present invention;

[0013] Figure 2 A schematic diagram of target points of a goat immune-related target biological process provided by an embodiment of the present invention;

[0014] Figure 3 A schematic diagram of the results of PPI protein interaction analysis of 1680 targets obtained in an embodiment of the present invention;

[0015] Figure 4 A schematic diagram of the top 5 core targets provided by an embodiment of the present invention;

[0016] Figure 5 A schematic diagram of related components corresponding to the core targets provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0019] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0020] Figure 1 A schematic diagram of a method for designing a Chinese herbal feed additive based on network pharmacology provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method comprises at least the following steps:

[0021] Step S110, obtaining the target of the target biological process of livestock and poultry;

[0022] Step S120, performing PPI protein interaction analysis on the acquired targets to obtain core targets;

[0023] Step S130, obtaining relevant components corresponding to the core target and screening out active components therefrom;

[0024] Step S140, searching for corresponding Chinese herbal medicines through the active ingredients;

[0025] Step S150, obtaining the node degree value of the core target corresponding to the Chinese herbal medicine;

[0026] Step S160, calculating the ratio parameters of the Chinese herbal medicine to form a formula of a Chinese herbal feed additive.

[0027] Here, core targets, i.e. key genes, are genes that play a vital role in biological processes. In related pathways, the regulation of other genes is often affected by this gene. Therefore, core targets are often important targets and research hotspots. Core targets can be constructed through protein interactions to form interaction networks, and then screened based on the network topology.

[0028] The embodiment of the present invention establishes a method for designing Chinese herbal feed additives based on network pharmacology, aiming to effectively combine network pharmacology with the theoretical system of traditional Chinese medicine, and to target the key targets that affect the biological processes in livestock and poultry and the corresponding active ingredients and Chinese herbal medicines, thereby solving the problems of the current Chinese herbal feed additives lacking species targeting and poor specificity of effects. At the same time, in the design process, ratio parameters are proposed to solve the technical problem of determining the proportion of each component of Chinese herbal feed additives. The present invention provides a systematic theoretical basis for the design of Chinese herbal feed additives, and provides technical support for exploring the wide application of Chinese herbal medicines in the field of feed additives.

[0029] In some possible embodiments, the above-mentioned step S110 "obtaining targets of target biological processes of livestock and poultry" is specifically implemented through the following process: using the species name of livestock and poultry as the keyword to collect the gene data of livestock and poultry in the species interaction item "Organisms" in the CTD database; collecting the gene data related to the target biological process in the GO annotation item "Gene Ontology" in the CTD database, importing the gene data related to the target biological process into a spreadsheet for integration and removing duplicate values; and then taking the intersection of these two data using Venny 2.1.0 to obtain the targets of the target biological process of livestock and poultry.

[0030] Here, the address of the CTD database is http: / / ctdbase.org / , and the target bioengineering involves the use of biotechnology to improve the production performance, health and welfare of livestock and poultry, such as the following projects: genome selective breeding, artificial insemination and embryo transplantation, microbiome engineering, disease resistance breeding, nutritional genomics, reproductive technology engineering, etc., which are not limited in this embodiment of the present invention.

[0031] In some possible embodiments, the above-mentioned step S120 "performing PPI protein interaction analysis on the acquired targets to obtain core targets" is specifically implemented through the following process: using the STRING database to perform PPI protein interaction analysis and setting a minimum required interaction score level; wherein the minimum required interaction score level includes highest confidence, high confidence, medium confidence, and low confidence; using the cytoHubba plug-in in the cytoscape3.9.1 software to obtain the network topology parameter score value of each target, and obtaining the targets ranked in the top 5 to 10 as core targets.

[0032] Here, the address of the STRING database is https: / / string-db.org / , and the minimum required interaction score levels include the highest confidence, such as 0.900, high confidence, such as 0.700, medium confidence, such as 0.400, and low confidence, such as 0.150.

[0033] The CytoHubba plug-in is a network analysis-based tool for identifying core targets or subnetworks in biological networks. It integrates a variety of different algorithms for extracting key information from complex network structures. These different algorithms can be used to evaluate the importance of nodes in the network, and each algorithm has its own unique principles and applicable scenarios. When selecting an algorithm, users should make comprehensive considerations based on specific research questions and network characteristics to select the most suitable algorithm to extract and analyze key information. The embodiment of the present invention uses the cytoHubba plug-in to screen core targets based on network topology parameters, where the network topology parameters include the following:

[0034] Degree of a node: The importance of a node is evaluated based on the degree of the node (the number of neighboring nodes). The higher the degree of a node, the stronger its connectivity in the network and is generally considered a key node. The average shortest path length from a node to all other nodes in the network: Measures the average shortest path length from a node to all other nodes in the network. The higher the Closeness value of a node, the closer it is to the center of the network and the greater its influence on the network. The number of times a node appears in all shortest paths: The importance of a node is evaluated by calculating the number of times a node appears in all shortest paths. The higher the Betweenness value of a node, the greater the bridge role it plays in the network. Node Extension Coefficient (EPC): An algorithm based on node extension coefficient that takes into account the local and global connectivity of nodes. The higher the EPC value of a node, the more likely it is to play a key role in the network. Clustering coefficient of a node in a network and the degree of neighbor nodes (DMNC): The importance of a node is evaluated based on the clustering coefficient of a node in a network and the degree of neighbor nodes. Nodes with higher DMNC values ​​generally have stronger local connectivity and higher global influence. Average Clustering Coefficient (MCC) of the Maximum Connected Subgraph of a Node: The importance of a node is evaluated by calculating the average clustering coefficient of the maximum connected subgraph of the node. Nodes with higher MCC values ​​play an important role in maintaining network connectivity. Size of the Largest Subgraph in a Node's Neighborhood (MNC): The Maximum Neighborhood Component algorithm calculates the size of the largest subgraph in a node's neighborhood. The algorithm looks for nodes with the largest subgraphs, which have important connections in the network. Reciprocal of the Average Distance from a Node to All Other Nodes in the Network (Radiality): Measures the reciprocal of the average distance from a node to all other nodes in the network. Nodes with higher Radiality values ​​are closer to the core area of ​​the network. Number of Nodes as Unique Paths in All Shortest Paths (Stress): The importance of a node is evaluated based on the number of nodes as unique paths in all shortest paths. Nodes with higher Stress values ​​play more important roles in the network. Bottleneck Effect of a Node in a Network (BottleNeck): Evaluates the bottleneck effect of a node in a network, that is, the key role of a node in maintaining network connectivity. Nodes with higher BottleNeck values ​​are more likely to be key nodes in the network. The longest distance from a node to all other nodes in the network (EcCentricity): measures the longest distance from a node to all other nodes in the network. The smaller the EcCentricity value, the more important the position of the node in the network. Clustering Coefficient of a node (Clustering Coefficient): measures the clustering coefficient of a node, that is, the ratio of the actual number of edges between the node's neighbor nodes to the maximum number of edges that can exist. The higher the clustering coefficient, the tighter the connection between its neighbor nodes.

[0035] In some possible embodiments, the above-mentioned step S130 "obtaining the related ingredients corresponding to the core target and screening out active ingredients therefrom" is specifically implemented through the following process: using the UniProt database (https: / / www.uniprot.org / ) to convert the gene name (gene name) of the core target into a protein name (protein name), and searching the target name "Target name" item in the TCMSP database for the obtained protein name to obtain the related ingredients "Related ingredients" corresponding to the core target; summarizing the obtained related ingredients through a spreadsheet and removing duplicate values, and then screening through preset screening conditions to obtain active ingredients; wherein, the screening conditions are determined in combination with oral bioavailability and drug-like index.

[0036] In some possible embodiments, the screening conditions are oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18.

[0037] In some possible embodiments, the above step S140 "searching for corresponding Chinese herbal medicines through the active ingredients" is further implemented through the following process: the active ingredients screened in S130 are input into the chemical name "Chemical name" item in the TCMSP database, and the Chinese herbal medicines corresponding to the active ingredients are obtained in the related herbs "Related Herbs" item.

[0038] In some possible embodiments, the above-mentioned step S150 "the node degree value of the core target corresponding to the Chinese herbal medicine" is further implemented through the following process: the Chinese herbal medicine name "Herb name" item in the TCMSP database is searched in turn for the Chinese herbal medicine obtained in S130, and the node degree value is screened in the network parameter "NetworkParameters" item in the related target "Related Targets" item, and the screening condition is the core target obtained in S120, and all the screening results are sorted in a spreadsheet such as Excel.

[0039] In some possible embodiments, the above-mentioned step S160 "calculating the ratio parameters of Chinese herbal medicines to form a formula of Chinese herbal feed additives" is specifically implemented through the following process: comprehensively calculating the network topology parameter score value of the core target, i.e., the "Score" value, and the degree value of the Chinese herbal medicine corresponding to the core target, and obtaining the ratio parameters of the Chinese herbal medicine after weighted calculation; determining the proportion of each component in the Chinese herbal feed additive based on the ratio parameters, and selecting the Chinese herbal medicine with the highest ratio parameter for each active ingredient to form a formula of Chinese herbal feed additives.

[0040] The following takes goat immunity-related target biological processes as an example and describes the method for designing herbal feed additives based on network pharmacology in combination with a specific embodiment. However, it is worth noting that this specific embodiment is only for better illustrating the present invention and does not constitute an improper limitation of the present invention.

[0041] S1. Obtaining targets of goat immune-related target biological processes: Using “Caprahircus” as the keyword, the gene data of goats were collected in the “Organisms” item in the CTD database (http: / / ctdbase.org / ), and the gene data related to immune-related target biological processes were collected in the “GeneOntology” item (see Table 1). After the gene data related to immune-related target biological processes were integrated through Excel to remove duplicate values, the two data were intersected using Venny 2.1.0 to obtain the targets of goat immune-related target biological processes, such as Figure 2 shown.

[0042] Table 1 Immune-related target biological processes

[0043]

[0044] It can be seen that 16,623 goat gene data and 2,998 gene data of immune-related target biological processes were obtained in the CTD database. After taking the intersection, 1,680 targets of goat immune-related target biological processes were obtained.

[0045] S2, perform PPI protein interaction analysis on the collected targets to obtain core targets: Use the STRING database (https: / / string-db.org / ) to perform PPI protein interaction analysis, such as Figure 3As shown in Figure 1, the target dataset related to goat immunity obtained in S1 was imported into the STRING database, the species was set to goat (Caprahircus), "full STRING network" was selected as the Network type in the setting options, "evidence" was selected as the meaning of network edges, "Textmining, Experiments, Databases, Coexpression, Neighborhood, Gene Fusion, Co occurrence" was checked as the active interaction sources, and the minimum required interaction score was selected as the highest confidence (0.900). The core targets were screened according to the Betweenness value in the network topology parameters using cytoscape3.9.1 software, and "Top 5node(s) ranked by Betweenness" was entered in the "Hubbanodes" tab, and the top 5 targets were selected as the core targets (see Figure 4 and Table 2 ).

[0046] The STRING database (https: / / string-db.org / ) was used to perform PPI protein interaction analysis on 1680 targets obtained from goat immune-related target biological processes. The results are as follows Figure 3 The obtained PPI network data were visualized using cytoscape3.9.1 software, and the core targets were screened using the Betweenness value in the network topology parameters in the cytoHubba plug-in. Figure 4 The five core targets shown in Figure 2 are CD4, TP53, CTNNB1, IL6, and ACTB. The “Score” values ​​of the core targets are shown in Table 2. Figure 4 The darker the color, the greater the "Score" value of the target, and the more important the role of the target in the PPI network.

[0047] Table 2 Top 5 targets in the network ranked by betweenness centrality

[0048]

[0049] S3, obtain the related ingredients corresponding to the core target: use the Uniprot database to convert the gene name of the core target into a protein name, search the obtained protein name in the "Target name" item in the TCMSP database, and obtain the "Related ingredients" corresponding to the core target (see Figure 5 ).

[0050] like Figure 5 As shown in the figure, after removing duplicate values, a total of 55 related components corresponding to the core targets were found, namely (-)-epigallocatechin-3-gallate, ellipticine, genistein, alpha-santalol, luteolin, caffeine, daidzein, emodin, kainic acid, eupatilin, nobiletin, resveratrol, beta-elemene, 3,4,5-trihydroxybenzoic acid, fisetin, tetrandrine, triptolide, juglone, aloe-emodin, diosgenin, acacetin, 6-gingerol, apigenin, cantharidin, progesterone, baicalein, tanshinone iia, wogonin, ursolic acid, quercetin, 18beta-glycyrrhetinic acid, beta-carotene, atractylenolide i, melanin, gamma-aminobutyric acid, morphine, rutin, corilagin, piperine, aucubin, sinomenine, farnesol, myricetin, lauric acid, matrine, paeoniflorin, acetaldehyde, beta-caryophyllene, sophocarpine, sophoridine, oroxylin a, methyl palmitate, (-)-epicatechin, danshensu, linolenic acid.

[0051] S4, screening related ingredients to obtain active ingredients: the obtained related ingredients were summarized through Excel and duplicate values ​​were removed, and then the active ingredients were screened through oral bioavailability (OB) value and drug-likeness (DL) value. The screening conditions were oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18.

[0052] The OB values ​​and DL values ​​of relevant ingredients were collected, and 21 active ingredients were further screened out through OB value ≥ 30% and DL value ≥ 0.18 (see Table 3), including aloe-emodin, diosgenin, sophocarpine, matrine, nobiletin, sophoridine, (-)-epigallocatechin gallate, paeoniflorin, fisetin, triptolide, tanshinone IIA, quercetin, piperine, melaleucalyptol A, β-carotene, luteolin, acacetin, baicalin, sinomenine, ellipticine, and wogonin.

[0053] Table 3 Screened active ingredients

[0054]

[0055] S5, search for the corresponding Chinese herbal medicine by active ingredients: enter the active ingredients screened in S4 in the "Chemical name" item in the TCMSP database, and obtain the Chinese herbal medicine corresponding to the active ingredient in the "Related Herbs" item (see Table 4).

[0056] Table 4 Chinese herbal medicines corresponding to active ingredients

[0057]

[0058]

[0059]

[0060] S6, obtain the degree value data of the core targets in S2 corresponding to the Chinese herbal medicines: search the Chinese herbal medicines obtained in S5 in the "Herbname" item in the TCMSP database in turn, and filter the degree value in the "NetworkParameters" item in "RelatedTargets". The screening condition is the core targets obtained in S2, and all the screening results are sorted in Excel (see Table 5).

[0061] Table 5 Degree values ​​of core targets corresponding to Chinese herbal medicines

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] S7, calculate the ratio parameters of Chinese herbal medicines to form a formula for Chinese herbal feed additives for goats: after weighted calculation of the "Score" value of the core target in the goat-immune network (see Table 2) and the degree value corresponding to the core target (see Table 5), obtain the ratio parameters of Chinese herbal medicines (see Table 6), determine the proportion of each component in the Chinese herbal feed additives according to the ratio parameters, select the Chinese herbal medicine with the highest ratio parameter for each active ingredient, and form a formula for Chinese herbal feed additives for goats (see Table 7). The material basis for the specific action of the Chinese herbal feed additive is that the Chinese herbal feed additives designed through network pharmacology contain active ingredients with target-targeted effects, and these ingredients all play a role in the core targets of goat immunity.

[0072] Table 6 Ratio parameters of active ingredients corresponding to Chinese herbal medicines

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[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] Table 7 A Chinese herbal medicine feed additive formula for improving goat immunity

[0083]

[0084] At present, all patents on Chinese herbal feed additives directly give formulas, and the design methods are just changes to some classic prescriptions, lacking a systematic theoretical basis. The present invention designs Chinese herbal feed additives based on network pharmacology and combines the theoretical system of traditional Chinese medicine, which broadens the application of Chinese herbal medicine in Chinese herbal feed additives and provides an innovative method and important theoretical basis for the development of Chinese herbal feed additives.

[0085] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for designing a Chinese herbal feed additive based on network pharmacology, characterized in that: include: Obtain targets for target biological processes in livestock and poultry; Perform PPI protein interaction analysis on the acquired targets to obtain core targets; Obtaining relevant components corresponding to the core target and screening out active components therefrom; Find the corresponding Chinese herbal medicine through the active ingredients; Obtaining the node degree value of the core target corresponding to the Chinese herbal medicine; The ratio parameters of the Chinese herbal medicine are calculated to form a formula of a Chinese herbal feed additive.

2. The method according to claim 1, characterized in that The target of the target biological process of livestock and poultry is obtained, including: The gene data of livestock and poultry were collected from the species interaction items in the CTD database using the species names of livestock and poultry as keywords; the gene data related to the target biological process were collected from the GO annotation items in the CTD database, and imported into a spreadsheet for integration and then duplicate values ​​were removed; finally, the gene data of livestock and poultry and the integrated gene data related to the target biological process were intersected using Venny 2.1.0 to obtain the targets of the target biological process of livestock and poultry.

3. The method according to claim 1, characterized in that The obtained targets are subjected to PPI protein interaction analysis to obtain core targets, including: Use the STRING database to perform PPI protein interaction analysis and set a minimum required interaction score level; wherein the minimum required interaction score level includes highest confidence, high confidence, medium confidence, and low confidence; The cytoHubba plug-in in the cytoscape3.9.1 software was used to obtain the network topology parameter scores of each target, and the targets ranked in the top 5 to 10 were selected as core targets; The network topology parameters include the degree of the node, the average shortest path length from the node to all other nodes in the network, the number of times the node appears in all shortest paths, the node expansion degree, the clustering coefficient of the node in the network and the degree of the neighboring nodes, the average clustering coefficient of the largest connected subgraph of the node, the size of the largest subgraph in the node neighborhood, the reciprocal of the average distance from the node to all other nodes in the network, the number of nodes as unique paths in all shortest paths, the bottleneck effect of the node in the network, the longest distance from the node to all other nodes in the network, and the clustering coefficient of the node.

4. The method according to claim 1, characterized in that: The step of obtaining the relevant components corresponding to the core target and screening out active components therefrom includes: The UniProt database was used to convert the gene names of the core targets into protein names, and the obtained protein names were compared with the target genes in the TCMSP database. Label Name Search for items to obtain relevant components corresponding to the core targets; The obtained related components are summarized through a spreadsheet and duplicate values ​​are eliminated, and then the active components are screened through preset screening conditions; wherein the screening conditions are determined in combination with oral bioavailability and drug-like index.

5. The method according to claim 4, characterized in that The screening conditions are oral bioavailability ≥ 30% and drug-like index ≥ 0.

18.

6. The method according to any one of claims 1 to 5, characterized in that: The method of searching for the corresponding Chinese herbal medicine through the active ingredients includes: Input the screened active ingredients in the chemical name item of TCMSP database, and obtain the Chinese herbal medicine corresponding to the active ingredients in the relevant herbal medicine item.

7. The method according to any one of claims 1 to 5, characterized in that: The step of obtaining the node degree value of the core target corresponding to the Chinese herbal medicine includes: The Chinese herbal medicines obtained were retrieved in turn in the Chinese herbal medicine name items in the TCMSP database, the node degree values ​​of the core targets were screened in the network parameter items of the relevant target items, and all the screening results were sorted and arranged in a spreadsheet.

8. The method according to any one of claims 1 to 5, characterized in that: The step of calculating the ratio parameters of the Chinese herbal medicine to form a Chinese herbal medicine feed additive formula includes: The network topology parameter score value of the core target and the node degree value are comprehensively considered to obtain the ratio parameter of the Chinese herbal medicine through weighted calculation; The ratio of each component in the Chinese herbal feed additive is determined according to the ratio parameters, and the Chinese herbal medicine with the highest ratio parameter is selected for each active ingredient to form a formula of the Chinese herbal feed additive.

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