A method and system for drug target screening

Abstract

The application provides a drug target screening method and system, and belongs to the technical field of network disintegration. The method comprises the following steps: constructing a disease-specific directed biomolecular network; based on the structure of the current directed biomolecular network, calculating the regulation level and feedback incoherence centrality of each node in the directed biomolecular network; virtually knocking out the node with the maximum feedback incoherence centrality value in the current directed biomolecular network, and updating the network structure to obtain a residual network; judging the structural integrity of the disease-specific directed biomolecular network and outputting a target screening result. The application can more efficiently disintegrate the disease network and induce early cascade collapse by adaptively iterating to cope with network dynamic recombination and accurately locking the “feedback hub” node based on the feedback incoherence centrality value of the regulation level.

Description

Technical Field

[0001] This invention relates to the field of network disruption technology, and in particular to a method and system for screening drug targets. Background Technology

[0002] In drug development for cancer, autoimmune diseases, and complex metabolic disorders, the occurrence of these diseases is often not caused by a single gene mutation, but by the dysregulation of a large-scale biomolecular network consisting of thousands or even tens of thousands of genes, proteins, and their complex interactions. Faced with such complex systems characterized by high dimensionality, heterogeneity, and dynamic evolution, finding "key targets" that can effectively reverse the disease network state is a core challenge in modern drug development.

[0003] To address this challenge, computational biology has seen the emergence of various target prediction strategies, including sequence homology analysis, molecular docking based on protein three-dimensional structure, and network analysis methods based on systems biology. Given the multi-gene synergistic nature of complex diseases, the traditional reductionist approach of "single drug-single target" is no longer sufficient for research and development needs. Therefore, systems analysis methods based on network pharmacology have become a current research hotspot, aiming to reveal the relationships between drugs, targets, and diseases from a holistic perspective. Existing mainstream technical solutions typically employ the following process: First, a biomolecular interaction network is constructed based on gene expression data and a protein interaction database; second, static topology analysis algorithms are used to calculate the centrality indices (such as degree centrality, betweenness centrality, and tight centrality) of each node in the network; finally, genes or proteins are ranked according to the values ​​of these indices, and the top-ranked nodes are recommended as potential drug targets.

[0004] However, despite the enormous potential shown by network-based analysis methods, existing mainstream network target prediction techniques still have the following significant drawbacks in practical applications: (1) Ignoring the directionality and hierarchical structure of biological systems: Traditional network pharmacology analysis is often based on gene co-expression networks, protein interaction networks, etc., which are usually undirected graphs. However, intracellular biological processes (such as transcriptional regulation and phosphorylation cascades) have strict directionality (e.g., transcription factor → target gene). Ignoring directionality makes it impossible to distinguish between "upstream driving factors" and "downstream effector factors", which may result in screening effector molecules at the end of the network instead of key upstream regulators that control the occurrence of diseases.

[0005] (2) Neglecting drug resistance and dynamic reorganization: Existing methods are usually static, that is, the target is determined by calculating the index only once on the initial network. However, biological systems are extremely robust. When a highly connected node is blocked by a drug, the cell will often quickly activate the backup bypass mechanism or feedback loop to reorganize the network, thereby maintaining survival (i.e., developing drug resistance). Existing static network analysis methods (such as simple degree centrality and betweenness centrality analysis) cannot simulate this dynamic evolution process of "drug interference-network adaptation-structural reorganization", which makes the screened targets easily ineffective in clinical applications due to the development of drug resistance.

[0006] (3) Risk of off-target toxicity due to over-reliance on "high connectivity": Traditional algorithms tend to target nodes with the highest connectivity. However, these nodes often participate in multiple key housekeeping functions of normal cells. While blindly attacking nodes with the highest connectivity can disrupt disease-specific directed biomolecular networks, it is highly likely to cause serious side effects (off-target toxicity). Existing technologies lack the ability to identify those nodes with a small number of connections but located in the critical "throat" position (i.e., feedback hub) for maintaining the pathological state, making it difficult to achieve a therapeutic effect of "low side effects and high precision". Summary of the Invention

[0007] This invention provides a method and system for screening drug targets, which addresses at least one deficiency in the prior art.

[0008] In a first aspect, the present invention provides a method for screening drug targets, comprising: S10: Construct disease-specific directed biomolecular networks; where nodes in the network represent biomolecular entities and directed edges represent directional regulatory relationships between biomolecules. S20: Based on the current structure of directed biomolecular networks, calculate the regulatory hierarchy and feedback incoherence centrality of each node in the directed biomolecular network. S30: Virtually knock out the node with the largest feedback incoherence centrality value in the current directed biomolecular network, update the network structure, and obtain the residual network; S40: Calculate the preset network structure integrity index of the residual network. If the preset network structure integrity index is greater than the preset threshold, return to step S20 for iteration based on the residual network. If the preset network structure integrity index is less than or equal to the preset threshold, output the sequence of nodes virtually knocked out in each iteration as the drug target screening result.

[0009] According to the drug target screening method provided by the present invention, a disease-specific directed biomolecular network is constructed, including: based on the omics data of the target disease, constructing an adjacency matrix containing a set of nodes and a set of directed edges by integrating a biological prior knowledge base and data-driven correlation analysis. The node set represents the set of biomolecular entities within the cell; the directed edge set represents the set of directional regulatory relationships between molecules; and the matrix elements... This indicates the existence of slave nodes. Pointing to node Regulated flow.

[0010] According to the drug target screening method provided by the present invention, the calculation of the regulatory level of the node in step S20 specifically includes: calculating the in-degree and out-degree of each node based on the adjacency matrix of the current directed biomolecular network; constructing the Laplace matrix Λ and the unbalanced vector v based on the adjacency matrix, the in-degree and out-degree of each node; solving the linear equation system Λh=v to obtain the regulatory level vector h that characterizes the topological position of the node in the network.

[0011] According to the drug target screening method provided by the present invention, the calculation of the feedback incoherence centrality of the nodes in step S20 specifically includes: The feedback link in the network is identified based on the control hierarchy vector h; wherein, for any node i Pointing to node j A directed edge, if node i The regulatory level value h i Greater than node j The regulatory level value h j Then the directed edge is determined to be a feedback link; Construct a feedback adjacency matrix  based on the feedback links; wherein, for an edge determined to be a feedback link, its corresponding matrix element  ij and  ji Set all values ​​to 1, otherwise set them to 0; According to the formula compute nodes i Feedback irrelevance centrality value.

[0012] According to the drug target screening method provided by the present invention, step S30 specifically includes: selecting the node with the largest feedback incoherence centrality from all nodes of the current directed biomolecular network as the interference target of this iteration, and removing the largest node and all its associated incoming and outgoing edges from the network; updating the adjacency matrix A and the node set of the network to obtain the residual network structure after removing the node.

[0013] According to the drug target screening method provided by the present invention, the preset network structure integrity index is the maximum strongly connected component.

[0014] According to the drug target screening method provided by the present invention, the biomolecular entities include genes, transcription factors, and proteins or their complexes; the directional regulatory relationship includes transcriptional regulation, signal transduction, or metabolic response.

[0015] Secondly, the present invention also provides a drug target screening system, comprising: A directed network building module is used to construct disease-specific directed biomolecular networks; in which nodes in the network represent biomolecular entities, and directed edges represent directional regulatory relationships between biomolecules. The hierarchical dynamics analysis module is used to calculate the regulatory hierarchy and feedback incoherence centrality of each node in a directed biomolecular network based on the current structure of the directed biomolecular network. The disintegration execution module is used to virtually knock out the node with the largest feedback incoherence centrality value in the current directed biomolecular network and update the network structure to obtain the residual network. The iterative control module is used to calculate the preset network structure integrity index of the residual network. If the preset network structure integrity index is greater than the preset threshold, the module returns to the execution hierarchical dynamics analysis module for iteration based on the residual network. If the preset network structure integrity index is less than or equal to the preset threshold, the sequence of nodes virtually knocked out in each iteration is output as the drug target screening result.

[0016] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the drug target screening methods described above.

[0017] Fourthly, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the drug target screening methods described above.

[0018] The drug target screening method and system provided by this invention have the following advantages compared with the prior art: (1) Unlike existing static network pharmacology methods that only perform one-time calculations on the initial network, this invention establishes a dynamic feedback loop. After each simulated drug attack (node ​​removal), the regulation hierarchy equations are forced to be resolved for the remaining residual network, and the feedback incoherence centrality of all remaining nodes is updated.

[0019] This invention ensures that the screened targets (or combinations of targets) remain effective after network recombination. They are the most critical nodes for maintaining the system state in the current disease-specific directed biomolecular network state, thereby enabling continuous and efficient drive of the collapse of the disease-specific directed biomolecular network.

[0020] (2) This invention defines the regulatory hierarchy of genes by solving the flow equilibrium equation and calculates the feedback incoherence centrality accordingly. This index is specifically used to quantify the contribution of nodes to maintaining pathological feedback loops.

[0021] This invention can accurately identify feedback hubs that may have a small number of connections but occupy a crucial "throat" position in maintaining pathological homeostasis. Attacking such targets can dismantle disease-specific directed biomolecular networks at minimal cost. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the drug target screening method provided by the present invention; Figure 2 This is a schematic diagram of the structure of the drug target screening system provided by the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] It should be noted that, in the description of the embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0026] The following is combined Figures 1-3This invention describes the drug target screening method and system provided in the embodiments of the present invention.

[0027] Figure 1 This is a flowchart illustrating the drug target screening method provided by the present invention, as shown below. Figure 1 As shown, including but not limited to the following steps: S10: Construct a disease-specific directed biomolecular network, where nodes in the network represent biomolecular entities and directed edges represent directional regulatory relationships between biomolecules.

[0028] The specific steps are as follows: Acquire omics data for the target disease (such as single-cell RNA sequencing data, ChIP-seq data, etc.). Utilize existing network inference methods to construct a network containing a set of nodes. and the set of directed edges adjacency matrix Specifically, the directed network can be constructed by integrating biological prior knowledge bases (such as TRRUST and KEGG, used to determine specific regulatory directions) with data-driven correlation analysis (used to determine active connections in specific tissues). The node set... A set of directed edges representing biomolecular entities within a cell (specifically, genes, transcription factors, proteins, or their complexes); A set representing the directional regulatory relationships between molecules (specifically transcriptional regulation, signal transduction, or metabolic responses). Matrix elements. This indicates the existence of slave nodes. Pointing to node Regulated flow.

[0029] It should be noted that step S10 is not the core innovation of this invention. This invention is compatible with any method capable of generating directed graphs. The specific network construction can be directly accomplished using existing mature commercial software or open-source algorithms (such as SCENIC, GENIE3, etc.). The innovation of this invention lies in how to perform dynamic analysis in subsequent steps after obtaining matrix A.

[0030] S20: Based on the current structure of directed biomolecular networks, calculate the regulatory hierarchy and feedback incoherence centrality of each node in the directed biomolecular network.

[0031] This step aims to identify key nodes that cross different regulatory levels and mediate the formation of pathological closed loops. In each iteration of the algorithm, this step recalculates the remaining network structure. Specifically, it includes the following sub-steps: S21: Construct the Laplace matrix and solve for the control hierarchy.

[0032] Based on the current adjacency matrix Calculate each node in-degree and out-degree Construct the Laplace matrix ,in A diagonal matrix, diagonal elements Constructing an unbalanced vector , of which elements .

[0033] Solve the system of linear equations To obtain the control level vector Each element in the vector The corresponding nodes were quantified. In the topological hierarchy of a directed network; where the smaller The value usually corresponds to the upstream signal receiver, and a larger value indicates a higher value. The value typically corresponds to the downstream effector. To accommodate the computational demands of large-scale networks, iterative solutions using the least squares method are preferred to reduce time complexity.

[0034] S22: Pathological feedback chain and construct feedback matrix.

[0035] This step aims to define anomalous connections that violate the flow direction of the regulatory hierarchy as "feedback links," and to construct a feedback matrix for subsequent calculations based on these links. The specific implementation process is as follows: (a) Identifying pathological feedback loops. In healthy biological signal transduction networks, the topology often implies an upward flow from lower regulatory levels to higher regulatory levels (e.g., receptors). kinase (Transcription factors). In this embodiment, edges that violate this hierarchical flow are defined as "feedback links".

[0036] The specific determination rule is as follows: for any directed edge in the network... (i.e., adjacency matrix elements) If the starting point The regulatory level is higher than the endpoint The regulatory level (i.e., meeting the conditions) If an edge is found to be a feedback link, then that edge is considered a feedback link. Such reverse links constitute pathological feedback loops in disease-specific directed biomolecular networks and are a key structural basis for maintaining disease states and the Giant Strongly Connected Component (GSCC) of directed networks.

[0037] (b) Constructing the feedback adjacency matrix To quantify the degree to which nodes participate in backward links, an adjacency matrix is ​​constructed. Feedback adjacency matrices of the same size This matrix records only connections identified as feedback links and employs symmetry to capture the shared contributions of the nodes at both ends of the feedback loop (i.e., the signal sender and receiver). Matrix elements The specific assignment rules are as follows: for any directed edge If the edge is determined to be a feedback link edge, then Otherwise, set the corresponding element. ,Right now: .

[0038] (c) Calculate the feedback incoherence centrality. According to the formula... Calculate the feedback incoherence centrality of each node. ,in, express Traverse all nodes in the network. In this formula, This measure assesses the extent to which the feedback link crosses the control level (i.e., the severity of the signal backflow deviating from the normal transduction level). The value is for the node The weighted summation of the hierarchical misalignment magnitudes across all participating pathological feedback links can accurately quantify the nodes. The structural contribution to maintaining the global pathological feedback loop. The higher the value, the stronger the gene's ability to forcibly couple distant upstream and downstream signals, and the more critical its role in maintaining the maximum strongly connected component (GSCC) of disease-specific directed biomolecular networks and pathological homeostasis (such as drug resistance). Therefore, in subsequent virtual screening steps, such biomolecular entities will be identified as high-priority potential drug targets for simulated inhibition.

[0039] S30: Virtually knock out the node with the largest feedback incoherence centrality value in the current directed biomolecular network, update the network structure, and obtain the residual network.

[0040] This step performs virtual drug screening operations in a computer model and simulates the state evolution of a biological network after external disturbances. Specifically, it includes the following sub-steps: S31: Identify the drug interference target and perform virtual knockout.

[0041] Among all nodes in the current directed network, select those with feedback incoherence centrality. The node with the largest value is designated as the key interference target (i.e., the ideal drug target) in this iteration. This node and all its associated incoming and outgoing edges are removed from the directed network model.

[0042] S32: Update disease-specific directed biomolecular network topology data.

[0043] Update the adjacency matrix and node set This yields the residual network structure after node removal. At this point, due to the topology change, the regulatory hierarchy and pathological feedback loop structure of the remaining nodes have undergone dynamic reorganization. This step simulates the process in biological systems where, after a critical pathway is blocked, signal flow is forced to divert, re-establishing homeostasis through alternative pathways or potential connections.

[0044] S40: Calculate the preset network structure integrity index of the residual network. If the preset network structure integrity index is greater than the preset threshold, return to step S20 for iteration based on the residual network. If the preset network structure integrity index is less than or equal to the preset threshold, output the sequence of nodes virtually knocked out in each iteration as the drug target screening result.

[0045] This step controls the adaptive iterative process, simulating the determination of clinical treatment endpoints. It specifically includes the following sub-steps: S41: Calculate the structural integrity index of the disease-specific directed biomolecular network and determine termination. Calculate the size of the largest strongly connected component GSCC of the current residual disease-specific directed biomolecular network, which is the network structural integrity index. Compare this index with the preset system collapse threshold (e.g., 1% of the initial network size). (1) If GSCC > threshold: It is determined that the disease-specific directed biomolecular network has not completely collapsed, and return to step S20. Using the updated adjacency matrix A, for the residual network, calculate the regulatory hierarchy and feedback incoherence centrality of the nodes in the network, and enter the next iteration. This "calculation-removal-recalculation" cycle constitutes the adaptive mechanism of the present invention. (2) If GSCC < threshold: It is determined that the disease-specific directed biomolecular network has collapsed, and execute step S42.

[0046] S42: Output key target sequences. Gene nodes removed in each iteration are recorded sequentially to form key target sequences. These sequences are then output as optimal combination drug targets or sequential treatment strategies recommended to the user.

[0047] Figure 2 This is a schematic diagram of the structure of the drug target screening system provided by the present invention, as shown below. Figure 2 As shown, the present invention also provides a drug target screening system, comprising: The directed network construction module 100 is used to construct disease-specific directed biomolecular networks; wherein, nodes in the network represent biomolecular entities, and directed edges represent directional regulatory relationships between biomolecules. The hierarchical dynamics analysis module 200 is used to calculate the regulatory hierarchy and feedback incoherence centrality of each node in the directed biomolecular network based on the current structure of the directed biomolecular network. The disintegration execution module 300 is used to virtually knock out the node with the largest feedback incoherence centrality value in the current directed biomolecular network and update the network structure to obtain the residual network. The iteration control module 400 is used to calculate the preset network structure integrity index of the residual network. If the preset network structure integrity index is greater than the preset threshold, the module returns to the execution hierarchical dynamics analysis module for iteration based on the residual network. If the preset network structure integrity index is less than or equal to the preset threshold, the sequence of nodes virtually knocked out in each iteration is output as the drug target screening result.

[0048] It should be noted that the drug target screening system provided in this embodiment of the invention can execute the drug target screening method described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0049] In summary, the improvements, technical problems solved, and beneficial effects of the drug target screening method and system provided by this invention include at least the following: (1) An adaptive iterative mechanism based on a closed loop of “calculation-removal-recalculation” is introduced.

[0050] Improvement Description: Unlike existing static network pharmacology methods that perform only one-time calculations on the initial network, this invention establishes a dynamic feedback loop. After each simulated drug attack (node ​​removal), the regulatory hierarchy equations are forced to be resolved for the remaining residual network, and the feedback incoherence centrality of all remaining nodes is updated.

[0051] Technical problem solved: This addresses the inability of existing static methods to handle network reorganization and drug resistance in biological systems (such as cancer cells) under drug stress. When a key target is inhibited, cells often activate bypass mechanisms, leading to the failure of static predictions.

[0052] Corresponding advantages: It ensures that the selected targets (or combinations of targets) remain effective after network recombination. They are the most critical nodes for maintaining the system state in the current disease-specific directed biomolecular network state, thereby continuously and efficiently driving the collapse of the disease-specific directed biomolecular network.

[0053] (2) A targeted screening strategy based on "regulation level" and "feedback incoherence" was established.

[0054] Improvement Description: This invention defines the regulatory hierarchy of genes by solving the flow equilibrium equation and calculates the feedback incoherence centrality accordingly. This index is specifically used to quantify the contribution of nodes to maintaining pathological feedback loops.

[0055] Technical problems solved: This technology addresses the issues of ignoring the directionality of biological signals and blindly attacking critical nodes in existing techniques. Blindly attacking critical nodes can easily disrupt the housekeeping function of normal cells, leading to severe drug side effects (off-target toxicity).

[0056] The corresponding advantages are: it can accurately identify feedback hubs that may have a small number of connections but are located in the "throat" position of maintaining pathological homeostasis. Attacking such targets can dismantle disease-specific directed biomolecular networks at minimal cost, achieving a "highly precise, low-side-effect" therapeutic effect.

[0057] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communications bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communications bus 340. The processor 310 can call logical instructions stored in the memory 330 to execute drug target screening methods.

[0058] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for screening drug targets, characterized in that, include: S10: Construct disease-specific directed biomolecular networks; where nodes in the network represent biomolecular entities and directed edges represent directional regulatory relationships between biomolecules. S20: Based on the current structure of directed biomolecular networks, calculate the regulatory hierarchy and feedback incoherence centrality of each node in the directed biomolecular network. S30: Virtually knock out the node with the largest feedback incoherence centrality value in the current directed biomolecular network, update the network structure, and obtain the residual network; S40: Calculate the preset network structure integrity index of the residual network. If the preset network structure integrity index is greater than the preset threshold, return to step S20 for iteration based on the residual network. If the preset network structure integrity index is less than or equal to the preset threshold, output the sequence of nodes virtually knocked out in each iteration as the drug target screening result.

2. The drug target screening method according to claim 1, characterized in that, Constructing disease-specific directed biomolecular networks, including: Based on omics data of the target disease, a node set is constructed by integrating a biological prior knowledge base with data-driven correlation analysis. Adjacency matrix of directed edge set ; Here, the node set represents the set of biomolecular entities within the cell; the directed edge set represents the set of directional regulatory relationships between molecules; and the matrix elements... This indicates the existence of slave nodes. Pointing to node Regulated flow.

3. The drug target screening method according to claim 1, characterized in that, The control hierarchy of the computing nodes in step S20 specifically includes: Calculate the in-degree and out-degree of each node based on the adjacency matrix of the current directed biomolecular network. Based on the adjacency matrix, the in-degree and out-degree of each node, construct the Laplacian matrix Λ and the unbalanced vector v; Solving the linear equation system Λh=v yields the control level vector h, which represents the topological position of a node in the network.

4. The drug target screening method according to claim 3, characterized in that, The calculation of the feedback incoherence centrality of the nodes in step S20 specifically includes: The feedback link in the network is identified based on the control hierarchy vector h; wherein, for any node i Pointing to node j A directed edge, if node i The regulatory level value h i Greater than node j The regulatory level value h j Then the directed edge is determined to be a feedback link; Construct a feedback adjacency matrix  based on the feedback links; wherein, for an edge determined to be a feedback link, its corresponding matrix element  ij and  ji Set all values ​​to 1, otherwise set them to 0; According to the formula compute nodes i Feedback irrelevance centrality value.

5. The drug target screening method according to claim 1, characterized in that, Step S30 specifically includes: From all nodes in the current directed biomolecular network, select the node with the largest feedback incoherence centrality as the interference target for this round of iteration, and remove the node with the largest centrality and all its associated inbound and outbound edges from the network. Update the adjacency matrix A and the set of nodes of the network to obtain the residual network structure after removing the nodes.

6. The drug target screening method according to claim 1, characterized in that, The preset network structure integrity index is the maximum strongly connected component.

7. The drug target screening method according to claim 1, characterized in that, The biomolecular entities include genes, transcription factors, and proteins or complexes thereof; The directional regulatory relationships include transcriptional regulation, signal transduction, or metabolic responses.

8. A drug target screening system, characterized in that, include: A directed network building module is used to construct disease-specific directed biomolecular networks; in which nodes in the network represent biomolecular entities, and directed edges represent directional regulatory relationships between biomolecules. The hierarchical dynamics analysis module is used to calculate the regulatory hierarchy and feedback incoherence centrality of each node in a directed biomolecular network based on the current structure of the directed biomolecular network. The disintegration execution module is used to virtually knock out the node with the largest feedback incoherence centrality value in the current directed biomolecular network and update the network structure to obtain the residual network. The iterative control module is used to calculate the preset network structure integrity index of the residual network. If the preset network structure integrity index is greater than the preset threshold, the module returns to the execution hierarchical dynamics analysis module for iteration based on the residual network. If the preset network structure integrity index is less than or equal to the preset threshold, the sequence of nodes virtually knocked out in each iteration is output as the drug target screening result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the drug target screening method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the drug target screening method as described in any one of claims 1 to 7.

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