Method for screening fulvic acid angiogenesis promoting core active components and key targets

Abstract

The invention relates to the technical field of screening of pharmacodynamic substances of natural products, and particularly discloses a method for screening fulvic acid angiogenesis promoting core active components and key targets. According to the method, a first key target point set is obtained through system biological analysis, meanwhile, core active ingredients are screened in combination with priori knowledge, and independent target point re-prediction is conducted only on the basis of the active ingredient set to obtain a second key target point set. The method is characterized in that multi-dimensional convergence verification is carried out by comparing results of independent paths of the two methodologies, so that the false positive risk of a single path is overcome. 10 core active components such as lactic acid and 4 key targets such as PTGS2 are finally screened out, and the binding affinity is verified through molecular docking. According to the method, a technical closed loop of mutual independence-mutual verification is constructed, and objectivity and reliability of a screening result are ensured.

Description

Technical Field

[0001] This invention relates to the field of screening technology for pharmacodynamic substances in natural products, specifically a method for screening the core active ingredients and key targets of fulvic acid in promoting angiogenesis. This method employs a multidimensional convergence validation strategy, providing technical solutions for mechanism elucidation, target discovery, and lead compound selection. It is applicable to the material basis research of traditional Chinese medicine and natural products, as well as the research and development and quality evaluation of angiogenesis-promoting products. Background Technology

[0002] Angiogenesis, as a core component of embryonic development, tissue repair, and various pathological processes, has long been a key topic in the biomedical field, with the elucidation of its regulatory mechanisms and the development of intervention methods being crucial. Clinically, the treatment of chronic wounds such as ischemic cardiovascular and cerebrovascular diseases and diabetic foot ulcers urgently requires exogenous pro-angiogenic strategies. Currently, while biologics, represented by recombinant growth factors, have shown clear therapeutic potential, their inherent limitations, such as short half-life, complex delivery systems, and susceptibility to inducing abnormal angiogenesis at high concentrations, significantly restrict the breadth and depth of their clinical application.

[0003] Against this backdrop, natural products, with their sustainable sources, diverse components, and synergistic effects across multiple targets, have become a vital resource for discovering novel pro-angiogenic drugs. Fulvic acid, a complex organic mixture of humic acids with excellent water solubility, low molecular weight, and abundant oxygen-containing functional groups, is a prime example. Its chemical matrix consists of a wide variety of small molecules, including organic acids, amino acids, phenolic acids, and short-chain fatty acids, theoretically possessing good bioavailability. Existing research has confirmed the effects of fulvic acid on multiple biological dimensions, including antioxidation, anti-inflammation, and immunomodulation, and there is also sporadic evidence pointing to its potential impact on vascular endothelial function.

[0004] However, the value of fulvic acid is accompanied by the difficulty of its research. The extreme complexity of its chemical composition, significant batch-to-batch differences between samples from different sources, and the incompletely resolved interaction networks among its components constitute a formidable technical barrier. This makes the traditional "chemical separation-oriented, single-target validation" research model inadequate when dealing with fulvic acid, failing to efficiently and systematically reveal the material basis and molecular mechanisms behind its angiogenesis-promoting activity. This predicament has become a core bottleneck restricting the high-value transformation of such natural products.

[0005] To address the challenges of studying complex systems, network pharmacology, integrating analytical chemistry and systems biology, has emerged and rapidly become the mainstream paradigm for natural product mechanism research. This methodology begins with comprehensive chemical information acquired through UPLC-MS / MS technology, uses platforms such as SwissTargetPrediction for target prediction, and maps this prediction to disease target sets retrieved from databases like GeneCards and TTD. Building upon this, a protein-protein interaction network is constructed using the STRING database, and key targets are screened using Cytoscape software based on topological parameters such as node degree and betweenness centrality. Finally, GO and KEGG enrichment analyses are used to deduce the pathways of action. This standardized analytical workflow indeed provides powerful data integration capabilities for understanding complex pharmacological effects involving multiple components, targets, and pathways from a macroscopic perspective.

[0006] Nevertheless, when this standard paradigm is directly applied to highly heterogeneous research subjects like fulvic acid, its inherent methodological limitations are dramatically amplified, exposing several fundamental problems that cannot be ignored. First, the target prediction process suffers from inherent algorithmic bias. This technical approach heavily relies on comparing the structural similarity between ligands and known targets; this "look-back" strategy naturally favors those "star targets" that are currently receiving a lot of research attention. For the numerous structurally simple organic acid molecules in fulvic acid, the prediction results are often broad-spectrum but lack specificity, easily generating a large set of false positive targets, thus severely contaminating the sample space for subsequent analysis.

[0007] The integration of disease target data sources has inherent flaws. Different disease knowledge bases exhibit significant heterogeneity in their construction logic, inclusion criteria, and update frequency. Simply merging databases from diverse sources and with compound targets ignores the heterogeneity and complexity behind biological phenotypes, introduces systemic noise that is difficult to quantify, and may incorrectly include genes with weak associations to core phenotypes in the analysis.

[0008] The screening logic for key targets contains a critical logical leap. Defining the "core" based on PPI network topological centrality essentially equates the network's "traffic hub" status with the "driving factor" of pharmacological action. However, a node's high centrality only indicates its importance in information transmission within the biological network; it does not directly prove that it is a key node for a specific drug intervention phenotype. Furthermore, the inherent correlations between centrality indicators and their sensitivity to network parameter settings raise questions about the robustness and biological significance of methods that solely rely on ranking to screen targets.

[0009] This single-path derivation model lacks an effective orthogonal verification mechanism. Due to the cumulative effect of errors in each step, the final selected "core components / targets" often revert to a few well-known signaling pathways, such as PI3K-AKT or MAPK. While the "reasonableness" of this result is undeniable, its "reliability" and "novelty" as a scientific discovery are significantly diminished in the absence of an independent chain of evidence for verification. Molecular docking, as a supplementary method, is also not a panacea. Its results are extremely sensitive to receptor conformation, pocket definition, and scoring functions. If there are biases in the upstream target selection, molecular docking is more likely to become an "error amplifier" rather than an effective correction tool.

[0010] In utilizing existing literature evidence, current technologies also face a methodological dilemma. Completely abandoning literature means giving up valuable prior knowledge from real biological experiments; however, relying solely on quantitative indicators such as the number of publications or impact factors for rigid screening may stifle valuable new discoveries due to "evidence scarcity," turning the research process into a rigid data screening procedure lacking biological intuition. The core of improving the robustness of the entire screening process lies in how to organically integrate qualitative literature evidence containing expert knowledge with quantitative computational predictions to form a mutually verifying and complementary closed loop.

[0011] In summary, while network pharmacology provides a robust theoretical framework for natural product research, this current analytical paradigm, based on a single data source and a single pathway, faces significant challenges in terms of objectivity, robustness, and traceability when dealing with highly complex and heterogeneous chemical matrices like fulvic acid. Therefore, this field urgently needs a new methodology that can guarantee data quality from the source, achieve multidimensional independent validation along the analytical pathway, and establish rigorous convergence criteria at the conclusion level. The aim is to elevate network pharmacology from a tool for "generating reasonable inferences" to a platform for "deriving reliable conclusions." Summary of the Invention

[0012] This invention aims to address the technical problems of high false positive rates and insufficient reliability of conclusions in existing network pharmacology methods when dealing with complex natural products such as fulvic acid, due to single path dependence, algorithmic model bias, and improper data integration. To this end, this invention proposes a screening method based on multidimensional convergence verification. This method uses systems biology analysis and prior knowledge guidance as two independent analytical paths, and cross-validates the results of both through rigorous convergence criteria. This provides a robust and traceable solution for identifying the core active ingredient and key targets of fulvic acid in promoting angiogenesis.

[0013] To achieve the above objectives, this invention discloses a method for screening the core active ingredients and key targets of fulvic acid in promoting angiogenesis. The technical solution includes the following interrelated steps and key points: (1) Constructing a systems biology analysis pathway: Using tools such as ChemDraw and αExtractor, the structural formulas and SMILES codes of fulvic acid chemical components resolved by ultra-high performance liquid chromatography-mass spectrometry were standardized. Subsequently, the structural information of each component was input into multiple databases, including SwissTargetPrediction, SuperPred, and SEA, for target prediction. All predicted target proteins were then mapped and integrated into the Uniprot database, and a complete set of fulvic acid-related targets was constructed after deduplication. Simultaneously, using "angiogenesis" as a keyword, multiple disease gene knowledge bases, including DisGeNET, PharmGKB, GeneCards, and TTD, were mined and integrated, and angiogenesis-related disease targets were obtained after deduplication. Intersection analysis of the two target sets was performed using the Venny 2.1.0 platform to preliminarily obtain intersecting targets, which can be considered as potential targets for fulvic acid's pro-angiogenic effects. Based on this, a protein-protein interaction (PPI) network of potential targets was constructed. In the Cytoscape environment, key nodes in the network were screened out by calculating multi-dimensional topological parameters such as node degree, betweenness centrality, and proximity centrality, forming the first set of key targets.

[0014] (2) Constructing an analytical path guided by prior knowledge: This approach first constructs a network based on the "fulvic acid-component-target" relationship and sorts the compound nodes according to their topological parameters to obtain a candidate core compound set. Subsequently, focusing on the core phenotype of promoting angiogenesis, a broad literature search and evidence verification are conducted on the candidate compounds. The verification is not based on quantitative indicators (such as the number of publications or impact factor), but rather on the qualitative criterion of "the presence or absence of supporting evidence," i.e., searching for experimental reports on whether the compound can promote angiogenesis, endothelial cell proliferation / migration, or lumen function. Through this credibility check, a core compound set supported by prior knowledge is obtained.

[0015] (3) Perform multidimensional convergence verification and molecular-level confirmation: The core of this stage lies in constructing a methodologically independent feedback loop. Specifically, based solely on the aforementioned core compound set corroborated by prior knowledge, target prediction is performed independently again, and the intersection of the prediction results with the aforementioned angiogenesis disease targets is calculated; this constitutes the second key target set. The data input and computational process in this step are completely independent of the first path, ensuring the methodological independence of the evidence chain. Subsequently, a consistency comparison is performed between the first and second key target sets. This comparison is based on pre-defined multidimensional convergence criteria, including whether the target identities overlap, whether their positions in the PPI network are similar, and whether the functional and pathway enrichment results point to the same biological process. When the comparison results meet this consistency pattern, the consistent targets are confirmed as the final core targets, and the corroborated compound set is confirmed as the final core active ingredient. Finally, to provide orthogonal verification at the atomic level, this invention employs molecular docking technology to obtain the three-dimensional structures of the final core active ingredient and key targets from a public database. After standardized pretreatment such as dehydration and hydrogenation, docking software is used to evaluate their binding modes and affinity, and interaction diagrams are used to analyze key binding sites.

[0016] Compared with the prior art, the present invention has the following significant advantages: (1) Methodological robustness and closed-loop calibration: This invention establishes a technical closed loop of "mutual independence - mutual verification - convergence" through two independent methodological paths of "systems biology" and "prior knowledge guidance" and a verification mechanism consisting of "independent reverse prediction and multidimensional convergence criteria". This design fundamentally avoids circular reasoning caused by the repeated use of the same data in different analysis stages, so that the final "consistency" result has solid statistical significance, rather than expected repetition.

[0017] (2) Significantly reduced false positive rate and traceable results: This invention combines quantitative screening of network topology with qualitative evidence from experimental literature. Without setting rigid quantitative thresholds, it introduces real-world biological constraints to correct algorithmic bias while retaining the opportunity to discover effective components that are not yet fully studied. The convergence comparison of two independent paths naturally eliminates inconsistent noise terms, making the final core results exhibit high confidence and low noise characteristics. Moreover, each conclusion node has a complete traceable chain from components and targets to literature evidence.

[0018] (3) Full-chain evidence support and high translational value: Starting from the material basis of UPLC-MS / MS, this invention connects multi-platform target prediction, multi-database disease gene integration, network topology analysis, literature evidence verification, independent reverse prediction, and molecular docking verification to construct a complete evidence chain from substance to target to pathway and phenotype. The core components and targets identified by this strategy have both network hub attributes and molecular binding feasibility, providing clear and reliable translational basis for subsequent in vivo and in vitro functional verification, targeted drug development, and the establishment of an evaluation system based on quality markers. Attached Figure Description

[0019] Figure 1 This is the total ion chromatogram (TIC) obtained from the analysis of the fulvic acid sample in positive ion mode by ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS / MS).

[0020] Figure 2 This is the total ion current chromatogram (TIC) of the same fulvic acid sample in negative ion mode by UPLC-MS / MS analysis in this embodiment.

[0021] Figure 3 This is the visualization result obtained by processing the set of fulvic acid-related targets and the set of angiogenesis-related disease targets based on Venn diagram analysis in this embodiment.

[0022] Figure 4 This is the visualization result obtained by performing Venn diagram analysis on the targets of the core compound set supported by prior knowledge and the targets of angiogenesis-related diseases in this embodiment.

[0023] Figure 5 This is a schematic diagram of the molecular docking binding pattern between the core compound malonic acid and the key target NR4A1. Detailed Implementation

[0024] To make the technical solution, objective, and beneficial effects of the present invention clearer, the present invention will now be described in detail with reference to a specific embodiment. It should be understood that the embodiment described herein is for illustrative purposes only and is not intended to limit its scope of protection. Those skilled in the art can make various modifications and variations to the present invention without departing from its spirit and principles, and all such modifications and variations should fall within the scope of protection of the present invention. Example

[0025] This embodiment discloses a method for screening core substances and targets of fulvic acid in promoting angiogenesis based on multidimensional convergence verification. The core technical concept of this method lies in constructing two independent chains of evidence: "systems biology analysis" and "prior knowledge-guided analysis," which are then cross-verified through a rigorous "multidimensional convergence verification" mechanism, thus forming a complete technical closed loop. This process aims to fundamentally overcome the inherent technical bias and false positive risk of a single analytical path, ensuring that the ultimately screened core active ingredients and key targets possess a high degree of objectivity, reliability, and robustness.

[0026] (1) Constructing a systems biology analysis pathway: This approach serves as the starting point for the entire technical solution. Its goal is to identify candidate targets that may play a key pivotal role in the angiogenesis process of fulvic acid through a top-down analysis process centered on the target interaction network.

[0027] First, a high-precision chemical composition analysis was performed on the fulvic acid sample. 25.0 mg of fulvic acid sample (origin: Eshan, Yunnan) was accurately weighed and placed in a 1.5 mL centrifuge tube. Two 5 mm diameter homogenizing beads and 500 μL of extraction buffer (methanol:acetonitrile:water = 2:2:1, V / V / V) were added to the tube. The mixture was homogenized at 35 Hz for 4 minutes using a high-throughput tissue homogenizer at 4°C, followed by ultrasonic extraction in an ice-water bath for 5 minutes. This homogenization-ultrasonic extraction cycle was repeated three times. After extraction, the sample tube was incubated at -40°C for 1 hour. 300 μL of the supernatant was collected and filtered through a positive pressure filtration device using a 0.22 μm filter membrane. The filtrate was collected for later use.

[0028] Chromatographic separation was performed on a Vanquish UPLC system (Thermo Fisher Scientific) using a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm). Mobile phase A was an aqueous solution containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and mobile phase B was acetonitrile. Mass spectrometry was performed on an Orbitrap Exploris 120 high-resolution mass spectrometer. The ion source spray voltage was +3.8 kV in positive ion mode and -3.4 kV in negative ion mode. The capillary temperature was 320 °C, and the sheath gas and auxiliary gas flow rates were 50 and 15 Arb, respectively. The first-stage mass spectrometry full scan resolution was 60,000, with a scan range of m / z 100–1500; the second-stage mass spectrometry data-dependent scan resolution was 15,000, using stepwise normalized collision energies of 20, 30, and 40 eV. Figure 1 and Figure 2As shown, the total ion chromatograms in both positive and negative ion modes exhibit symmetrical peak shapes and good separation, demonstrating the effectiveness of this analytical method.

[0029] After the raw data was converted using ProteoWizard, peak extraction, alignment, and compound identification were performed using R scripts in conjunction with the BiotreeDB database. The identification criterion was an exact mass deviation of less than 10 ppm. Through this process, a total of 229 compounds were identified from the fulvic acid samples in this embodiment, covering various chemical categories such as organic acids, amino acids, and fatty acids. Detailed information on the 10 ultimately confirmed core active ingredients is shown in Table 1.

[0030] Table 1. UPLC-MS / MS identification information of 10 core active components in fulvic acid

[0031] Having clarified the material basis, this embodiment constructs a comprehensive map of potential targets for fulvic acid. SMILES codes for 229 compounds were obtained using ChemDraw and αExtractor tools, and target prediction was performed using the SwissTargetPrediction, SuperPred, and SEA databases. All predicted proteins were mapped to their gene names in the Uniprot database, and after integration and deduplication, a total of 1692 fulvic acid-related targets were obtained.

[0032] Meanwhile, using "angiogenesis" as the keyword, a systematic search was conducted in the DisGeNET, PharmGKB, GeneCards, and TTD databases. After integration and deduplication, 115 angiogenesis-related disease targets were obtained. The 1692 fulvic acid targets and 115 disease targets were imported into the Venny 2.1.0 platform for Venn diagram analysis, as shown below. Figure 3 As shown, a total of 26 overlapping target sites were obtained, which constitute the set of potential targets for fulvic acid to promote angiogenesis.

[0033] To identify the network core from these 26 potential targets, this embodiment constructs a protein-protein interaction (PPI) network in the STRING database and uses Cytoscape 3.9.1 software and its CytoNCA plugin to calculate the node degree (DC), betweenness centrality (BC), and proximity centrality (CC) of each node. Considering all three topological parameters, the five targets with the highest rankings in all parameters of the PPI network are selected, forming the first key target set (PTGS2, HIF1A, NR4A1, FPR2, EGFR), providing precise "target guidance" for subsequent validation.

[0034] (2) Constructing an analytical path guided by prior knowledge: This approach aims to screen for candidates with clear biological activity at the compound level through a bottom-up, compound-centric analytical process that incorporates independent literature corroboration.

[0035] This embodiment first constructs a ternary interaction network of "fulvic acid-component-target" based on the aforementioned 229 compounds and 26 potential targets. Subsequently, the compounds in the network are sorted in descending order of node degree, and the top-ranked compounds are selected to form a candidate core compound set.

[0036] To overcome the risk of false positives from purely computational predictions, this embodiment rigorously validated the candidate compound set with prior knowledge. The validation process was conducted in mainstream scientific databases such as PubMed, Web of Science, Scopus, and CNKI, using a search strategy focused on compound names and keywords such as "angiogenesis" and "tube formation." The evaluation criterion adopted a qualitative criterion of "the presence or absence of supporting evidence," requiring at least one independently published, peer-reviewed article reporting that the compound exhibited a statistically significant positive regulatory effect on key angiogenesis indicators (such as total tube length, number of branch points, and microvessel density) in a standard experimental model.

[0037] After rigorous literature review of all candidate core compounds, the 10 compounds listed in Table 1 (lactic acid, butyric acid, stearic acid, malic acid, glycolic acid, malonic acid, glyceric acid, lysine, citric acid, and glutamic acid) were confirmed as the core compound set supported by prior knowledge, with sufficient experimental evidence. This result highlights the effectiveness of the "prior knowledge-guided" approach of this invention, successfully screening core substances with real biological activity from a vast compound library.

[0038] (3) Perform multidimensional convergence verification and molecular-level confirmation: This stage is the "quality certification" step of the technical solution of this invention. Its core lies in the mutual verification of the results of two completely independent analytical paths to achieve the final confirmation of the core substance and the target, and to provide the final binding evidence at the atomic level.

[0039] To ensure methodological independence, this embodiment independently predicts targets again based solely on the aforementioned set of 10 validated core compounds. The intersection of these independently predicted 347 targets with the aforementioned 115 angiogenesis disease targets is then calculated, as follows: Figure 4 As shown, a second set of key targets (PTGS2, HIF1A, NR4A1, FPR2) containing four intersecting targets is obtained. The data input and calculation process in this step are completely independent of the first path, ensuring the methodological independence of the chain of evidence.

[0040] Subsequently, this embodiment performed a multidimensional convergence analysis on the first and second key target sets. This comparison was based on preset multidimensional convergence criteria, including "whether the target identities overlap," "whether their positions in the PPI network are similar," and "whether the functional and pathway enrichment results point to the same biological process." The analysis results showed perfect convergence: the two completely independent analysis paths ultimately yielded identical results for the first four target identities. Based on this perfect convergence, this embodiment ultimately confirmed PTGS2, HIF1A, NR4A1, and FPR2 as the four final core targets, and also identified 10 corroborated compounds as the final core active ingredients.

[0041] Finally, to provide orthogonal verification at the atomic level, this embodiment employs molecular docking technology. Small molecule structures were obtained from PubChem, and high-resolution crystal structures of the target protein were obtained from the RCSB PDB database. The target protein underwent normalization pretreatment using PyMOL software, including removal of water molecules, removal of the original ligand, and hydrogenation. Semi-flexible molecular docking was performed using AutoDock Vina 1.2.0 software.

[0042] The docking results are detailed in Table 2. Analysis shows that 72.5% (29 / 40) of the docking combinations had binding free energies less than -4.25 kcal / mol, with 45% (18 / 40) less than -5.0 kcal / mol, indicating that these small molecules generally possess good spontaneous binding capabilities to the target sites. Some combinations, such as malonic acid with NR4A1, have binding free energies as low as -6.74 kcal / mol, while stearic acid with NR4A1 and PTGS2 both have binding free energies below -6.3 kcal / mol. These results strongly validate, at the atomic level, the high affinity between the core substances screened in this invention and the target sites, forming the final chain of evidence for the entire technical solution.

[0043] In summary, this embodiment successfully screened 10 core active ingredients and 4 core targets from fulvic acid using a complete technical solution that includes systems biology analysis, prior knowledge-guided analysis, multidimensional convergence verification, and molecular docking verification. All results have undergone repeated verification at multiple levels, including chemical analysis, network pharmacology, literature corroboration, and molecular docking, forming a complete, rigorous, and reliable chain of evidence. This provides a novel and powerful technical approach for the study of the pharmacodynamic material basis of natural products.

[0044] Table 2. Molecular docking binding free energy (kcal / mol) between the final core active ingredient and key target

Claims

1. A method for screening key targets of the pro-angiogenic core active ingredients of fulvic acid, characterized in that, Comprising the following steps: a) System biology analysis pathway: performing ultra-performance liquid chromatography-high resolution mass spectrometry analysis on the fulvic acid sample, based on the structural information of the obtained chemical components, synergistically using SwissTargetPrediction, SuperPred and SEA databases to predict target points, and intersecting with disease target points obtained according to the "Angiogenesis" theme by integrating DisGeNET, PharmGKB, GeneCards and TTD databases to form a potential action target point set; Building a protein-protein interaction network around the potential action target point set in the STRING database, using Cytoscape software and its topological analysis plug-in, and screening to obtain a first key target point set according to the node degree, betweenness centrality and closeness centrality indicators; b) Prior knowledge guided analysis pathway: constructing a "fulvic acid-component-target" network, sorting according to the network topological parameters of the compound nodes to obtain a candidate core compound set; performing independent prior knowledge retrieval and evidence checking on the candidate core compound set to determine whether there is experimental evidence supporting the relationship between the candidate core compound set and angiogenesis, endothelial cell proliferation / migration or lumen formation, and then determining a core compound set supported by prior knowledge; c) Multi-dimensional convergence verification and molecular level confirmation: only based on the core compound set supported by prior knowledge, re-predicting the target points and intersecting with the disease target points to obtain a second key target point set; performing multi-dimensional convergence analysis on the first key target point set and the second key target point set, if they are consistent in terms of target point identity, network hub identity and topological position, then confirming the consistent target points as the final key target points, and confirming the supported compound set as the final core active ingredients; and using molecular docking to verify the binding mode and affinity between the final core active ingredients and the final key target points.

2. The method of claim 1, wherein: in the ultra-performance liquid chromatography-high resolution mass spectrometry analysis, a methanol: acetonitrile: water mixed solution is used to extract the sample, and data acquisition is completed on a Vanquish UPLC system and an Orbitrap Exploris 120 high resolution mass spectrometer; before the target point prediction, the structural formula and SMILES code of the compound are obtained using ChemDraw and aExtractor tools, and the predicted target points are mapped to gene names in the Uniprot database; the databases for prior knowledge retrieval include PubMed, Web of Science, Scopus and China National Knowledge Infrastructure, and the evidence checking standard is that there is at least one independently published peer-reviewed literature reporting that the candidate core compound has a statistically significant positive regulatory effect on the key indicators of angiogenesis; The molecular docking includes: obtaining small molecule three-dimensional structure from PubChem database, obtaining target protein crystal structure from RCSB PDB database, performing water removal, ligand removal and hydrogen addition pretreatment by using PyMOL software, and performing binding energy evaluation by using AutoDock Vina software.

3. The method of claim 1 or 2, wherein: The final core active ingredients include lactic acid, butyric acid, stearic acid, malic acid, glycolic acid, malonic acid, glyceric acid, lysine, citric acid and glutamic acid. The final key target points include PTGS2, HIF1A, NR4A1 and FPR2.

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