Method for evaluating anti-diarrhea component of fulvic acid based on synergistic potential index

Abstract

The invention belongs to the technical field of natural product drug screening, and aims to solve the technical problem that a high-value combination is easily ignored due to difficulty in systematic evaluation of a fulvic acid multi-component synergistic effect due to dependence on a single index. The invention provides an evaluation method. The core of the evaluation method is to construct a synergy potential index S. The index organically integrates three dimensions: compound action breadth and target importance obtained through UPLC-MS / MS and network pharmacology, and molecular action intensity obtained through molecular docking, and quantitative calculation is carried out according to a formula S = alpha * f (Dc) + beta * g (Dt) + gamma * h (Eb). The key innovation is that the weight coefficients alpha, beta and gamma can be properly and dynamically adjusted, so that the evaluation model can adapt to different research and development strategies. When the model is applied to a fulvic acid anti-diarrhea system, the priority of a compound-target combination can be objectively remodeled, a high-value synergistic combination neglected by a traditional method is accurately identified, and a quantitative decision basis is provided for drug research and development.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of natural product drug screening, and specifically relates to a systematic evaluation method combining network pharmacology and molecular docking. The method aims to break the dependence on single indicators in traditional evaluation paradigms and realize scientific evaluation of the synergistic effect of multiple components in complex natural products by constructing a quantitative synergistic potential index. The technical scheme can be widely applied to the research and high-efficiency screening of the pharmacodynamic material basis of multi-component natural products such as fulvic acid in the specific pharmacodynamic direction of anti-diarrhea. BACKGROUND

[0002] Diarrhea is a global public health challenge with a wide etiological spectrum, from acute infection to chronic digestive system disorders. Although existing chemical synthetic drugs have achieved remarkable results in symptom control, their side effects and drug resistance problems have become increasingly prominent, which has effectively prompted the focus of global drug research and development to gradually shift to the field of natural products with higher structural diversity, broader activity spectrum and better safety performance. Under this macro background, fulvic acid, as the component with the smallest molecular weight, the highest biological activity and the best water solubility in humic acid, is becoming a potential lead substance in the intervention strategy for diarrhea due to its scientifically proven multiple biological activities such as anti-inflammatory, antioxidant and regulation of intestinal flora.

[0003] However, the development of the pharmacological effects of fulvic acid faces a fundamental technical bottleneck. The essence is that fulvic acid is not a single compound, but a complex mixture system composed of hundreds or even thousands of small molecules such as phenolic acid, fatty acid, and amino acid. This inherent and inherent complexity constitutes the main obstacle to elucidating its pharmacological material basis and precise mechanism of action. To cope with this challenge, a set of mainstream technical paradigm has gradually formed and solidified in the field. This paradigm skillfully integrates three major technical modules of ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS), network pharmacology and molecular docking, forming a systematic analysis process. Specifically, first, the sample is systematically chemically characterized by UPLC-MS / MS technology to construct its complete 'chemical image'; then, by predicting the action target of the compound and mapping it with the disease target related to diarrhea, a multi-dimensional interaction network of 'component-target-pathway-disease' is constructed to reveal its potential pharmacological mechanism from the perspective of systems biology; finally, molecular docking technology is introduced to quantify the binding energy between key molecules from the perspective of computational chemistry, providing structural biological evidence for the key interactions in the above network. It should be acknowledged that this paradigm has successfully shifted the research perspective from the traditional reductionist model of 'one drug, one target' to the holistic view of'multi-component, multi-target, and multi-pathway', providing a powerful analysis tool for understanding the complex mechanism of natural complex prescriptions, and can be called a major methodological leap in this field.

[0004] Although this integrated paradigm has achieved remarkable success in theory, its inherent and deep-seated limitations have gradually been exposed when it is applied to complex systems such as fulvic acid with strong integrated body regulation, and are mainly reflected in the following two interrelated core aspects: First, the evaluation system has not yet escaped the single evaluation logic of 'key molecules' in nature, and there is a difficult philosophical paradox. In the final screening decision-making link, the paradigm often habitually attributes the overall efficacy to the single or few 'key components' with the lowest binding energy or the strongest predicted activity in the interaction network. This approach conflicts with the core theory of'multi-component synergistic action' advocated by network pharmacology. The macro pharmacological effect of fulvic acid is more likely to be the result of the joint contribution of a large number of components through complex network relationships. Among them, many active'moderate' components may make indispensable cumulative contributions to the overall efficacy through synergistic, complementary, or additive effects between each other. The evaluation method of the existing paradigm is prone to systematically ignore such components with potential synergistic value due to its inherent 'only optimal theory' tendency, resulting in distortion and simplification of the interpretation of the true mechanism of action of the drug.

[0005] Secondly, there is a significant gap between multi-dimensional information output and decision-making model. In the entire research process, at least three dimensions of key data can be stably obtained: the connectivity of compounds (Dc), which represents the "breadth" of the compound in the action network; the connectivity of the target (Dt), which represents the "importance" of the target in the biological regulation network; and the molecular docking binding energy (Eb), which represents the "interaction strength" from the molecular level. These three dimensions jointly define the comprehensive value of the "compound-target" interaction combination from different levels and scales. However, in the actual screening operation, the existing technology often relies too much on and focuses on the single dimension of "binding energy" for subjective evaluation, and there is a serious lack of a comprehensive quantitative model that can scientifically integrate multi-source heterogeneous information such as interaction breadth, target importance, and interaction strength. This single-dimensional decision-making mode is prone to cause one-sidedness of the screening results and lead to critical misjudgments. For example, a compound that acts on a core target of the network but has slightly weak binding energy may be severely underestimated in its potential value in the overall system regulation under this evaluation system.

[0006] In summary, the "key molecule" thinking that is adhered to in the existing technical route, as well as the lack of ability to effectively integrate and quantify multi-dimensional information, together constitute an insurmountable bottleneck in accurately revealing the efficacy material basis of complex systems such as fulvic acid. Therefore, there is an urgent need in the art for a new technical solution that must be able to go beyond the traditional single evaluation mode, effectively integrate multi-source heterogeneous biological and chemical data, and ultimately build an objective, reliable, and quantifiable synergistic potential evaluation model, in order to more scientifically and accurately lock in those research targets with high synergistic value. SUMMARY

[0007] The purpose of the present application is to overcome the technical bottleneck in the prior art that the natural product efficacy material evaluation system relies too much on the "key molecule" thinking and lacks the ability to effectively integrate and quantify multi-dimensional information, thereby providing a fulvic acid anti-diarrhea component evaluation method based on synergistic potential index, a computer system, and a storage medium.

[0008] To achieve the above-mentioned purpose, the present application provides a fulvic acid anti-diarrhea component evaluation method based on synergistic potential index, which is realized by the following steps: a) Systematically identifying the chemical components contained in fulvic acid and predicting the potential action targets corresponding to each of the chemical components, thereby constructing a compound target set; b) Obtaining disease targets related to diarrhea and constructing a disease target set; c) Mapping the compound target set and the disease target set, and taking the intersection of the two to obtain common action targets; d) Based on the interaction relationship between the common acting targets and chemical components, a network pharmacology model is constructed, and compound connectivity Dc of each chemical component is calculated according to the model, wherein the Dc is used to represent the breadth of the action of the compound; e) Protein-protein interaction network analysis is performed on the common acting targets, and target connectivity Dt of each target is calculated, wherein the Dt is used to represent the importance of the target in the biological network; f) Molecular docking technology is adopted to calculate the binding energy Eb between each chemical component and each common acting target, wherein the Eb is used to represent the interaction strength between molecules; g) For each combination of the chemical component and the common acting target, the synergistic value is quantified by a synergistic potential index S, wherein the index S is calculated according to the following formula: S = α×f(Dc)+β×g(Dt)+γ×h(|Eb|) Wherein, f, g and h are conversion functions for normalizing Dc, Dt and |Eb| respectively; α, β and γ are preset weight coefficients; |Eb| is the absolute value of Eb; h) According to the calculated synergistic potential index S, all chemical component-common acting target combinations are sorted, so as to realize the quantitative evaluation of the synergistic potential of the anti-diarrhea component.

[0009] As a preferred scheme of the present application, in step a), the chemical components are identified by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology; and the prediction of the potential acting targets integrates information from SwissTargetPrediction, SuperPred and SEA databases. In step b), the disease targets are obtained by integrating information from DisGeNET, DrugBank, GeneCards and TTD databases.

[0010] As a further optimization of the present application, before step h), the method further comprises steps of: i) performing protein-protein interaction network topology analysis on the common acting targets, and screening core targets according to preset network topology parameters; and j) performing network topology analysis on the chemical components, and screening core compounds according to preset network topology parameters. This step helps to focus on the most critical research targets, and improves the evaluation efficiency and accuracy.

[0011] As a key design of the present application, in step g), the weight coefficients a, b, g are parameters that can be adjusted according to the specific evaluation strategy. Preferably, the value range of the weight coefficients a, b, g is 1 to 5. The lower limit of the range ensures the integrity of the evaluation dimension, while the upper limit provides sufficient space for strategy adjustment, avoiding the neglect of any dimension and preventing the decision bias caused by excessive weight, giving the evaluation model high flexibility and customizability.

[0012] In addition, the present application further provides a computer system for evaluating the anti-diarrhea multi-component synergy of fulvic acid, which comprises a memory and a processor. The memory is used to store computer program instructions, and the processor is used to execute the computer program instructions in the memory to implement the method of any one of the above.

[0013] Meanwhile, the present application also provides a computer readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method of any one of the above can be implemented.

[0014] Compared with the prior art, the present application has the following remarkable beneficial effects: (1) Beyond the "key molecule" thinking, revealing the deep synergy law: the present application introduces the compound connectivity (Dc) and target connectivity (Dt) into the evaluation system, breaking through the limitation of traditional methods relying only on single binding energy (Eb). This method can identify those "high potential combinations" acting on the network core nodes or having broad-spectrum activity but with non-optimal binding energy, revealing the systematic synergy value that traditional methods cannot discover, making the evaluation result more consistent with the overall action characteristics of natural product multi-component and multi-target.

[0015] (2) Realize the scientific integration and quantification of multi-dimensional information: the present application initiates the synergy potential index, a comprehensive quantification model, which effectively integrates the originally isolated multi-dimensional information (action breadth, target importance, action strength) with different dimensions. This model successfully converts the abstract "synergy potential" concept into a specific numerical value that can be calculated and compared, providing precise quantitative navigation for the screening of the complex system of fulvic acid.

[0016] (3) Provide flexible and adjustable strategy-oriented evaluation framework: the weight coefficients (α, β, γ) in the application are designed as adjustable parameters, which give the evaluation framework high flexibility. By adjusting the weight, this method can flexibly adapt to different drug research strategies and scientific hypotheses. By adjusting the α value, the "component synergy theory" strategy can be executed, by adjusting the β value, the "target center theory" strategy can be executed, or by adjusting the γ value, the "molecular intensity theory" strategy can be executed. This strong strategy adaptability and interpretability make this method a dynamic and customizable evaluation platform, rather than a rigid formula, greatly improving its practical value in different research scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The overall technical path flowchart of the evaluation method based on the synergistic potential index provided by the application.

[0018] Figure 2 The Wayne analysis diagram of the yellow humic acid compound prediction target set and the diarrhea related disease target set in the embodiment of the application. Figure 3 The protein-protein interaction (PPI) network topology diagram constructed based on the co-acting target in the embodiment of the application.

[0019] Figure 4 The visualization diagram of the "yellow humic acid-component-target-pathway-disease" multidimensional interaction network constructed in the embodiment of the application.

[0020] Figure 5 The three-dimensional conformation diagram of the molecular docking result of the core compound α-linolenic acid and the core target PTGS2 in the embodiment of the application.

[0021] Figure 6 The bar chart of quantitatively sorting the "compound-target" combination according to the synergistic potential index (S) value in Example 1 of the application. DETAILED DESCRIPTION

[0022] To make the purpose, technical scheme and advantages of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0023] It should be understood that the methods disclosed in this invention can be implemented by a computer program, which can be loaded into a computer system, server, or cloud platform for execution. Therefore, this invention also covers a computer system (as described in claim 6) for evaluating the synergistic effects of multiple components of natural products, comprising a processor and a memory storing a computer program that, when executed by the processor, implements the methods of this invention. This invention also covers a computer-readable storage medium (as described in claim 7) storing a computer program that, when executed by a processor, implements the methods of this invention. Example 1

[0024] Benchmark equilibrium model (α=1, β=1, γ=1): The overall technical process of this embodiment is as follows: Figure 1 As shown, this invention aims to construct a synergistic potential evaluation benchmark that comprehensively considers multiple dimensions of factors, and fully elucidates the entire process of the method described in this invention. This embodiment effectively addresses the technical shortcomings pointed out in the background art, namely, the "single evaluation system" and the "lack of effective integration and quantification of multi-dimensional information."

[0025] Step 1: In-depth characterization of the chemical composition of fulvic acid To achieve a systematic analysis of the small molecule chemical composition of fulvic acid, this embodiment utilizes ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS).

[0026] 1.1 Sample pretreatment: 25 mg of fulvic acid sample was accurately weighed and placed in an EP tube under low-temperature conditions, with two homogenizing beads added to aid mechanical disruption. Then, 500 μL of extraction buffer, a mixture of methanol, acetonitrile, and water in a 2:2:1 volume ratio, was added, along with an isotope-labeled internal standard for subsequent data correction. After vortexing for 30 seconds, the sample was homogenized at 35 Hz for 4 minutes to ensure thorough dispersion of the fulvic acid particles. It was then transferred to an ice-water bath and sonicated for 5 minutes to utilize the cavitation effect of ultrasound to promote component dissolution. This "vortex-homogenization-sonication" cycle was repeated three times to maximize extraction efficiency. After extraction, the EP tube was placed at -40°C for 1 hour to allow large molecules such as proteins to precipitate, reducing matrix effects on subsequent mass spectrometry analysis. Finally, 300 μL of the supernatant was pipetted through a 0.22 μm filter plate and filtered under positive pressure at 6 psi for 180 seconds. The filtrate was collected for analysis.

[0027] 1.2 Chromatographic and Mass Spectrometry Analysis Conditions: Chromatographic conditions: Chromatographic analysis was performed on a Vanquish ultra-high performance liquid chromatograph (UHPLC) from Thermo Fisher Scientific, using a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm), which exhibits excellent retention and separation capabilities for polar compounds. Mobile phase A was an aqueous solution containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and mobile phase B was acetonitrile. The sample pan temperature was maintained at 4 °C to prevent degradation, the injection volume was 2 μL, and a gradient elution program was used to achieve effective separation of the compounds.

[0028] Mass spectrometry conditions: Mass spectrometry detection was performed using an Orbitrap Exploris 120 high-resolution mass spectrometer, controlled by Xcalibur software (version 4.4). Ion source parameters were set as follows: sheath gas flow rate 50 Arb, auxiliary gas flow rate 15 Arb, capillary temperature 320℃. The spray voltages in positive and negative ion modes were +3.8 kV and -3.4 kV, respectively. The acquisition mode was data-dependent acquisition (DDA), with a first-stage full scan resolution of 60,000 (m / z 100-1500) to ensure accurate molecular weight acquisition; the second-stage mass spectrometry resolution was 15,000, and a stepped collision energy (20 / 30 / 40 eV) was used to obtain rich fragment information, which is beneficial for structure resolution.

[0029] 1.3 Data Processing and Results: The raw data were converted to mzXML format using ProteoWizard software (version V3.0.24054), and then preprocessed using the XCMS package in the R environment, including peak extraction and peak alignment. The preprocessed data were compared with the BiotreeDB mass spectrometry database (version V3.0), and compounds were accurately identified by combining precise molecular weight (error < 5 ppm), retention time, and secondary fragment ion information. This process identified a total of 229 chemical components, and the information of representative compounds is summarized in Table 1. These structurally diverse compounds lay a solid material foundation for subsequent target prediction and synergistic effect evaluation.

[0030] Step 2: Network Pharmacology Analysis 2.1 Target Prediction and Disease Target Acquisition: The SMILES structures of the 229 compounds identified in Step 1 were imported into the SwissTargetPrediction, SuperPred, and SEA databases in batches, with the species designated as "Homosapiens". The union of the prediction results from each database yielded 1692 potential targets for fulvic acid, i.e., the compound target set. Simultaneously, searches were conducted in the DisGeNET, DrugBank, GeneCards, and TTD databases using "diarrhea" as the keyword. After merging and deduplication, 1875 diarrhea-related targets were finally obtained, i.e., the disease target set.

[0031] 2.2 Obtaining Intersection Target Points: Venn analysis was performed on 1692 compound targets and 1875 disease targets using the Venny 2.1.0 online platform (e.g., Figure 2 As shown in the figure, taking their intersection yielded 395 common targets of fulvic acid and diarrhea. This set of common targets forms the cornerstone of subsequent network construction and analysis.

[0032] Step 3: Screening of core compounds and core targets This step aims to focus key research objectives from a complex network in order to address the challenges posed by the complexity of natural product components.

[0033] 3.1 Construction and analysis of the "fulvic acid-component-target" network: Interaction information of fulvic acid, 229 compounds, and 395 common targets was imported into Cytoscape 3.9.1 software to construct a "fulvic acid-component-target" interaction network. Based on the network topology parameters and using the CytoNCA plugin for calculation, the 7 compounds with the highest comprehensive scores (which needed to be verified by literature search to have research reports related to antidiarrheal effects) were selected and defined as core compounds.

[0034] 3.2 Construction of Protein-Protein Interaction (PPI) Network and Screening of Core Targets: 395 co-interacting targets were imported into the STRING database, and the species was designated as "Homo sapiens" to construct a protein-protein interaction (PPI) network. Using the CytoNCA plugin, the top 5 targets were selected as core targets based on network centrality parameters. The connectivity parameters between core compounds and core targets are summarized in Table 2.

[0035] Step 4: Molecular docking verification To verify the molecular interaction between the core compound and the core target at the computational level, this embodiment uses AutoDock Vina 1.2.0 software to perform semi-flexible molecular docking. The protein receptor (core target) structure was obtained from the RCSBPDB database, and the ligand (core compound) structure was obtained from the PubChem database and subjected to energy minimization processing using ChemDraw 22.0. The docking results are quantitatively characterized by binding energy (Eb, kcal / mol), and the specific values ​​are shown in Table 3.

[0036] Step 5: Calculation and analysis of the synergistic potential index (S) (benchmark model) In this baseline model, the weighting coefficients are set to α=β=γ=1, meaning that the contributions of the three dimensions—breadth of action, target importance, and intensity of action—are considered equally. This setting falls within the preferred range of 1 to 5, constituting a balanced starting point for evaluation.

[0037] 5.1 Data Normalization: To eliminate the influence of different parameter dimensions on the results, a minimum-maximum normalization method is used to linearly map the values ​​of Dc, Dt, and |Eb| to the interval between 0 and 1. To ensure the rigor of the calculation and the accuracy of the results, the normalization parameter ranges are determined based on the actual data in Tables 2 and 3: Dc_min=14, Dc_max=71; Dt_min=46, Dt_max=82; |Eb|_min=1.52, |Eb|_max=5.23.

[0038] The following uses the S-value of vanillic acid-ALB as an example to present the complete calculation process. Other combinations follow the same principle. The entire calculation process follows three steps: data acquisition → normalization → weighted summation. (1) Define the original parameters required for the calculation: Based on Tables 2 and 3 and the settings of Example 1, the following data were obtained: Weighting coefficients (α, β, γ): Example 1 is the baseline equilibrium model, with α = 1, β = 1, and γ = 1.

[0039] The degree of linkage of the compound “vanillic acid”: Referring to Table 2, the Dc = 71 for the compound “vanillic acid”.

[0040] Target connectivity of “ALB”: Referring to Table 2, the Dt of target “ALB” is 82.

[0041] The binding energy of "vanillic acid-ALB": Referring to Table 3, the binding energy at the intersection of "vanillic acid" and "ALB" is Eb = -3.70 kcal / mol. Using its absolute value in the formula, |Eb| = 3.70.

[0042] The parameters for the normalization range (determined by all data): Dc_min = 14, Dc_max = 71 Dt_min = 46, Dt_max = 82 |Eb|_min = 1.52, |Eb|_max = 5.23 (2) Normalize the calculation of each indicator: We use the minimum-maximum normalization formula: x_norm = (x - x_min) / (x_max - x_min) to map the original data to the interval [0, 1].

[0043] Normalized value of scope (Dc) (Dc_norm): Dc_norm = (71 - 14) / (71 - 14) Dc_norm = 57 / 57 Dc_norm = 1.000 Normalized value of target importance (Dt_norm): Dt_norm = (82 - 46) / (82 - 46) Dt_norm = 36 / 36 Dt_norm = 1.000 The normalized value (|Eb|_norm) of the action intensity (|Eb|): |Eb|_norm = (3.70 - 1.52) / (5.23 - 1.52) |Eb|_norm = 2.18 / 3.71 |Eb|_norm ≈ 0.5876 (3) Composite calculation of the synergistic potential index (S): Substitute the normalized values ​​and weighting coefficients into the synergistic potential index formula: S = α× Dc_norm + β× Dt_norm + γ× |Eb|_norm S = (1×1.000) + (1×1.000) + (1×0.5876) S = 1.000 + 1.000 + 0.5876 S ≈ 2.588 (rounded to three decimal places) 5.2 Result Ranking and Comparative Analysis: Synergistic potential index (S) was calculated for all 35 compound-target combinations, and they were ranked in descending order based on their S values. This ranking result was compared with the traditional ranking method based solely on binding energy, as detailed in Table 4.

[0044] Step 6: Strength Validation and Synergy Explanation The data in Table 4 strongly demonstrates the ability of the method of this invention to reshape the priority of combinations, which is precisely a powerful response to the shortcomings of the background technology in "over-reliance on 'key molecule' thinking" and "single-index decision-making". Specifically, the "vanillic acid-ALB" combination acting on the core target ALB (Dt=82), although its binding energy (-3.70 kcal / mol) is not optimal (ranked 15th), its synergistic potential index (S) surpasses several combinations with better binding energies due to its extremely high compound connectivity (Dc=71) and target importance, jumping to the first place. This discovery reveals the "network value" that traditional methods have overlooked: a moderate-strength interaction acting on a key network node may have a better overall synergistic potential than a strong interaction acting on a peripheral node. By quantifying this network core position, this method identifies "high-value" combinations that may be overlooked under the traditional "strength theory" perspective, proving the effectiveness of multi-dimensional integrated evaluation. Example 2

[0045] Strong "component synergetics" strategy model (α=5, β=1, γ=3): This embodiment aims to simulate a drug development strategy that extremely emphasizes "component synergy," that is, prioritizing the screening of chemical components that possess broad-spectrum activity. This embodiment verifies the flexibility of the "adjustable weights" in the claims by setting weighting coefficients to α=5 (upper limit), β=1 (lower limit), and γ=3 (intermediate value).

[0046] Step 5: Calculation and analysis of the synergistic potential index (S) (strong "component synergism" model) The S-values ​​were recalculated and ranked according to the new weights (α=5, β=1, γ=3), and the results are compared with those of Example 1 in Table 5. All combinations of the "vanillic acid" series with the highest compound connectivity (Dc=71) entered the top five. Among them, the "vanillic acid-HIF1A" combination jumped from 26th in the baseline model to 5th, clearly demonstrating that this method can effectively focus on chemical components with important network positions. Example 3

[0047] Strong "target-centric" strategy model (α=3, β=5, γ=1): This embodiment aims to simulate another extreme strategy, namely the "target-centric approach," which prioritizes combinations that act on core and key targets in the network. This embodiment further verifies the strategy adaptability of the present invention by setting weights α=3 (intermediate value), β=5 (upper limit value), and γ=1 (lower limit value).

[0048] Step 5: Calculation and analysis of the synergistic potential index (S) (strong "target-centric" model) The S-values ​​were recalculated and ranked according to the new weights (α=3, β=5, γ=1), and the results are compared with those of Example 1 in Table 6. Combinations acting on the core target ALB (Dt=82) occupied the top seven positions. Particularly noteworthy was the "citric acid-ALB" combination, which, despite having the weakest binding affinity, saw its ranking soar from 31st in the baseline model to 7th under this strategy. This result precisely achieves the research and development intent of the "target-centric approach," that is, prioritizing the screening of all drug combinations related to the core target.

[0049] Example 4

[0050] Strong "Molecular Strength Theory" Strategy Model (α=1, β=3, γ=5): This embodiment aims to simulate a more refined, traditional "molecular strength" strategy, prioritizing combinations with the strongest intermolecular bonds while still considering other dimensions to a moderate degree. This embodiment verifies the compatibility and optimization capabilities of this method with traditional strategies by setting weights α=1 (lower limit), β=3 (intermediate value), and γ=5 (upper limit).

[0051] Step 5: Calculation and analysis of the synergistic potential index (S) (strong "molecular strength" model) The S-values ​​were recalculated and ranked according to the new weights (α=1, β=3, γ=5), and the results are compared with those of Example 1 in Table 7. The rankings are highly correlated with the traditional ranking by binding energy, but still demonstrate the optimization effect. For example, the "benzoic acid-FOS" combination, ranked 12th in binding energy, has an S-value ranking (13th) that surpasses the "salicylic acid-HIF1A" combination, ranked 11th in binding energy (15th in S-value). This indicates that this method can achieve refined screening of the "best among the best" by leveraging network background information while respecting traditional evaluation methods.

[0052] A comprehensive explanation of the range of values ​​for the weighting coefficients (α, β, γ): Through the system demonstrations of the above embodiments 1-4, it can be clearly demonstrated that the range of values ​​(1 to 5) of the weight coefficients α, β, and γ defined by the present invention is reasonable, effective, and has high strategic value.

[0053] Validity of endpoint values ​​(1 and 5): Examples 2, 3, and 4 demonstrate the system's ability to execute extreme, targeted screening strategies by setting one or more coefficients to the upper limit of 5 and combining them with other coefficients. Example 1 demonstrates the validity of the baseline model by setting all coefficients to the lower limit of 1. The combined use of these endpoint values ​​ensures that the entire scope defined in claim 5 is fully supported.

[0054] The effectiveness of the intermediate value (3): By setting the coefficient to the intermediate value 3 in each embodiment, it is shown that when implementing a main strategy, other dimensions can be balanced to a moderate degree, avoiding the rigidity of the evaluation model and reflecting the fineness and practicality of the internal design in this range.

[0055] Synergy and Creativity in Range: Through combinations such as (1,1,1), (5,1,3), (3,5,1), and (1,3,5), this invention demonstrates that α, β, and γ can be flexibly combined within this numerical range to achieve diverse evaluation strategies ranging from equilibrium to various preferences. As shown in the "Ranking Changes" column of Table 5-7, the ranking results of each strategy have undergone expected, significant, and even disruptive changes (e.g., in Example 3, the "citric acid-ALB" combination's ranking soared from 31st to 7th).

[0056] Precise handling of boundary cases: It is worth noting that for the "citric acid-FOS" combination, since the connectivity (Dt=46) and binding energy (|Eb|=1.52) of its target FOS are both minimum values ​​in the dataset, its normalized Dt_norm and |Eb|_norm values ​​are 0. This mathematically accurately reflects the disadvantage of this combination in both network importance and effect strength. When the evaluation strategy is biased towards these dimensions (as in Examples 3 and 4), its ranking will be significantly affected, resulting in a lower S-value. This precisely demonstrates the accuracy and discriminative power of the evaluation system of this invention, which can effectively identify combinations at the data boundary and avoid the information ambiguity that may result from traditional methods.

[0057] Through comparative analysis of the four embodiments above, the synergistic potential evaluation system constructed by this invention demonstrates unprecedented flexibility and depth, collectively providing complete and sufficient support for claims 4 and 5. It can not only discover new "network value" by integrating multi-dimensional information, but also accurately simulate and serve diverse drug development strategies from "component center" to "target center" to "intensity center" through dynamic adjustment of weights, providing a powerful and customizable quantitative tool for the evaluation and efficient development of synergistic effects of complex natural products.

[0058] Table 1. Identification information of some representative chemical components in fulvic acid

[0059] Table 2 Summary of core compound and core target parameters

[0060] Table 3. Molecular docking binding energy (Eb, kcal / mol) results

[0061] Table 4 Comparison of sorting results in Example 1 (α=β=γ=1) with traditional methods

[0062] Table 5. Comparison of sorting results between Example 2 (α=5, β=1, γ=3) and Example 1

[0063] Table 6 Comparison of sorting results between Example 3 (α=3, β=5, γ=1) and Example 1

[0064] Table 7 Comparison of sorting results between Example 4 (α=1, β=3, γ=5) and Example 1

Claims

1. A method for evaluating an anti-diarrhea component of fulvic acid based on a synergistic potential index, characterized by, The method is realized by the following steps: a) The system identifies the chemical components contained in fulvic acid, and predicts the corresponding potential action targets of each chemical component, thereby constructing a compound target set; b) Obtain disease targets related to diarrhea, and construct a disease target set; c) Map the compound target set and the disease target set, and take the intersection of the two to obtain common action targets; d) Based on the interaction relationship between the common action targets and the chemical components, a network pharmacology model is constructed, and the compound connectivity Dc of each chemical component is calculated, which is used to represent the breadth of compound action; e) Protein-protein interaction network analysis is performed on the common action targets, and the target connectivity Dt of each target is calculated, which is used to represent the importance of the target in the biological network; f) The binding energy Eb between each chemical component and each common action target is calculated by molecular docking technology, which is used to represent the interaction strength between molecules; g) For each combination of the chemical components and the common action targets, the synergistic value is quantified by the synergistic potential index S, which is calculated by the following formula: S = α×f(Dc)+β×g(Dt)+γ×h(|Eb|) Wherein, f, g, h are conversion functions for normalizing Dc, Dt, |Eb| respectively; α, β, γ are preset weight coefficients; |Eb| is the absolute value of Eb; h) According to the calculated synergistic potential index S, all chemical component-common action target combinations are sorted, thereby realizing the quantitative evaluation of the synergistic potential of anti-diarrhea components.

2. The method of claim 1, wherein, In step a), the chemical components are identified by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology; the prediction of the potential action targets integrates the information of SwissTargetPrediction, SuperPred and SEA databases; In step b), the disease targets are obtained by integrating the information of DisGeNET, DrugBank, GeneCards and TTD databases.

3. The method according to claim 1 or 2, characterized in that, Before step h), the following steps are also included: i) Protein-protein interaction network topology analysis is performed on the common action targets, and the core targets are screened according to the preset network topology parameters; j) Network topology analysis is performed on the chemical components, and the core compounds are screened according to the preset network topology parameters.

4. The method of claim 1, wherein, In step g), the weight coefficients α, β, γ are parameters that can be adjusted according to the specific evaluation strategy.

5. The method of claim 4, wherein, The value range of the weight coefficients α, β, γ is 1 to 5.

6. A computer system for evaluating the anti-diarrhea multi-component synergy of fulvic acid, characterized in that, It comprises: a memory for storing computer program instructions; a processor for executing the computer program instructions in the memory to implement the method of any one of claims 1 to 5.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, the steps of the method of any one of claims 1 to 5 are realized.

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