Risk evaluation method and device of compound, electronic equipment and storage medium

Abstract

This application provides a method, apparatus, electronic device, and storage medium for risk assessment of compounds, relating to the field of wastewater treatment technology. The method includes: identifying multiple compounds in a target type of wastewater and identifying first target proteins corresponding to the multiple compounds; wherein the first target protein is a human target protein for the compounds; determining health risk scores corresponding to the multiple compounds based on at least one of a score indicating the degree of influence of the multiple compounds in a pathway network and a score indicating the binding strength between the multiple compounds and their corresponding first target proteins; wherein the pathway network is used to characterize the correspondence between the compounds, the target genes of the first target proteins, and biological pathways, and the pathway network is constructed through enrichment analysis of the target genes. This application can accurately quantify the impact of compounds in wastewater on human health, improving the accuracy of compound risk assessment.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, and in particular to a method, apparatus, electronic device and storage medium for risk assessment of a compound. Background Technology

[0002] Wastewater contains a variety of compounds that may pose potential threats to the environment and human health. Current technologies for risk assessment of compounds in wastewater have limitations. They primarily determine the risk level based on compound concentration, neglecting the potential impact of even low-concentration compounds on human health, leading to insufficient accuracy in risk assessment. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, electronic device, and storage medium for risk assessment of compounds to solve the above-mentioned technical problems.

[0004] Firstly, a risk assessment method for compounds is provided, including: Multiple compounds in target type wastewater are identified, and the first target protein corresponding to the multiple compounds is identified; wherein, the first target protein is the human target protein to which the compounds act; The health risk score corresponding to the multiple compounds is determined based on at least one of the following: the influence score of the multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The pathway network is used to characterize the correspondence between the compound, the target gene of the first target protein, and the biological pathway. The pathway network is constructed by enrichment analysis of the target gene.

[0005] In some embodiments, determining the first target protein corresponding to the plurality of compounds includes: Obtain the molecular structure representation information of the various compounds, and query the second target protein corresponding to the various compounds based on the molecular structure representation information; Query the target database to identify multiple third-target proteins corresponding to the target disease; The first target protein corresponding to the various compounds is determined based on the intersection of the second target proteins corresponding to the various compounds with the various third target proteins.

[0006] In some embodiments, the impact score includes a first impact score, and before determining the health risk scores corresponding to the multiple compounds, the method further includes: The first influence score is determined based on at least one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of the plurality of compounds in the pathway network.

[0007] In some embodiments, the impact score includes a second impact score, and before determining the health risk scores corresponding to the multiple compounds, the method further includes: Based on the pathway network, at least one of the following is determined: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score for the various compounds; The second degree of influence score corresponding to the various compounds is determined based on at least one of the pathway breadth score, target number score, pathway importance score, and gene regulation intensity score. The pathway breadth score is determined based on the total number of biological pathways of the compound, the target gene number score is determined based on the total number of target genes of the compound, the pathway importance score is determined based on the total score of the biological pathways of the compound, and the gene regulation strength score is determined based on the number of times the target genes of the compound are repeated in the pathway network.

[0008] In some embodiments, determining at least one of the following based on the pathway network: pathway breadth score, target gene number score, pathway importance score, and gene regulatory strength score for the various compounds, includes: The weights of each biological pathway in the pathway network are determined, and those with weights greater than a preset threshold are identified as core biological pathways, while those with weights less than the preset threshold are identified as non-core biological pathways. The score of the core biological pathway is determined as a first preset value, and the score of the non-core biological pathway is determined as a second preset value; wherein, the first preset value is greater than the second preset value; The total scores of each core biological pathway and non-core biological pathway of the compound in the pathway network are normalized to obtain the pathway importance score.

[0009] In some embodiments, prior to determining the health risk scores corresponding to the plurality of compounds, the method further includes: Based on the pathway network or protein interaction network, the degree centrality of the first target protein is determined; wherein, the protein interaction network is used to characterize the association relationships between the first target proteins. For each compound, the first target protein with the highest degree centrality is selected as the key target protein. Based on the binding free energy of the various compounds with the corresponding key target proteins, the binding strength scores of the various compounds with the corresponding key target proteins are determined.

[0010] In some embodiments, determining the binding strength score between the plurality of compounds and the corresponding key target proteins based on the binding free energy of the plurality of compounds and the corresponding key target proteins includes: The binding free energies of the various compounds to the corresponding key target proteins are sequentially subjected to absolute value processing and normalization processing to obtain the initial binding strength scores of the compounds to the corresponding key target proteins. The initial binding strength score and the third preset value are summed to obtain the binding strength scores of the various compounds and the corresponding key target proteins; wherein the third preset value is greater than 0.

[0011] Secondly, a risk assessment device for a compound is also provided, comprising: The first determining module is used to determine multiple compounds in the target type of wastewater and to determine the first target protein corresponding to the multiple compounds; wherein, the first target protein is the human target protein that the compounds act on. The second determining module is used to determine the health risk score corresponding to the multiple compounds based on at least one of the influence score of the multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The pathway network is used to characterize the correspondence between the compound, the target gene of the first target protein, and the biological pathway. The pathway network is constructed by enrichment analysis of the target gene.

[0012] In some embodiments, the first determining module is specifically used for: Obtain the molecular structure representation information of the various compounds, and query the second target protein corresponding to the various compounds based on the molecular structure representation information; Query the target database to identify multiple third-target proteins corresponding to the target disease; The first target protein corresponding to the various compounds is determined based on the intersection of the second target proteins corresponding to the various compounds with the various third target proteins.

[0013] In some embodiments, the impact score includes a first impact score, and the apparatus further includes: The third determining module is used to determine the first influence score based on at least one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of the plurality of compounds in the pathway network.

[0014] In some embodiments, the impact score includes a second impact score, and the apparatus further includes: The fourth determining module is used to determine, based on the pathway network, at least one of the following: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score corresponding to the various compounds; The fifth determining module is used to determine the second degree of influence score corresponding to the multiple compounds based on at least one of the pathway breadth score, target number score, pathway importance score, and gene regulation intensity score corresponding to the multiple compounds; The pathway breadth score is determined based on the total number of biological pathways of the compound, the target gene number score is determined based on the total number of target genes of the compound, the pathway importance score is determined based on the total score of the biological pathways of the compound, and the gene regulation strength score is determined based on the number of times the target genes of the compound are repeated in the pathway network.

[0015] In some embodiments, the fourth determining module is specifically used for: The weights of each biological pathway in the pathway network are determined, and those with weights greater than a preset threshold are identified as core biological pathways, while those with weights less than the preset threshold are identified as non-core biological pathways. The score of the core biological pathway is determined as a first preset value, and the score of the non-core biological pathway is determined as a second preset value; wherein, the first preset value is greater than the second preset value; The total scores of each core biological pathway and non-core biological pathway of the compound in the pathway network are normalized to obtain the pathway importance score.

[0016] In some embodiments, prior to determining the health risk scores corresponding to the plurality of compounds, the method further includes: Based on the pathway network or protein interaction network, the degree centrality of the first target protein is determined; wherein, the protein interaction network is used to characterize the association relationships between the first target proteins. For each compound, the first target protein with the highest degree centrality is selected as the key target protein. Based on the binding free energy of the various compounds with the corresponding key target proteins, the binding strength scores of the various compounds with the corresponding key target proteins are determined.

[0017] In some embodiments, determining the binding strength score between the plurality of compounds and the corresponding key target proteins based on the binding free energy of the plurality of compounds and the corresponding key target proteins includes: The binding free energies of the various compounds to the corresponding key target proteins are sequentially subjected to absolute value processing and normalization processing to obtain the initial binding strength scores of the compounds to the corresponding key target proteins. The initial binding strength score and the third preset value are summed to obtain the binding strength scores of the various compounds and the corresponding key target proteins; wherein the third preset value is greater than 0.

[0018] Thirdly, an electronic device is also provided, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the methods described above.

[0019] Fourthly, a computer-readable storage medium is also provided, the computer-readable storage medium storing a computer program that, when executed by at least one processor, implements the method as described above.

[0020] In this embodiment of the application, at least one of the following is used: the influence score of multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The health risk score of the multiple compounds is determined, which can accurately quantify the impact of compounds in wastewater on human health and improve the accuracy of compound risk assessment. Attached Figure Description

[0021] Figure 1 A flowchart of a risk assessment method for compounds provided in the embodiments of this application; Figure 2 A diagram showing the results of the intersection analysis of target proteins provided in the embodiments of this application; Figure 3 A schematic diagram of a protein interaction network provided in an embodiment of this application; Figure 4 A schematic diagram of the pathway network provided in the embodiments of this application; Figure 5 A schematic diagram illustrating the risk rating results of the compounds provided in the embodiments of this application; Figure 6 A structural diagram of the risk assessment device for compounds provided in the embodiments of this application; Figure 7 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0023] The terms "first" and "second" in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. The term "multiple" in this application can mean at least two, for example, two, three, or more, and the embodiments of this application do not impose limitations.

[0024] This application provides a method, apparatus, electronic device, and storage medium for risk assessment of compounds, in order to improve the accuracy of compound risk assessment.

[0025] The risk assessment method for the compounds in the embodiments of this application will be described in detail below.

[0026] See Figure 1 , Figure 1 This is a flowchart of a risk assessment method for a compound provided in this application, such as... Figure 1 As shown, the method includes: Step 101: Identify multiple compounds in the target type of wastewater and determine the first target protein corresponding to each compound.

[0027] Gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) can be used to analyze the target type of wastewater samples, identify the organic compound components, and thus determine the aforementioned compounds. As an example, the target type of wastewater could be the tailwater from a type of Maotai-flavor liquor.

[0028] To improve the accuracy of compound identification, multi-source literature searches can be conducted in authoritative databases such as Elsevier and PubMed using combined keywords to collect characteristic information of organic compounds related to the target type of wastewater. After data cleaning and standardization, and correlation with actual sample detection results, compounds that are clearly present in the target type of wastewater, newly discovered compounds, and organic substances with potential environmental risks are screened out, resulting in the aforementioned variety of compounds.

[0029] After obtaining the aforementioned compounds, a wastewater-compound data table can be constructed to record relevant information about the compounds. This includes, for example, the compound's full two-dimensional retention time, peak area, and area percentage, as well as its chemical name, molecular weight, Chemical Abstracts Service (CAS) number, and chemical formula.

[0030] After identifying the aforementioned compounds, the primary target protein for each compound can be predicted using methods such as database searches. The primary target protein is the human target protein that the aforementioned compounds can act upon; it can also be called the target protein. The number of primary target proteins corresponding to each compound may be multiple or single.

[0031] Step 102: Determine the health risk scores corresponding to multiple compounds based on at least one of the following: the score of the degree of influence of multiple compounds in the pathway network and the score of the binding strength of multiple compounds to the corresponding first target protein.

[0032] The pathway network described above, used to determine the degree of influence score, is used to characterize the correspondence between compounds, target genes of the first target protein, and biological pathways. It was constructed through enrichment analysis of target genes. The construction of the pathway network is explained in detail below.

[0033] In this embodiment, the target genes of the first target protein can be queried through a relevant platform (e.g., "WeBio"). Then, enrichment analysis of the target genes of the first target protein is performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG). KEGG-based enrichment analysis identifies biological pathways with significantly enriched target genes by comparing the distribution of target genes in the KEGG database and performing statistical tests. After enrichment analysis of the target genes of the first target protein, the aforementioned pathway network can be constructed to characterize the correspondence between compounds, target genes of the first target protein, and biological pathways. One target protein may correspond to multiple target genes.

[0034] It is readily understood that the compounds referred to in the embodiments of this application are compounds found in target type wastewater, which pose potential risks. The higher the degree of influence of the aforementioned compounds with potential risks in the aforementioned pathway network, the higher the risk of impact on human health. Based on this, the embodiments of this application can determine the degree of influence score of a compound in the pathway network (the higher the degree of influence, the higher the degree of influence score), and determine the corresponding health risk score of the compound based on the degree of influence score.

[0035] In this embodiment, the aforementioned health risk score can also be determined based on the binding strength scores of various compounds with their corresponding first target proteins. It is readily understood that the higher the binding strength of a compound with its first target protein, the higher the risk of impact on human health. Based on this, this embodiment demonstrates that a health risk score corresponding to a compound can be determined based on the aforementioned binding strength score (the higher the degree of influence, the higher the influence score).

[0036] It is worth noting that the health risk score in this application embodiment can be determined based on either the impact degree score or the combination strength score. To further improve the accuracy of the health risk score, the impact degree score and the combination strength score can also be weighted to obtain the aforementioned health risk score.

[0037] In this embodiment of the application, at least one of the following is used: the influence degree score of the above-mentioned multiple compounds in the pathway network and the binding strength score of multiple compounds to the corresponding first target protein. The health risk score of multiple compounds can be determined, which can accurately quantify the impact of compounds in wastewater on human health and improve the accuracy of compound risk assessment.

[0038] In some embodiments, in order to evaluate the potential risk of a target type of wastewater to a specific target disease, step 101 above includes: Obtain molecular structure representation information of multiple compounds, and query the second target protein corresponding to multiple compounds based on the molecular structure representation information; Query the target database to identify multiple third-target proteins corresponding to the target disease; Based on the intersection of the second target proteins corresponding to various compounds with multiple third target proteins, the first target proteins corresponding to various compounds are determined.

[0039] In this embodiment, molecular structure representation information for multiple compounds can be obtained based on their CAS numbers. For example, the SMILES molecular identifier can be retrieved from the PubChem database based on the CAS number.

[0040] After obtaining the above molecular structure representation information, it can be input into relevant platforms and databases for target prediction. The UniProt database is used to standardize the predicted target protein names, and potential targets with a probability value greater than 0 are screened as the second target proteins of the compound.

[0041] The aforementioned target database is a disease target database. Multiple third-target proteins corresponding to the aforementioned target diseases can be retrieved through this database.

[0042] By taking the intersection of the second and third target proteins, the common target protein can be obtained.

[0043] In some embodiments, the common target protein corresponding to the above-mentioned multiple compounds can be directly identified as the first target protein corresponding to the multiple compounds.

[0044] In other embodiments, to further improve the accuracy and criticality of the first target protein, common target proteins can be further screened to obtain the aforementioned first target protein, specifically: Based on the aforementioned common target proteins, a protein-protein interaction (PPI) network is constructed by setting a fourth preset confidence threshold and hiding isolated common target proteins. The proteins in the PPI network are the aforementioned first target proteins.

[0045] Specifically, the PPI network can be constructed based on the STRING database. The confidence threshold is strictly set to the fourth preset value, and isolated nodes in the network are hidden, thereby eliminating common target proteins with insufficient confidence and isolated existence, and finally obtaining a PPI network composed only of key first target proteins.

[0046] In some embodiments, the impact assessment includes a first impact assessment, and before determining the health risk scores for multiple compounds, the method further includes: The first degree of influence score is determined based on at least one of the following: degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of multiple compounds in the pathway network.

[0047] In this implementation, the first influence score can be determined based on any one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficients. Alternatively, a weighted average of multiple factors, including degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficients, can be used to determine the first influence score.

[0048] When determining the score of the first degree of influence based on multiple weights, normalization can be performed before weighting to eliminate dimensional differences. Normalization methods include range standardization or maximum standardization.

[0049] To make it clearer, the following example provides a formula for determining the first degree of influence score.

[0050] ; In the formula, The score represents the degree of influence of compound i. Indicates the degree centrality of compound i. This represents the maximum degree centrality of each compound in the pathway network. Degree centrality reflects the breadth of connections between compound nodes in the pathway network. This represents the neighborhood connectivity (NC) of compound i. This represents the maximum NC value of each compound in the pathway network. The NC value can characterize the network control capability. The clustering coefficient (CC) represents compound i. This represents the maximum CC value of each compound in the pathway network. The CC value can indicate modularity. , ,as well as These are the weights of each item, and the sum of the three equals 1. This can be set. Greater than as well as ,For example It is 0.5. It is 0.3. The value is 0.2, which is based on the fundamental role of prominence centrality, while also taking into account other topological properties.

[0051] In this embodiment, a first influence score is determined based on at least one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of multiple compounds in the pathway network. This can accurately quantify the influence of compounds in the pathway network and improve the accuracy of the first influence score.

[0052] In some embodiments, the impact assessment includes a second impact assessment, and before determining the health risk scores for multiple compounds, the method further includes: Based on pathway networks, determine at least one of the following for various compounds: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score; The second degree of influence score for each compound is determined based on at least one of the following: pathway breadth score, target number score, pathway importance score, and gene regulation intensity score.

[0053] Among them, the pathway breadth score is based on the total number of biological pathways of the compound, the target gene number score is based on the total number of target genes of the compound, the pathway importance score is based on the total score of the biological pathway of the compound, and the gene regulation strength score is based on the number of times the target gene of the compound is repeated in the pathway network.

[0054] In this implementation, the second degree of influence score can be determined based on any one of the aforementioned pathway breadth score, target number score, pathway importance score, and gene regulation intensity score. Alternatively, multiple factors among the aforementioned pathway breadth score, target number score, pathway importance score, and gene regulation intensity score can be weighted to determine the second degree of influence score.

[0055] In the above embodiments, when determining the second degree of influence score based on multiple weights, normalization can be performed before weighting to eliminate dimensional differences. Normalization, for example, is range standardization or maximum standardization.

[0056] To make it clearer, the following example provides a formula for determining the second degree of influence score.

[0057] ; In the formula, The score represents the degree of influence of compound i. This represents the number of biological pathways associated with compound i. Represents the compounds in the pathway network. The maximum value, That is, the above-mentioned pathway breadth score. This represents the total number of target genes for which compound i acts in the pathway network. Represents the compounds in the pathway network. The maximum value, This refers to the score based on the number of target genes mentioned above. The scores representing the various biological pathways of compound i. Represents the compounds in the pathway network. The maximum value, This refers to the pathway importance score. This represents the total number of repetitions of the target gene of compound i in multiple pathways. Represents the compounds in the pathway network. The maximum value, That is, the gene regulation intensity score. , , as well as These are the weights for each item, and the sum of the four equals 1. This can be set. Greater than , as well as ,For example It is 0.4. 0.3 0.2 and The value is 0.1. The pathway breadth score and pathway importance score can reflect the scope of action of compounds, the pathway importance score can highlight the importance of key biological pathways, and the gene regulation intensity score can reflect the intensity of multi-pathway regulation.

[0058] In this embodiment, a second influence score is determined based on at least one of the pathway breadth score, target gene number score, pathway importance score, and gene regulation intensity score corresponding to multiple compounds. This can further accurately quantify the influence of compounds in the above-mentioned pathway network and improve the accuracy of the second influence score.

[0059] In some embodiments, based on pathway networks, at least one of the following is determined: pathway breadth score, target gene number score, pathway importance score, and gene regulatory strength score for multiple compounds, including: The weights of each biological pathway in the pathway network are determined, and those with weights greater than a preset threshold are identified as core biological pathways, while those with weights less than a preset threshold are identified as non-core biological pathways. The scores of core biological pathways are determined as a first preset value, and the scores of non-core biological pathways are determined as a second preset value; wherein, the first preset value is greater than the second preset value. The total scores of each core biological pathway and non-core biological pathway in the pathway network were normalized to obtain the pathway importance score.

[0060] The first preset value is greater than 0. For example, the first preset value is 2 and the second preset value is 1, or the first preset value is 1 and the second preset value is 0.

[0061] In this embodiment, core biological pathways and non-core biological pathways are identified. The scores of core biological pathways are directly set to a first preset value and the scores of non-core biological pathways are set to a second preset value. This can effectively increase the score difference and improve the discrimination and recognition of pathway importance scores.

[0062] The following provides an example of how to assign weights to biological pathways.

[0063] When performing enrichment analysis on the Metascape platform, key parameters are determined, such as the p-value determined by a method based on hypergeometric test combined with Benjamini-Hochberg correction, as well as the enrichment factor, betweenness centrality, or overlap with the aforementioned PPI network modules in the STRING database ("overlap" refers to the degree of gene intersection between gene sets in a biological pathway and PPI network modules).

[0064] In some embodiments, the weights can be determined directly based on the -p-value described above. Specifically, the weights can be equal to -log10(p-value).

[0065] In other embodiments, the above weights can be determined based on (-log10(p-value)×enrichment factor).

[0066] In other embodiments, the above weights can be calculated using a combination of more parameters. For example, the logarithmic transformation of the corrected q-value can be used as the base weight (60%), combined with path topology analysis from KEGG Mapper (based on gene betweenness centrality value, accounting for 30%) and overlap with PPI network modules in the STRING database (accounting for 10%) to calculate the above weights.

[0067] In some embodiments, before determining the health risk scores corresponding to multiple compounds, the method further includes: The degree centrality of the first target protein is determined based on pathway networks or protein interaction networks; wherein, the protein interaction network is used to characterize the association relationships between the first target proteins. For each compound, the first target protein with the highest degree of centrality is selected as the key target protein. Based on the binding free energy of various compounds with their corresponding key target proteins, the binding strength scores of various compounds with their corresponding key target proteins were determined.

[0068] In this embodiment, the pathway network is used to establish the correspondence between compounds, target genes of the first target protein, and biological pathways. The degree centrality of the first target protein determined based on the pathway network can be the sum of the degree centralities of the target genes of the first target protein. Alternatively, the degree centrality of each first target protein can be determined directly based on the aforementioned PPI network.

[0069] In this embodiment, the binding strength scores of various compounds and their corresponding key target proteins are determined based on the binding free energy of these compounds. This helps to ensure the representativeness of the binding strength scores while saving computational resources.

[0070] In some embodiments, the binding strength score between multiple compounds and their corresponding key target proteins is determined based on the binding free energy of these compounds and their corresponding key target proteins, including: The binding free energies of various compounds to their corresponding key target proteins were normalized to obtain the initial binding strength scores of the compounds to their corresponding key target proteins. The initial binding strength score and the third preset value were summed to obtain the binding strength scores of various compounds and their corresponding key target proteins; the third preset value is greater than 0.

[0071] In this embodiment of the application, by setting a third preset value as the base score, the observability of the combined strength score is enhanced, and extreme value interference can be effectively buffered.

[0072] As an example, the formula combining the intensity score can be as follows: ; In the formula, This indicates a combination of intensity scores. This represents the binding free energy of compound i to the corresponding key target protein. This represents the maximum absolute value of the binding free energy of each compound. This represents the minimum absolute value of the binding free energy of each compound. This represents the third preset value mentioned above. As an example, the above... It can be equal to 50.

[0073] By applying the above formula, the binding free energies of various compounds and their corresponding key target proteins are sequentially processed by taking the absolute value and then standardizing the range. Multiplying this by 100 and adding a base score of 50 converts the binding free energy into a binding strength score of 0-150 points. This score is then normalized to a standardized score of 0-100 points, offering multiple advantages. Specifically, taking the absolute value and standardizing the range (min-max) preserves the physical essence of the binding energy reflecting the strength of the interaction while eliminating dimensional differences. Furthermore, the 50-point base score enhances the observability of the scoring system and effectively buffers the interference of extreme values. Finally, the percentage-based scoring not only facilitates cross-platform data comparison but also allows for direct integration with other evaluation indicators. This processing maintains the physical meaning of the original data while significantly improving the comparability and interpretability of data from different studies.

[0074] In some embodiments, the health risk score can be determined by combining the first impact score (CTS), the second impact score (PAS), and the combined intensity score (BAS) to improve the accuracy of health risk score determination. As an example, it can be determined using the following formula: ; In the formula, That is, the health risk score of compound i. , and For each weight, , and The sum is 1. As an example, it can be set to 1. , It is 0.3. It is 0.4.

[0075] In some embodiments, after determining the above health risk scores, the health risks of each compound can be classified, for example, a TRS score of [0, 40) is considered low risk, a TRS score of [40, 65) is considered medium risk, and a TRS score greater than or equal to 65 is considered high risk.

[0076] To clearly understand the technical solutions of the embodiments of this application, a specific embodiment is provided below for illustration.

[0077] Taking the wastewater from a Maotai-flavor liquor production enterprise as the target type of wastewater as an example, the steps for risk assessment of compounds are as follows: 1. Sample Collection and Processing: Wastewater, water samples from different treatment stages of the wastewater treatment facility, treated effluent, river water in its natural state, and water samples from a mixture of effluent and river water were collected. Each sample had a volume of approximately 4 liters, and three parallel samples were taken at each sampling point. The n-hexane extraction method was used for sample processing.

[0078] 2. Two-dimensional gas chromatography-mass spectrometry analysis: Gas chromatography conditions included a DB-5MS capillary column (30m × 0.25mm × 0.25μm, film thickness), helium as carrier gas (purity ≥ 99.999%), injection port temperature 250℃, interface temperature 280℃, and a temperature program of 80℃ for 3 min, increasing to 250℃ at 20℃ / min and holding for 20 min. Mass spectrometry conditions included an ion source temperature of 250℃, a quadrupole temperature of 150℃, an EI source voltage of 70 eV, an injection volume of 1 μL, a flow rate of 1 mL / min, and a full scan. Sixty-nine potentially hazardous organic compounds were identified in the liquor tail water sample.

[0079] 3. Constructing a database of potential hazardous substances: Multi-source literature searches were conducted in authoritative databases such as Elsevier and PubMed using combined keywords to collect characteristic information of organic compounds related to the production of Maotai-flavor liquor. After data cleaning and standardization, and correlation with actual sample test results, compounds already known to exist in the wastewater of Maotai-flavor liquor, newly discovered compounds, and organic substances with potential environmental risks were screened out. A data table of organic compounds in the wastewater of Maotai-flavor liquor was constructed, as shown in Table 1.

[0080] Table 1. Data on Organic Compounds (Partial) 4. Network toxicology analysis 4.1 Identification of Potential Organic Compound Targets: Using the 82 compounds listed in Table 1 as research objects, standardized SMILES molecular identifiers were obtained from the PubChem database based on the CAS number of each compound. These identifiers were then input into relevant platforms and databases for target protein prediction. The names of the predicted target proteins were standardized using the UniProt database, and potential targets with a probability value greater than 0 were screened as secondary target proteins of the compounds. A total of 426 related secondary target proteins were obtained (see Table 2). These target compounds mainly involve neurotransmitter receptors (such as acetylcholine receptors, glutamate receptors, etc.), metabolic enzymes (such as AChE, monoamine oxidase, etc.), and inflammation-related signaling pathway proteins (such as TNF-α, IL-6, etc.). KEGG pathway enrichment analysis revealed that these targets were significantly enriched in pathways closely related to the nervous system, such as the neuroactive ligand-receptor interaction pathway, the calcium signaling pathway, and the Alzheimer's disease pathway.

[0081] Table 2. List of Second Target Proteins (Partial) 4.2 Disease Target Prediction: Taking Alzheimer's disease (AD) as an example, a systematic search was conducted using three authoritative disease target databases: GeneCard, DrugBank, and DisGeNET, with the keyword "Alzheimer's disease." A multi-database cross-validation strategy was employed, identifying 15,513 third-target proteins closely related to AD. The list of these third-target proteins is shown in Table 3.

[0082] Table 3. Partial List of Third Target Proteins 4.3 Target Protein Intersection and PPI Network Construction: The 426 second target proteins and 15,513 AD-related third target proteins obtained in the previous stage were simultaneously input into the Venny platform. Visualization of the Venn diagram revealed 366 common target proteins (see...). Figure 2These target proteins mainly involve targets related to the neurotransmitter system (such as CHRM1, GRIN2B, etc.), targets related to oxidative stress response (such as SOD2, CAT, etc.), targets related to inflammation regulation (such as TNF, IL1B, etc.), and targets related to tau protein phosphorylation (such as GSK3B, MAPT, etc.).

[0083] Based on 366 intersection target points obtained through screening, a high-confidence PPI network was constructed using the STRING database. By strictly setting the confidence threshold to 0.900 and hiding isolated nodes, a PPI network containing 277 key first-target proteins was finally obtained. As an example, the PPI network data can be imported into Cytoscape software for in-depth analysis and visualization. First, the network structure is optimized using a Force-Directed layout algorithm to highlight core modules and key hub nodes. Second, multi-dimensional visual encoding enhances network readability, including making node size proportional to degree value, node color varying according to degree value gradient, and edge thickness reflecting interaction confidence. Simultaneously, the CytoHubba plugin is used to identify key target proteins such as SRC, PIK3CA, and PIK3CD, resulting in a visualized PPI network. The PPI network is shown below. Figure 3 As shown in the figure. This systematic analysis not only visually demonstrates the network topology characteristics of potential targets of organic pollutants, but also reveals the key pathological links of AD that they may interfere with. 5. Enrichment analysis based on KEGG KEGG enrichment analysis was performed on the target genes of 277 first-target proteins using the Metascape database to confirm the enrichment analysis results.

[0084] 6. Construct a pathway network The enrichment analysis results were converted into a data network format and imported into Cytoscape software. Compounds, target genes, and biological pathways with no interaction relationships were screened and removed, thus constructing a pathway network related to AD. Figure 4 An example is shown showing a partial visualized network, which is composed of key nodes in the network. Figure 3 In the pathway network diagram, pink circles represent 16 key components, orange triangles represent 20 key pathways, and purple squares represent related target genes. Based on Figure 4 This study effectively reveals the complex interactions between compounds, metabolic pathways, and their related target genes, further demonstrating the important role of these compounds in the occurrence and development of Alzheimer's disease (AD).

[0085] 7. Calculation of the first degree of influence score Using the pathway network corresponding to "compound-target gene-biological pathway" constructed in the previous stage, three key topological parameters were calculated using the Network Analyzer module in Cytoscap: degree centrality (Degree), neighborhood connectivity (NC value), and clustering coefficient (CC value).

[0086] Then, based on the above formula for calculating the first influence score, the first influence score of each compound is calculated using the combination centrality, neighborhood connectivity, and clustering coefficient. The calculation results are shown in Table 4.

[0087] Table 4. Score of First Influence Level (Partial) 7. Calculation of the second degree of influence score Using the pathway network corresponding to the previously constructed "compound-target gene-biological pathway", the second degree of influence score was calculated using the above-mentioned formula for calculating the second degree of influence. The calculation results are shown in Table 5.

[0088] Table 5. Second Influence Level Rating (Partial) As shown in Table 5, 1,3,5-triallyltriazine trione (score 95) exhibited the strongest multipath interference characteristics.

[0089] 8. Calculate based on intensity score 8.1 Calculations based on free energy Based on the previous PPI network or pathway network, key target proteins corresponding to each compound are screened from the first target proteins.

[0090] Then, using the AutoDockTools 4.0 software platform, molecular docking simulations were systematically performed on 82 compounds and the screened key target proteins (detailed parameters are shown in Table 6). The docking results showed that the binding free energy (ΔG, unit kcal / mol) is an important indicator for evaluating the stability of the complex; the smaller the negative value, the more stable the binding. This binding stability mainly stems from the synergistic contribution of intermolecular forces.

[0091] Table 6. Docking results of compounds with key target proteins (partial) 8.2 Calculation based on intensity score Based on the above docking results, the bonding strength score was calculated according to the bonding strength score calculation formula, and the calculation results are shown in Table 7.

[0092] Table 7 Bond Strength Scoring 9. Calculate health risk score By integrating multidimensional data such as molecular docking, network topology analysis, and pathway enrichment, an AD risk grading system for neurotoxic substances in the tail water of Maotai liquor was established. This system employed a weighted scoring method (combining intensity score (40%), first-degree-of-influence score (30%), and second-degree-of-influence score (30%) to systematically evaluate the potential association between 82 compounds and the pathological progression of AD (Table 8 and...). Figure 5 ).

[0093] Table 8 Health Risk Scores of Compounds This application provides a systematic and comprehensive risk assessment method for organic compounds in water in industrial parks. It comprehensively considers the topological characteristics of compounds (first criticality score), pathway relevance (second criticality score), and binding energy (binding strength score), which can accurately identify potential health hazard substances, comprehensively assess the potential risks of compounds, and classify them, providing strong support for environmental management decisions.

[0094] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0095] Based on the same inventive concept, please refer to Figure 6 As shown, this embodiment provides a risk assessment device for a compound, including: The first determining module is used to determine multiple compounds in the target type of wastewater and to determine the first target protein corresponding to the multiple compounds; wherein, the first target protein is the human target protein that the compounds act on. The second determining module is used to determine the health risk score corresponding to the multiple compounds based on at least one of the influence score of the multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The pathway network is used to characterize the correspondence between the compound, the target gene of the first target protein, and the biological pathway. The pathway network is constructed by enrichment analysis of the target gene.

[0096] In some embodiments, the first determining module is specifically used for: Obtain the molecular structure representation information of the various compounds, and query the second target protein corresponding to the various compounds based on the molecular structure representation information; Query the target database to identify multiple third-target proteins corresponding to the target disease; The first target protein corresponding to the various compounds is determined based on the intersection of the second target proteins corresponding to the various compounds with the various third target proteins.

[0097] In some embodiments, the impact score includes a first impact score, and the apparatus further includes: The third determining module is used to determine the first influence score based on at least one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of the plurality of compounds in the pathway network.

[0098] In some embodiments, the impact score includes a second impact score, and the apparatus further includes: The fourth determining module is used to determine, based on the pathway network, at least one of the following: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score corresponding to the various compounds; The fifth determining module is used to determine the second degree of influence score corresponding to the multiple compounds based on at least one of the pathway breadth score, target number score, pathway importance score, and gene regulation intensity score corresponding to the multiple compounds; The pathway breadth score is determined based on the total number of biological pathways of the compound, the target gene number score is determined based on the total number of target genes of the compound, the pathway importance score is determined based on the total score of the biological pathways of the compound, and the gene regulation strength score is determined based on the number of times the target genes of the compound are repeated in the pathway network.

[0099] In some embodiments, the fourth determining module is specifically used for: The weights of each biological pathway in the pathway network are determined, and those with weights greater than a preset threshold are identified as core biological pathways, while those with weights less than the preset threshold are identified as non-core biological pathways. The score of the core biological pathway is determined as a first preset value, and the score of the non-core biological pathway is determined as a second preset value; wherein, the first preset value is greater than the second preset value; The total scores of each core biological pathway and non-core biological pathway of the compound in the pathway network are normalized to obtain the pathway importance score.

[0100] In some embodiments, prior to determining the health risk scores corresponding to the plurality of compounds, the method further includes: Based on the pathway network or protein interaction network, the degree centrality of the first target protein is determined; wherein, the protein interaction network is used to characterize the association relationships between the first target proteins. For each compound, the first target protein with the highest degree centrality is selected as the key target protein. Based on the binding free energy of the various compounds with the corresponding key target proteins, the binding strength scores of the various compounds with the corresponding key target proteins are determined.

[0101] In some embodiments, determining the binding strength score between the plurality of compounds and the corresponding key target proteins based on the binding free energy of the plurality of compounds and the corresponding key target proteins includes: The binding free energies of the various compounds to the corresponding key target proteins are sequentially subjected to absolute value processing and normalization processing to obtain the initial binding strength scores of the compounds to the corresponding key target proteins. The initial binding strength score and the third preset value are summed to obtain the binding strength scores of the various compounds and the corresponding key target proteins; wherein the third preset value is greater than 0.

[0102] It should be understood that, for the sake of brevity, some of the content described in the previous embodiments will not be repeated in this embodiment.

[0103] Based on the same inventive concept, embodiments of this application provide a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a text classification method.

[0104] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0105] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: Multiple compounds in target type wastewater are identified, and the first target protein corresponding to the multiple compounds is identified; wherein, the first target protein is the human target protein to which the compounds act; The health risk score corresponding to the multiple compounds is determined based on at least one of the following: the influence score of the multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The pathway network is used to characterize the correspondence between the compound, the target gene of the first target protein, and the biological pathway. The pathway network is constructed by enrichment analysis of the target gene.

[0106] In some embodiments, when a processor executes a computer program, it performs the following steps: Obtain the molecular structure representation information of the various compounds, and query the second target protein corresponding to the various compounds based on the molecular structure representation information; Query the target database to identify multiple third-target proteins corresponding to the target disease; The first target protein corresponding to the various compounds is determined based on the intersection of the second target proteins corresponding to the various compounds with the various third target proteins.

[0107] In some embodiments, the impact score includes a first impact score, and the processor executes the following steps when executing a computer program: The first influence score is determined based on at least one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of the plurality of compounds in the pathway network.

[0108] In some embodiments, the impact score includes a second impact score, and the processor executes the following steps when executing a computer program: Based on the pathway network, at least one of the following is determined: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score for the various compounds; The second degree of influence score corresponding to the various compounds is determined based on at least one of the pathway breadth score, target number score, pathway importance score, and gene regulation intensity score. The pathway breadth score is determined based on the total number of biological pathways of the compound, the target gene number score is determined based on the total number of target genes of the compound, the pathway importance score is determined based on the total score of the biological pathways of the compound, and the gene regulation strength score is determined based on the number of times the target genes of the compound are repeated in the pathway network.

[0109] In some embodiments, when a processor executes a computer program, it performs the following steps: The weights of each biological pathway in the pathway network are determined, and those with weights greater than a preset threshold are identified as core biological pathways, while those with weights less than the preset threshold are identified as non-core biological pathways. The score of the core biological pathway is determined as a first preset value, and the score of the non-core biological pathway is determined as a second preset value; wherein, the first preset value is greater than the second preset value; The total scores of each core biological pathway and non-core biological pathway of the compound in the pathway network are normalized to obtain the pathway importance score.

[0110] In some embodiments, when a processor executes a computer program, it performs the following steps: Based on the pathway network or protein interaction network, the degree centrality of the first target protein is determined; wherein, the protein interaction network is used to characterize the association relationships between the first target proteins. For each compound, the first target protein with the highest degree centrality is selected as the key target protein. Based on the binding free energy of the various compounds with the corresponding key target proteins, the binding strength scores of the various compounds with the corresponding key target proteins are determined.

[0111] In some embodiments, when a processor executes a computer program, it performs the following steps: The binding free energies of the various compounds to the corresponding key target proteins are sequentially subjected to absolute value processing and normalization processing to obtain the initial binding strength scores of the compounds to the corresponding key target proteins. The initial binding strength score and the third preset value are summed to obtain the binding strength scores of the various compounds and the corresponding key target proteins; wherein the third preset value is greater than 0.

[0112] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0113] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for risk assessment of a compound, characterized in that, The method includes: Multiple compounds in target type wastewater are identified, and the first target protein corresponding to the multiple compounds is identified; wherein, the first target protein is the human target protein to which the compounds act; The health risk score corresponding to the multiple compounds is determined based on at least one of the following: the influence score of the multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The pathway network is used to characterize the correspondence between the compound, the target gene of the first target protein, and the biological pathway. The pathway network is constructed by enrichment analysis of the target gene.

2. The risk assessment method for the compound according to claim 1, characterized in that, The determination of the first target protein corresponding to the plurality of compounds includes: Obtain the molecular structure representation information of the various compounds, and query the second target protein corresponding to the various compounds based on the molecular structure representation information; Query the target database to identify multiple third-target proteins corresponding to the target disease; The first target protein corresponding to the various compounds is determined based on the intersection of the second target proteins corresponding to the various compounds with the various third target proteins.

3. The risk assessment method for the compound according to claim 1, characterized in that, The impact assessment includes a first impact assessment, and before determining the health risk scores corresponding to the multiple compounds, the method further includes: The first influence score is determined based on at least one of the degree centrality, neighborhood connectivity, proximity centrality, and clustering coefficient of the plurality of compounds in the pathway network.

4. The risk assessment method for the compound according to claim 1 or 3, characterized in that, The impact assessment includes a second impact assessment, and before determining the health risk scores corresponding to the multiple compounds, the method further includes: Based on the pathway network, at least one of the following is determined: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score for the various compounds; The second degree of influence score corresponding to the various compounds is determined based on at least one of the pathway breadth score, target number score, pathway importance score, and gene regulation intensity score. The pathway breadth score is determined based on the total number of biological pathways of the compound, the target gene number score is determined based on the total number of target genes of the compound, the pathway importance score is determined based on the total score of the biological pathways of the compound, and the gene regulation strength score is determined based on the number of times the target genes of the compound are repeated in the pathway network.

5. The risk assessment method for the compound according to claim 1, characterized in that, The determination of at least one of the following based on the pathway network: pathway breadth score, target gene number score, pathway importance score, and gene regulation strength score for the various compounds, including: The weights of each biological pathway in the pathway network are determined, and those with weights greater than a preset threshold are identified as core biological pathways, while those with weights less than the preset threshold are identified as non-core biological pathways. The score of the core biological pathway is determined as a first preset value, and the score of the non-core biological pathway is determined as a second preset value; wherein, the first preset value is greater than the second preset value; The total scores of each core biological pathway and non-core biological pathway of the compound in the pathway network are normalized to obtain the pathway importance score.

6. The risk assessment method for the compound according to claim 1, characterized in that, Before determining the health risk scores corresponding to the multiple compounds, the method further includes: Based on the pathway network or protein interaction network, the degree centrality of the first target protein is determined; wherein, the protein interaction network is used to characterize the association relationships between the first target proteins. For each compound, the first target protein with the highest degree centrality is selected as the key target protein. Based on the binding free energy of the various compounds with the corresponding key target proteins, the binding strength scores of the various compounds with the corresponding key target proteins are determined.

7. The method according to claim 6, characterized in that, The determination of the binding strength score between the various compounds and the corresponding key target proteins based on the binding free energy of the various compounds and the corresponding key target proteins includes: The binding free energies of the various compounds to the corresponding key target proteins are sequentially subjected to absolute value processing and normalization processing to obtain the initial binding strength scores of the compounds to the corresponding key target proteins. The initial binding strength score and the third preset value are summed to obtain the binding strength scores of the various compounds and the corresponding key target proteins; wherein the third preset value is greater than 0.

8. A risk assessment device for a compound, characterized in that, The device includes: The first determining module is used to determine multiple compounds in the target type of wastewater and to determine the first target protein corresponding to the multiple compounds; wherein, the first target protein is the human target protein that the compounds act on. The second determining module is used to determine the health risk score corresponding to the multiple compounds based on at least one of the influence score of the multiple compounds in the pathway network and the binding strength score of the multiple compounds to the corresponding first target protein. The pathway network is used to characterize the correspondence between the compound, the target gene of the first target protein, and the biological pathway. The pathway network is constructed by enrichment analysis of the target gene.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by at least one processor, implements the method as described in any one of claims 1-7.

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