Traditional Chinese medicine molecular network positioning navigation method and system, electronic equipment and medium

Abstract

The invention discloses a traditional Chinese medicine molecule network positioning navigation method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring traditional Chinese medicine molecule data of a target drug prescription acting on a target tissue and organ site, wherein the traditional Chinese medicine molecule data comprises molecular weights, lipid-water partition coefficients and molecule-target binding constants of a plurality of chemical components; calculating a concentration value based on the molecular weight and the fat-water distribution coefficient; calculating the effective action intensity of the target chemical component on the target protein according to the concentration numerical value and the molecule-target binding constant of each chemical component; the effective action strength is filled into a tensor structure with the target spot protein as the dimension, and a network connectivity tensor is obtained; and performing space-time difference calculation on the network connectivity tensor to obtain network topology structure data, constructing a corresponding molecular network navigation path diagram, and outputting a molecular network positioning navigation result. By implementing the technical scheme provided by the invention, the accuracy of traditional Chinese medicine molecule network positioning navigation can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a traditional Chinese medicine molecular network positioning and navigation method and system, an electronic device and a medium. BACKGROUND

[0002] With the in-depth development of modernization of traditional Chinese medicine, the molecular level research on the mechanism of traditional Chinese medicine compound has become a current research hotspot. As a complex drug system with multiple components and multiple targets, the action process of traditional Chinese medicine compound in the human body involves multiple biological pathways and organs, and accurate molecular network positioning and navigation is of great significance to guide clinical medication.

[0003] At present, network pharmacology methods are widely used in the molecular network positioning and navigation research of traditional Chinese medicine compound. Researchers construct a drug-target-disease correlation network, use molecular docking technology to predict the binding ability of drug molecules and target proteins, or use data mining methods to establish a correlation map of drug components and disease targets, so as to determine the molecular network pathway of drug action.

[0004] However, in practical application, the metabolism of traditional Chinese medicine compound has obvious time sequence characteristics, and the concentration of chemical components in the body will change dynamically with time, which directly affects the action effect between drugs and targets. The existing network analysis method mainly relies on static data for research, which often fails to reflect the time sequence changes of traditional Chinese medicine chemical components, and cannot accurately depict the dynamic evolution process of drug action, thereby reducing the accuracy of traditional Chinese medicine molecular network positioning and navigation. SUMMARY

[0005] The present application provides a traditional Chinese medicine molecular network positioning and navigation method, system, electronic device and medium, which can improve the accuracy of traditional Chinese medicine molecular network positioning and navigation.

[0006] In a first aspect, the present application provides a traditional Chinese medicine molecular network positioning and navigation method, comprising: obtaining traditional Chinese medicine molecular data of a target drug prescription acting on a target organ site, the traditional Chinese medicine molecular data comprising molecular weight, lipid-water partition coefficient and molecular-target binding constant of multiple chemical components; calculating the concentration values of the corresponding chemical components at different time sampling points based on the molecular weight and lipid-water partition coefficient of each chemical component; calculating the effective action strength of the target chemical component on the target protein at different time sampling points according to the concentration values of each chemical component at different time sampling points and the molecular-target binding constant of each chemical component; filling the effective action strength of the target chemical component into a tensor structure with target protein as the dimension according to the corresponding time sampling points to obtain a network connectivity tensor; Spatiotemporal difference calculation is performed on the network connectivity tensor to obtain network topology data. A molecular network navigation path graph corresponding to the network topology data is constructed, and the molecular network positioning and navigation results of the target drug prescription are output in the molecular network navigation path graph.

[0007] By employing the above technical solution, firstly, the concentration values ​​at different time sampling points are calculated based on the molecular weight and lipid-water partition coefficient of chemical components in traditional Chinese medicine molecular data. Then, the effective intensity of the target chemical component's action on the target protein is calculated using the molecule-target binding constant, thus accurately reflecting the dynamic changes of the chemical component in vivo. Secondly, by filling the effective intensity of the target chemical component into a tensor structure with the target protein as the dimension according to the time sampling points, the resulting network connectivity tensor not only retains the temporal characteristics of molecular action but also characterizes the interaction relationship between molecules and the target. Finally, spatiotemporal difference calculations are performed on the network connectivity tensor, and the resulting network topology data reflects the dynamic evolution of the molecular network. The constructed molecular network navigation path diagram can more accurately describe the mechanism of action of the target drug prescription, thereby improving the accuracy of traditional Chinese medicine molecular network positioning and navigation, and providing more reliable guidance for clinical medication.

[0008] Optionally, the time sampling interval and total sampling duration are obtained, and multiple time sampling point sequences are generated based on the time sampling interval; for each chemical component, the corresponding drug metabolism rate parameter is determined according to the molecular weight of the chemical component, and the corresponding tissue distribution characteristic parameter is determined based on the lipid-water partition coefficient; combining the drug metabolism rate parameter and tissue distribution characteristic parameter of each chemical component, the theoretical distribution concentration of each chemical component at each time sampling point is calculated; based on the physiological characteristic parameters of the target tissue organ site, the theoretical distribution concentration of each chemical component is site-specifically corrected to obtain the concentration values ​​of each chemical component at different time sampling points.

[0009] Optionally, the distribution density and types of target proteins within the target tissue / organ site are obtained; for each chemical component, the molecular-target binding constant corresponding to the chemical component is extracted, and based on the molecular-target binding constant and the types of target proteins, the binding affinity level between the chemical component and the target protein is determined; at each time sampling point, the concentration values ​​of each chemical component are matched with the distribution density of the target protein to obtain the molecular-target binding saturation of the corresponding chemical component at the time sampling point; the target chemical component in each chemical component is determined, and based on the binding affinity level of the target chemical component and the molecular-target binding saturation corresponding to each time sampling point, the effective intensity of the target chemical component on the target protein at each time sampling point is calculated.

[0010] Optionally, the total volume parameter of the target tissue / organ site is obtained; based on the total volume parameter and the distribution density of the target protein, the total amount of target protein in the target tissue / organ site is calculated; for the time sampling point, based on the concentration value of each chemical component and the total volume parameter, the total number of molecules of each chemical component in the target tissue / organ site is calculated; the total number of molecules of each chemical component and the total amount of target protein are normalized and the ratio is calculated to obtain the molecular-target binding saturation corresponding to each chemical component.

[0011] Optionally, among the chemical components, those with a binding affinity level greater than or equal to an affinity level threshold are selected as target chemical components; it is determined whether the molecular-target binding saturation of the target chemical component at each of the time sampling points exceeds a saturation threshold; when the molecular-target binding saturation at the time sampling point exceeds the saturation threshold, the binding affinity level of the target chemical component is used as a baseline effect strength, and the baseline effect strength is adjusted by a preset ratio according to the molecular-target binding saturation to obtain the effective effect strength of the target chemical component on the target protein; when the molecular-target binding saturation value at the time sampling point does not exceed the saturation threshold, the effective effect strength is set to a preset minimum effect strength.

[0012] Optionally, the network connectivity tensor is differentially processed in the time dimension to calculate the rate of change of connectivity strength between the target proteins as network topology data; based on the network topology data, target protein pairs whose connectivity change rate exceeds a threshold are identified and designated as key network nodes; the connection weights and directions between the key network nodes are determined according to their rate of change values ​​and directions; and a molecular network navigation path graph is constructed based on the connection weights and directions of the key network nodes.

[0013] Optionally, the change rate values ​​and change direction identifiers corresponding to any two key network nodes are obtained; when the change direction identifiers corresponding to any two key network nodes are the same, a connection weight coefficient is calculated based on the product of the change rate values ​​of the two key network nodes, and the key network node with the larger change rate value is taken as the connection starting point; when the change direction identifiers of any two key network nodes are opposite, a connection weight coefficient is calculated based on the ratio of the change rate values ​​of the two key network nodes, and the key network node with the change direction in a preset positive direction is taken as the connection starting point; the connection weight coefficient is multiplied by a preset weight benchmark value to obtain the connection weight between the key network nodes, and connectivity analysis is performed on each connection starting point to determine the connection direction between the key network nodes.

[0014] A second aspect of this application provides a molecular network positioning and navigation system for traditional Chinese medicine, the system comprising: The data acquisition module is used to acquire traditional Chinese medicine molecular data on the target tissue and organ sites where the target drug prescription acts. The traditional Chinese medicine molecular data includes the molecular weight, lipid-water partition coefficient and molecule-target binding constant of multiple chemical components. The concentration value determination module is used to calculate the concentration values ​​of the corresponding chemical components at different time sampling points based on the molecular weight and lipid-water partition coefficient of each chemical component. The network connectivity tensor determination module is used to calculate the effective intensity of the target chemical component on the target protein at different time sampling points based on the concentration values ​​of each chemical component at different time sampling points and the molecular-target binding constant of each chemical component; and to fill the effective intensity of the target chemical component into a tensor structure with the target protein as the dimension according to the corresponding time sampling points to obtain the network connectivity tensor. The molecular network positioning and navigation module is used to perform spatiotemporal difference calculation on the network connectivity tensor to obtain network topology data, construct the molecular network navigation path graph corresponding to the network topology data, and output the molecular network positioning and navigation result of the target drug prescription in the molecular network navigation path graph.

[0015] A third aspect of this application provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, the program being loaded and executed by the processor to implement a method for positioning and navigating molecular networks of traditional Chinese medicine.

[0016] In a fourth aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a method for positioning and navigating molecular networks in traditional Chinese medicine.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By employing the above technical solution, firstly, the concentration values ​​at different time sampling points are calculated based on the molecular weight and lipid-water partition coefficient of chemical components in traditional Chinese medicine molecular data. Then, the effective intensity of the target chemical component's action on the target protein is calculated using the molecule-target binding constant, thus accurately reflecting the dynamic changes of the chemical component in vivo. Secondly, by filling the effective intensity of the target chemical component into a tensor structure with the target protein as the dimension according to the time sampling points, the resulting network connectivity tensor not only retains the temporal characteristics of molecular action but also characterizes the interaction relationship between molecules and the target. Finally, spatiotemporal difference calculations are performed on the network connectivity tensor, and the resulting network topology data reflects the dynamic evolution of the molecular network. The constructed molecular network navigation path diagram can more accurately describe the mechanism of action of the target drug prescription, thereby improving the accuracy of traditional Chinese medicine molecular network positioning and navigation, and providing more reliable guidance for clinical medication. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a molecular network localization and navigation method for traditional Chinese medicine provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a traditional Chinese medicine molecular network positioning and navigation system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0019] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] This application provides a method for molecular network localization and navigation in traditional Chinese medicine. In one embodiment, please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the molecular network localization and navigation method for traditional Chinese medicine provided in this application. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone tool application. The method can also be implemented using a microcontroller or run on a molecular network localization and navigation system for traditional Chinese medicine based on the von Neumann architecture. Specifically, the method may include the following steps: Step 101: Obtain TCM molecular data on the target drug prescription acting on the target tissue and organ sites. The TCM molecular data includes the molecular weight, lipid-water partition coefficient and molecular-target binding constant of multiple chemical components.

[0024] Among them, the target drug prescription refers to the traditional Chinese medicine compound preparation that requires molecular network localization and navigation analysis; the target tissue and organ site refers to the specific human tissue or organ site where the drug exerts its therapeutic effect; the traditional Chinese medicine molecular data is used to represent the basic data set describing the characteristics of the chemical components of traditional Chinese medicine; the chemical component represents the bioactive effective molecules in the traditional Chinese medicine compound; the molecular weight refers to the relative molecular mass of the chemical component, expressed in Daltons; the lipid-water partition coefficient is used to represent the distribution ratio of the chemical component in lipid-soluble and water-soluble media; and the molecule-target binding constant represents the affinity strength between the chemical component and the target protein.

[0025] Specifically, the first step is to clearly define the composition of the traditional Chinese medicine compound prescription to be analyzed and its target tissues and organs. Then, through database searches, literature reviews, or experimental measurements, molecular characteristic data of each chemical component in the prescription are obtained. This data includes molecular weight (characterizing molecular size), lipid-water partition coefficient (reflecting the transmembrane ability of molecules), and binding constant (describing the ability of molecules to bind to the target site). This data will provide fundamental support for subsequent analysis of the distribution characteristics and effects of the chemical components in vivo.

[0026] In some embodiments, molecular data of traditional Chinese medicine can be obtained in multiple ways: Optionally, firstly, information on the chemical components contained in the target prescription can be retrieved from a database of chemical components of traditional Chinese medicine (such as TCMSP, TCMID, etc.), and physicochemical parameters such as molecular weight and LogP value of each component can be extracted; then, molecular docking software can be used to calculate the binding energy between the chemical components and the known target protein, and the binding energy can be converted into a binding constant; finally, the acquired data can be standardized to establish a unified molecular dataset. Optionally, firstly, the chemical components of the traditional Chinese medicine compound can be analyzed using liquid chromatography-mass spectrometry to obtain the molecular weight information of each component; then, the lipid-water partition coefficient of each component can be determined using an octanol-water partition experiment; next, the binding kinetic parameters between the molecule and the target can be determined using surface plasmon resonance technology, and the binding constant can be calculated; finally, all experimental data can be integrated to form a complete molecular dataset. It is understood that other experimental methods or data mining techniques can also be used to obtain the required molecular data, which are not limited here.

[0027] Step 102: Based on the molecular weight and lipid-water partition coefficient of each chemical component, calculate the concentration values ​​of the corresponding chemical component at different time sampling points.

[0028] Among them, chemical components represent the biologically active molecular entities in traditional Chinese medicine compound prescriptions; molecular weight refers to the relative molecular mass of chemical components, used to characterize molecular size; lipid-water partition coefficient represents the distribution ratio of chemical components in lipid-soluble and water-soluble media, used to characterize the transmembrane ability of molecules; time sampling point refers to discrete time nodes selected in the process of drug metabolism; concentration value is used to represent the content level of chemical components at a specific time point, usually expressed as molar concentration or mass concentration.

[0029] Specifically, this step is performed after acquiring molecular data of traditional Chinese medicine (TCM) drugs to simulate the dynamic distribution process of drugs in the body. First, a pharmacokinetic model needs to be established, using molecular weight as a key parameter affecting the drug's metabolic rate; the larger the molecular weight, the slower the metabolic rate. Simultaneously, the lipid-water partition coefficient is considered an important factor influencing drug tissue distribution; the larger the partition coefficient, the easier it is for the drug to cross biological membranes and enter tissues. Based on these physicochemical parameters, combined with human physiological characteristics, a mathematical model is used to calculate the theoretical concentration of each chemical component at different time points. This calculation needs to consider multiple stages such as drug absorption, distribution, metabolism, and excretion, ultimately yielding a concentration-time curve reflecting the dynamic distribution characteristics of the drug over time.

[0030] In some embodiments, the dynamic calculation of chemical component concentrations can be achieved in several ways: Optionally, a single-compartment pharmacokinetic model is first established, treating the human body as a single homogeneous system. The elimination half-life of the drug is determined based on its molecular weight, and the decay process of drug concentration over time is calculated based on the first-order kinetic equation. Then, the apparent volume of distribution of the drug is estimated using the lipid-water partition coefficient, and the concentration is volume-corrected. Finally, the theoretical concentration at different time points is solved using numerical integration methods, taking into account the dosage and administration method. Optionally, a multi-compartment pharmacokinetic model is first constructed, dividing the human body into a central compartment and peripheral compartments. The inter-compartment transport rate constant is calculated based on the molecular weight and lipid-water partition coefficient. Then, a set of differential equations describing drug transport between compartments is established, and numerical solutions are obtained using the Runge-Kutta method. Finally, the local concentration of the drug in the target tissue is calculated using the inter-compartment equilibrium principle. It is understood that other pharmacokinetic models or calculation methods can also be used to obtain the dynamic concentration distribution of chemical components, which is not limited here.

[0031] Based on the above embodiments, as an optional embodiment, in step 102: calculating the concentration values ​​of the corresponding chemical components at different time sampling points based on the molecular weight and lipid-water partition coefficient of each chemical component, this step may further include the following steps: Step 201: Obtain the time sampling interval and total sampling duration, and generate multiple time sampling point sequences based on the time sampling interval; for each chemical component, determine the corresponding drug metabolism rate parameter based on the molecular weight of the chemical component, and determine the corresponding tissue distribution characteristic parameter based on the lipid-water partition coefficient.

[0032] Among them, the time sampling interval represents the time difference between two adjacent sampling time points; the total sampling duration refers to the entire observation time from start to finish; the time sampling point sequence is used to represent a set of multiple time nodes arranged at fixed intervals; the drug metabolism rate parameter represents the rate at which chemical components are transformed and cleared in the body; the tissue distribution characteristic parameter is used to represent the distribution law of chemical components in various tissues; the molecular weight refers to the relative molecular mass of chemical components; and the lipid-water partition coefficient represents the distribution ratio of chemical components in lipid-soluble and water-soluble media.

[0033] Specifically, this step is performed after acquiring the molecular data of traditional Chinese medicine (TCM) to determine the time observation points and establish the basic kinetic parameters of the chemical components. First, the total sampling duration is determined based on the drug's metabolic patterns in vivo, typically spanning 3-5 drug half-lives. Then, the time sampling interval is set, with shorter intervals (e.g., 15 minutes) used during the drug absorption and distribution phase and longer intervals (e.g., 2 hours) used during the drug clearance phase, generating a complete time sampling sequence. For each chemical component, metabolic rate parameters are calculated using its molecular weight, establishing a quantitative relationship between molecular weight and metabolic rate. Simultaneously, tissue distribution characteristic parameters are determined using the lipid-water partition coefficient, constructing a correspondence between the partition coefficient and tissue affinity.

[0034] In some embodiments, the generation of sampling time series and the determination of kinetic parameters can be achieved in several ways: Optionally, firstly, the specific value of the drug's half-life is determined experimentally, and the total sampling time is set to four times this value; then, sampling is set to occur every 15 minutes for the first 2 hours, every 30 minutes for 2-6 hours, and every 2 hours after 6 hours; finally, the metabolic parameter values ​​corresponding to the molecular weight are calculated, and the partition coefficient of each tissue is calculated using the lipid-water partition coefficient. Optionally, firstly, the time point at which the drug reaches its maximum concentration is determined by blood drug concentration measurement, and the total sampling time is set to five times this time; then, the initial sampling interval is set to 10 minutes, and subsequent intervals are increased by 1.5 times; finally, the metabolic rate is calculated based on the molecular weight, and the tissue distribution parameters are calculated using the lipid-water partition coefficient. It is understood that other experimental methods or calculation methods can also be used to determine the time series and kinetic parameters, which are not limited here.

[0035] Step 202: Calculate the theoretical distribution concentration of each chemical component at each time sampling point by combining the drug metabolism rate parameters and tissue distribution characteristic parameters of each chemical component.

[0036] Among them, the theoretical distribution concentration represents the content value of the chemical component at a specific time point obtained through calculation; the drug metabolism rate parameter refers to the kinetic index describing the clearance rate of the chemical component; and the tissue distribution characteristic parameter is used to represent the distribution ratio of the chemical component in different tissues.

[0037] Specifically, this step is performed after determining the time series and kinetic parameters to calculate the theoretical concentration distribution of the chemical component over time. The initial concentration is obtained by dividing the initial dose by the volume of distribution in vivo, and then the concentration change at each time point is calculated using metabolic rate parameters. Simultaneously, the distribution of the chemical component in different tissues is determined based on tissue distribution characteristics. By calculating the concentration values ​​at each time point, a complete concentration-time pattern is obtained, reflecting the dynamic distribution characteristics of the chemical component in vivo.

[0038] In some embodiments, the theoretical distribution concentration can be calculated in several ways: Optionally, the initial concentration is obtained by first calculating the ratio of the administered dose to the distribution volume; then, the amount of drug remaining in the plasma at each time point is calculated using metabolic rate parameters; finally, the actual drug content in the target tissue is calculated using tissue distribution coefficients. Optionally, the drug transport rates between blood and each tissue are calculated separately; then, the changes in drug concentration in the blood are determined based on the transport amounts at each time point; finally, the drug distribution in the target tissue is calculated using tissue distribution characteristic parameters. It is understood that other calculation methods can also be used to obtain the theoretical distribution concentration, and this is not limited here.

[0039] Step 203: Based on the physiological characteristic parameters of the target tissue and organ sites, the theoretical distribution concentration of each chemical component is modified for site specificity to obtain the concentration values ​​of each chemical component at different sampling points at different times.

[0040] Among them, physiological characteristic parameters represent the characteristic indicators of the target tissues and organs, including blood flow, pH value, protein content, etc.; site-specific correction refers to adjusting the theoretical concentration according to the specificity of the target site; the concentration value is used to represent the actual drug content level after correction.

[0041] Specifically, this step is performed after obtaining the theoretical distribution concentration and is used to correct the calculation results under actual conditions. First, the physiological characteristics of the target tissue / organ are measured, including tissue blood flow, local pH value, and tissue protein content. Then, the influence of these physiological characteristics on drug distribution is calculated, obtaining the corresponding correction coefficient. Finally, the theoretical distribution concentration is multiplied by the correction coefficient to obtain a concentration value that more closely reflects reality.

[0042] In some embodiments, site-specific correction can be achieved in several ways: Optionally, firstly, tissue blood flow is measured and the perfusion coefficient is calculated; then, the local pH value is measured and the ionization correction coefficient is calculated; finally, the effective concentration coefficient is calculated using the tissue protein binding rate, and these coefficients are multiplied to obtain the final correction result. Optionally, firstly, standard physiological parameters of the target tissue are collected; then, the influence coefficient of each parameter is calculated separately; finally, all influence coefficients are combined to obtain the total correction coefficient, which is used to correct the theoretical concentration. It is understood that other correction methods can also be used to adjust the concentration value, and this is not limited here.

[0043] Step 103: Based on the concentration values ​​of each chemical component at different time sampling points and the molecular-target binding constant of each chemical component, calculate the effective effect intensity of the target chemical component on the target protein at different time sampling points.

[0044] Among them, chemical components refer to the bioactive molecules in traditional Chinese medicine compound prescriptions; concentration values ​​refer to the content level of chemical components at a specific time point; molecule-target binding constant is used to represent the affinity of chemical components for binding to target proteins; target chemical components refer to the screened chemical components with significant target effects; target proteins refer to the specific protein molecules that the drug acts on; and effective effect intensity is used to represent the actual degree of influence of chemical components on target proteins.

[0045] Specifically, this step is performed after obtaining the concentration values ​​of the chemical components to determine the interaction between the chemical components and the target protein. First, binding constant data for each chemical component and the target protein are collected; these data reflect the affinity between the molecule and the target. Then, the concentration values ​​at each time point are numerically calculated against the corresponding binding constants to obtain the degree of binding between the chemical component and the target. Chemical components with high binding degrees are identified as target chemical components, and their interaction strength with the target protein at each time point is calculated. The calculation of interaction strength needs to consider the product effect of the concentration value and the binding constant, as well as the specificity of the chemical component and the response characteristics of the target.

[0046] In some embodiments, the effective effect intensity can be calculated in several ways: Optionally, firstly, the binding constant of each chemical component to the target protein is determined through thermodynamic experiments; then, the concentration value is multiplied by the binding constant to obtain a preliminary binding index; finally, target chemical components are screened based on the magnitude of the binding index, and their effect intensity on the target is calculated. Optionally, firstly, a structural correspondence between chemical components and the target is established; then, the degree of specificity of the effect is determined using binding site analysis; finally, the actual effect is calculated by combining concentration data. It is understood that other methods can also be used to calculate the effect intensity of chemical components on the target, and this is not limited here.

[0047] Based on the above embodiments, as an optional embodiment, in step 103: calculating the effective effect intensity of the target chemical component on the target protein at different time sampling points based on the concentration values ​​of each chemical component at different time sampling points and the molecular-target binding constant of each chemical component, this step may further include the following steps: Step 301: Obtain the distribution density and types of target proteins within the target tissue and organ sites; for each chemical component, extract the corresponding molecular-target binding constant, and determine the binding affinity level between the chemical component and the target protein based on the molecular-target binding constant and the types of target proteins.

[0048] Among them, target tissue and organ sites indicate the specific tissue sites where the drug exerts its effects; target protein distribution density refers to the number of target proteins per unit volume; target protein types are used to indicate the types of target proteins with different functions; molecular-target binding constant indicates the binding ability of chemical components to target proteins; and binding affinity level is used to indicate the strength of the binding ability of chemical components to target proteins.

[0049] Specifically, this step is performed after the target tissue / organ site is identified, and is used to obtain basic information about the target proteins and determine the functional characteristics of the chemical components. The distribution density of target proteins within the target tissue / organ site is determined using proteomics analysis methods, and the content of target proteins per unit volume is calculated; simultaneously, the types of target proteins present are identified, and a target protein dataset is established. For each chemical component, its binding constant data with various target proteins are extracted, and binding affinity levels are classified according to the magnitude of the binding constant; the larger the binding constant, the higher the binding affinity level. Binding affinity levels are divided into multiple grades, each corresponding to a specific range of binding constant values.

[0050] In some embodiments, target information acquisition and affinity level determination can be achieved in multiple ways: Optionally, the spatial distribution of target proteins can be determined first by immunohistochemistry; then, the specific types of target proteins can be determined using mass spectrometry; finally, the binding constants of chemical components can be determined and affinity levels can be classified by combining literature data. Optionally, the target content can be determined first by protein quantification techniques; then, the target types can be determined using a protein classification database; finally, the binding constants can be determined and affinity levels can be determined by thermodynamic experiments. It is understood that other analytical methods can also be used to acquire target information and determine affinity levels, and this is not limited here.

[0051] Step 302: At each time sampling point, the concentration values ​​of each chemical component are matched with the distribution density of the target protein to calculate the molecular-target binding saturation of the corresponding chemical component at the time sampling point.

[0052] Among them, time sampling points represent specific time nodes of observation and recording; concentration values ​​refer to the content level of chemical components; molecular-target binding saturation is used to represent the degree of binding between chemical components and target proteins; matching calculation refers to numerical calculation of concentration and density data.

[0053] Specifically, this step is performed after obtaining target distribution information to calculate the binding of chemical components to target proteins. At each time sampling point, the actual concentration of the chemical component is acquired, and the distribution density of the target protein at that time point is determined. The molecule-target binding saturation is obtained by calculating the ratio of the number of chemical component molecules to the number of target proteins. The calculation process requires first converting the concentration value into the number of molecules, then comparing it with the number of target proteins, ultimately obtaining a saturation value between 0 and 1. The closer the value is to 1, the higher the degree of binding.

[0054] In some embodiments, the molecular-target binding saturation can be calculated in several ways: Optionally, the concentration units of the chemical components are first converted to molar concentration; then the number of molecules and the number of target sites per unit volume are calculated; finally, the binding saturation is calculated by the ratio of the two. Optionally, the total volume of the target tissue is first determined; then the total number of chemical components and the total number of target sites are calculated separately; finally, the binding saturation is obtained through normalization. It is understood that other calculation methods can also be used to determine the binding saturation, and this is not limited here.

[0055] Based on the above embodiments, as an optional embodiment, in step 302: matching the concentration values ​​of each chemical component with the distribution density of the target protein to obtain the molecular-target binding saturation of the corresponding chemical component at the time sampling point, this step may further include the following steps: Step 312: Obtain the total volume parameters of the target tissue / organ site; based on the total volume parameters and the distribution density of the target protein, calculate the total amount of target protein within the target tissue / organ site.

[0056] Among them, the total volume parameter represents the spatial volume of the target tissue or organ; the target protein distribution density refers to the number of target proteins per unit volume; the total target protein count is used to represent the total number of all target proteins in the target tissue or organ; and the target tissue or organ site represents the specific tissue site where the drug acts.

[0057] Specifically, this step is performed after obtaining the target protein distribution density, and is used to calculate the absolute quantity of target proteins. Volume data of the target tissue / organ is obtained through anatomical measurements or imaging analysis to determine the total volume parameter value. The obtained total volume parameter is multiplied by the known target protein distribution density to obtain the total amount of target proteins within the target tissue / organ site. This calculation needs to consider the actual shape and structural characteristics of the tissue / organ, and the total volume parameter needs to be corrected accordingly to ensure the accuracy of the calculation results.

[0058] In some embodiments, the total amount of target proteins can be calculated in several ways: Optionally, the geometric dimensions of the tissue or organ are first measured using three-dimensional reconstruction technology; then the actual volume is calculated based on the measurement results; finally, the volume is multiplied by the target protein density to obtain the total amount. Optionally, the local volume is first measured using tissue sections; then the total volume is calculated using an integration method; finally, the total protein amount is calculated by combining the target density data. It is understood that other methods can also be used to calculate the total amount of target proteins, and this is not limited here.

[0059] Step 322: For each time sampling point, calculate the total number of molecules of each chemical component in the target tissue or organ site based on the concentration values ​​and total volume parameters of each chemical component.

[0060] Among them, the time sampling point represents the specific time node of the observation record; the concentration value refers to the content level of the chemical component; and the total number of molecules is used to represent the absolute number of molecules of the chemical component.

[0061] Specifically, this step is performed after the total volume parameter is determined, and it is used to calculate the actual number of molecules of the chemical components. For each time sampling point, the concentration value of each chemical component is obtained. The concentration value is multiplied by the total volume parameter, and unit conversion is performed using Avogadro's constant to obtain the total number of molecules of the chemical component. This calculation process needs to ensure unit consistency; typically, the concentration is converted to molar concentration before calculation.

[0062] In some embodiments, the total number of molecules can be calculated in several ways: Optionally, the concentration unit is first converted to moles per liter; then multiplied by the tissue volume to obtain the amount of substance; finally, it is converted to the number of molecules using Avogadro's constant. Optionally, the conversion relationship between concentration and number of molecules is first established; then the number of molecules per unit volume is calculated; finally, it is multiplied by the total volume to obtain the total number of molecules. It is understood that other methods can also be used to calculate the total number of molecules, and this is not limited here.

[0063] Step 332: Normalize the total number of molecules of each chemical component to the total amount of target protein and calculate the ratio to obtain the molecular-target binding saturation of each chemical component.

[0064] Among them, normalization means converting the data to a uniform numerical range; molecular-target binding saturation is used to represent the degree of binding between chemical components and target proteins; ratio calculation refers to the result of division between two values; total number of molecules represents the absolute number of molecules of chemical components; total number of target proteins represents the total number of all target proteins in the target tissue or organ.

[0065] Specifically, this step is performed after obtaining the total number of molecules and the total amount of target protein, and is used to calculate the binding saturation of chemical components with target proteins. For each chemical component, its total number of molecules and the total amount of target protein are first normalized by dividing them by their respective maximum values, so that the data range is controlled between 0 and 1. Then, the ratio of the normalized total number of molecules to the total amount of target protein is calculated to obtain the molecule-target binding saturation. This saturation value is also between 0 and 1, with a higher value indicating a higher degree of binding.

[0066] In some embodiments, binding saturation can be calculated in several ways: Optionally, first, normalize the total number of chemical components by dividing it by the maximum number of molecules among all chemical components; then, normalize the total amount of target protein by dividing it by the theoretical maximum binding amount; finally, calculate the ratio of the two normalized values ​​to obtain the binding saturation. Optionally, first, calculate the quantity of chemical components and target protein per unit volume separately; then, convert both values ​​to relative percentages; finally, determine the binding saturation by calculating the ratio. It is understood that other calculation methods can also be used to determine binding saturation, and this is not limited here.

[0067] Step 303: Identify the target chemical component in each chemical component, and calculate the effective intensity of the target chemical component on the target protein at each time sampling point based on the binding affinity level of the target chemical component and the molecular-target binding saturation corresponding to each time sampling point.

[0068] Among them, the target chemical component refers to the chemical component with significant target effect screened out; the binding affinity level refers to the strength of the chemical component's ability to bind to the target protein; and the effective action intensity is used to indicate the actual degree of influence of the chemical component on the target protein.

[0069] Specifically, this step is performed after obtaining the binding saturation level and is used to calculate the actual effect of the chemical components. Based on a pre-set affinity level threshold, chemical components with higher binding affinity levels are selected as target chemical components. For each target chemical component, its binding affinity level is combined with the binding saturation data to calculate the effective action intensity against the target protein at each time point. During the calculation, when the binding saturation exceeds a specific threshold, the binding affinity level is used as the baseline action intensity and adjusted according to the actual binding saturation; when the binding saturation is below the threshold, the action intensity is set to the minimum value.

[0070] In some embodiments, the effective interaction strength can be calculated in several ways: Optionally, firstly, an affinity level threshold is set to screen target chemical components; then, the binding affinity level is converted into a baseline interaction strength; finally, the baseline strength is corrected based on the binding saturation. Optionally, firstly, important chemical components are determined based on the target type; then, the binding efficiency at each time point is calculated; finally, the interaction strength is calculated by comprehensively considering both affinity and saturation. It is understood that other methods can also be used to calculate the effective interaction strength, and this is not limited here.

[0071] Based on the above embodiments, as an optional embodiment, in step 303: determining the target chemical component in each chemical component, and calculating the effective effect intensity of the target chemical component on the target protein at each time sampling point based on the binding affinity level of the target chemical component and the molecular-target binding saturation corresponding to each time sampling point, this step may further include the following steps: Step 313: Among the various chemical components, select those with a binding affinity level greater than or equal to the affinity level threshold as target chemical components.

[0072] Among them, affinity grade represents the strength of the binding ability of chemical components to target proteins; affinity grade threshold refers to the standard value for screening target chemical components; target chemical components are used to represent the screened chemical components with significant target effects.

[0073] Specifically, this step is performed after determining the binding affinity level and is used to screen for chemical components with strong binding ability. An affinity level threshold is set as a screening criterion, and the binding affinity level of each chemical component is compared to this threshold. When the binding affinity level of a chemical component is greater than or equal to the set threshold, that chemical component is identified as the target chemical component. The setting of the affinity level threshold is based on the requirements of target action, and is usually selected as the lowest affinity level value that ensures effective pharmacological action.

[0074] In some embodiments, the screening of target chemical components can be achieved in several ways: Optionally, chemical components are first sorted from high to low binding affinity; then, affinity thresholds are determined based on pharmacological data; finally, target chemical components are screened out by comparison. Optionally, a baseline affinity level is first set based on the binding characteristics of the target protein; then, the fold relationship of the screening threshold is determined; finally, target chemical components that meet the requirements are screened out. It is understood that other methods can also be used to screen target chemical components, and this is not limited here.

[0075] Step 323: Determine whether the molecular-target binding saturation of the target chemical component at each time sampling point exceeds the saturation threshold; when the molecular-target binding saturation at the time sampling point exceeds the saturation threshold, use the binding affinity level of the target chemical component as the baseline interaction strength, and adjust the baseline interaction strength according to the molecular-target binding saturation by a preset ratio to obtain the effective interaction strength of the target chemical component on the target protein; when the molecular-target binding saturation value at the time sampling point does not exceed the saturation threshold, set the effective interaction strength to the preset minimum interaction strength.

[0076] Among them, molecular-target binding saturation represents the degree of binding between chemical components and target proteins; saturation threshold refers to the standard value for judging the degree of binding; baseline effect intensity is used to represent the basic effect of chemical components; preset ratio represents the adjustment coefficient of effect intensity; effective effect intensity is used to represent the actual degree of influence of chemical components on target proteins; minimum effect intensity refers to the minimum limit of effect.

[0077] Specifically, this step is performed after the target chemical component is screened, and it is used to calculate the actual effect of the chemical component. For each time sampling point, it is first determined whether the molecular-target binding saturation of the target chemical component exceeds a set saturation threshold. When the binding saturation exceeds the threshold, the binding affinity level of the chemical component is set as the baseline effect strength. Then, the baseline effect strength is adjusted according to the actual binding saturation. During adjustment, the binding saturation is multiplied by the baseline effect strength as a coefficient to obtain the final effective effect strength. When the binding saturation does not exceed the threshold, it indicates that the chemical component's binding with the target is insufficient. In this case, the effective effect strength is set to a pre-determined minimum effect strength value.

[0078] In some embodiments, the effective action intensity can be calculated in several ways: Optionally, firstly, a saturation threshold is set to 0.5; then, for cases exceeding the threshold, the binding saturation is multiplied by the baseline action intensity to obtain the effective action intensity; finally, for cases not exceeding the threshold, the effective action intensity is set to a minimum value of 0.1. Optionally, firstly, a saturation threshold is set based on target characteristics; then, a functional relationship between the baseline action intensity and binding saturation is established; finally, the effective action intensity is calculated using this function. It is understood that other methods can also be used to calculate the effective action intensity, and this is not limited here.

[0079] Step 104: Fill the effective intensity of the target chemical component into a tensor structure with the target protein as the dimension according to the corresponding time sampling points to obtain the network connectivity tensor.

[0080] Among them, target chemical components refer to the screened chemical components with significant target effects; effective effect intensity refers to the actual degree of influence of chemical components on target proteins; time sampling points are used to represent the specific time nodes of observation records; target proteins refer to the specific protein molecules of drug action; tensor structure refers to the organization form of multidimensional data; network connectivity tensor is used to represent the multidimensional data set of the relationship between chemical components and target proteins.

[0081] Specifically, this step is performed after the effective intensity is calculated, and it is used to construct a data structure linking chemical components and target proteins. First, a tensor structure is established with the target protein as the dimension, including the target protein type, time dimension, and intensity dimension. For each target chemical component, its effective intensity values ​​at different time sampling points are filled into the corresponding tensor positions. During the filling process, it is necessary to ensure the accuracy of the data correspondence, that is, each value is filled into the correct target protein position and time position. After filling, a complete network connectivity tensor is formed, which clearly shows the effect of the target chemical component on different target proteins over time.

[0082] In some embodiments, the network connectivity tensor can be constructed in several ways: Optionally, a three-dimensional array structure can be created first to store the data; then, the target protein number can be used as the first-dimensional index; finally, the action intensity can be filled into the corresponding positions in chronological order. Optionally, a sparse matrix storage framework can be constructed first; then, a mapping relationship between target proteins and time points can be established; finally, the effective action intensity can be filled into the corresponding matrix positions. It is understood that other data structures can also be used to construct the network connectivity tensor, and this is not limited here.

[0083] Step 105: Perform spatiotemporal difference calculation on the network connectivity tensor to obtain network topology data, construct the molecular network navigation path graph corresponding to the network topology data, and output the molecular network positioning and navigation results of the target drug prescription in the molecular network navigation path graph.

[0084] Among them, the network connectivity tensor represents a multidimensional data set of the interaction relationship between chemical components and target proteins; spatiotemporal difference calculation refers to the calculation of data changes in time and space dimensions; network topology data is used to represent the structured information of intermolecular interaction relationships; molecular network navigation path diagram represents the visual expression of molecular interaction paths; target drug prescription refers to the drug combination to be analyzed; molecular network localization and navigation results are used to represent the spatial and temporal characteristics of drug molecule interaction.

[0085] Specifically, this step is performed after constructing the network connectivity tensor to analyze the network characteristics of drug molecule interactions. First, spatiotemporal difference calculations are performed on the network connectivity tensor. This calculation includes determining the difference in interaction intensity between adjacent time points and the gradient of interaction intensity between different target points. The network topology data obtained through difference calculations reflects the dynamic changes in molecular interactions. Based on the network topology data, a molecular network navigation path graph is constructed. This path graph uses nodes to represent target proteins, connecting lines to represent molecular interactions, line thickness to represent interaction intensity, and color to represent time series. Important interaction nodes and key interaction paths are marked on the path graph, forming a complete molecular network localization and navigation result.

[0086] In some embodiments, molecular network navigation paths can be constructed in several ways: Optionally, firstly, the rate of change of influence intensity in the time dimension is calculated; then, the difference in influence intensity distribution in the spatial dimension is calculated; finally, a network connection graph is drawn based on the difference results. Optionally, firstly, the network connectivity tensor is numerically normalized; then, graph theory algorithms are applied to analyze the connection relationships between nodes; finally, a weighted directed graph is constructed to represent the influence path. It is understood that other methods can also be used to construct molecular network navigation paths, which are not limited here.

[0087] Based on the above embodiments, as an optional embodiment, step 105: performing spatiotemporal difference calculation on the network connectivity tensor to obtain network topology data, and constructing the molecular network navigation path graph corresponding to the network topology data, may further include the following steps: Step 401: Perform a difference operation on the network connectivity tensor in the time dimension to calculate the rate of change of connection strength between target proteins as network topology data.

[0088] Among them, the network connectivity tensor represents a multidimensional data set of the interaction relationship between chemical components and target proteins; the time dimension refers to the time series of observation records; the difference operation is used to represent the numerical changes between adjacent time points; the connection strength change rate represents the rate of change of the interaction strength between target proteins over time; and the network topology data is used to represent the structured information of the intermolecular interaction relationship.

[0089] Specifically, this step is performed after constructing the network connectivity tensor to calculate the dynamic changes in the interactions between target proteins. Differential calculations are performed on the data from adjacent time points in the network connectivity tensor. This calculation involves subtracting the connection strength from the previous time point from the connection strength at the later time point, and then dividing by the time interval to obtain the rate of change in connection strength. This differential operation is performed on the connection strengths between all target protein pairs, forming a complete dataset of rate of change. This rate of change data constitutes the network topology data, reflecting the dynamic characteristics of the interactions between target proteins.

[0090] In some embodiments, the rate of change of connection strength can be calculated in several ways: Optionally, firstly, connection strength data at adjacent time points are extracted; then, the difference in connection strength is calculated; finally, the difference is divided by the time interval to obtain the rate of change. Optionally, firstly, the connection strength data is smoothed; then, the derivative over the time series is calculated; finally, the rate of change is determined by the derivative value. It is understood that other methods can also be used to calculate the rate of change of connection strength, and this is not limited here.

[0091] Step 402: Based on network topology data, identify target protein pairs whose connectivity change rate exceeds the change rate threshold, and designate the target protein pairs as key network nodes.

[0092] Among them, network topology data represents the structured information of intermolecular interactions; the rate of change threshold is a standard value for identifying key nodes; target protein pairs are used to represent two interacting target proteins; and key network nodes represent target proteins that play an important role in the network.

[0093] Specifically, this step is performed after obtaining the network topology data to identify important relationships within the network. The rate of change in connectivity between each pair of target proteins is compared to a preset threshold. When the rate of change in connectivity between a pair of target proteins exceeds the threshold, this pair is marked as a key network node. The threshold is set based on biological significance to ensure that the identified key network nodes reflect significant biological effects. All target protein pairs are compared to obtain a complete set of key network nodes.

[0094] In some embodiments, key network nodes can be identified in several ways: Optionally, firstly, a change rate threshold is determined based on historical data; then, the change rates of target protein pairs are compared one by one; finally, node pairs exceeding the threshold are selected. Optionally, firstly, statistical analysis is performed on the change rate data; then, a confidence interval is set as a threshold; finally, node pairs with significant changes are identified. It is understood that other methods can also be used to identify key network nodes, which are not limited here.

[0095] Step 403: Determine the connection weights and directions between key network nodes based on their rate of change and direction of change; construct a molecular network navigation path diagram based on the connection weights and directions of the key network nodes.

[0096] Among them, the connection weight represents the strength of the interaction between target proteins; the connection direction is used to represent the direction of the interaction; the molecular network navigation path diagram is a visual network diagram describing the molecular interaction path; the rate of change value represents the degree of change in the interaction strength; and the direction of change is used to represent the trend of the interaction being enhanced or weakened.

[0097] Specifically, this step is performed after identifying key network nodes to construct a visual representation of the molecular interaction network. Connection weights are determined based on the rate of change of the key network nodes; a larger rate of change corresponds to a larger connection weight. The sign of the rate of change determines the connection direction; a positive value indicates enhanced interaction, and a negative value indicates weakened interaction. Based on the determined connection weights and directions, a directed weighted graph-like molecular network navigation path is constructed, where nodes represent target proteins, the thickness of the connecting lines represents weights, and arrows represent directions.

[0098] In some embodiments, the molecular network navigation path diagram can be constructed in several ways: Optionally, the rate of change values ​​are first converted into standardized weight values; then the arrow directions are determined based on the sign of the rate of change; finally, a network diagram is generated using a graphics drawing tool. Optionally, a topological relationship matrix between nodes is first established; then the visual attributes of the connecting lines are designed based on the weights; finally, a hierarchical network diagram is constructed. It is understood that other methods can also be used to construct the molecular network navigation path diagram, which are not limited here.

[0099] Based on the above embodiments, as an optional embodiment, step 403: determining the connection weights and connection directions between key network nodes based on the rate of change and direction of change of key network nodes, this step may further include the following steps: Step 413: Obtain the change rate value and change direction identifier corresponding to any two key network nodes; when the change direction identifiers corresponding to any two key network nodes are the same, calculate the connection weight coefficient based on the product of the change rate values ​​of the two key network nodes, and take the key network node with the larger change rate value as the connection starting point.

[0100] Among them, key network nodes represent target proteins that play an important role in the network; the rate of change value refers to the degree of change in the intensity of the action; the direction of change indicator is used to indicate the trend of the action being enhanced or weakened; the connection weight coefficient represents the strength factor of the relationship between nodes; the connection origin refers to the source node of the action transmission; and the product calculation represents the operation of multiplying two values.

[0101] Specifically, this step is performed after identifying key network nodes and is used to calculate the interaction relationship between nodes changing in the same direction. For any two key network nodes, their rate of change values ​​and direction of change indicators are first obtained. When the direction of change indicators of two nodes are the same, it indicates that they have a synergistic relationship. The connection weight coefficient is obtained by multiplying the rate of change values ​​of the two nodes. The node with the larger rate of change value is identified as the connection starting point, which reflects the dominant direction of the action transmission. The direction of change indicator here can be positive or negative; the same direction of change indicates that both nodes are either strengthening or weakening.

[0102] In some embodiments, the connection relationship between nodes moving in the same direction can be determined in several ways: Optionally, first, the change direction indicators of the nodes are compared to determine whether they are moving in the same direction; then, the product of the change rate values ​​is calculated to obtain the weight coefficient; finally, the starting point is determined by comparing the magnitude of the change rates. Optionally, the change rate values ​​are first standardized; then, the change trend is determined based on the indicators; finally, the connection relationship is determined through numerical calculations. It is understood that other methods can also be used to determine the connection relationship between nodes moving in the same direction, and this is not limited here.

[0103] Step 423: When the change direction indicators of any two key network nodes are opposite, calculate the connection weight coefficient based on the ratio of the change rate values ​​of the two key network nodes, and take the key network node with the change direction in the preset positive direction as the connection starting point.

[0104] Among them, the direction of change indicates the trend of strengthening or weakening of the effect; the rate of change value refers to the degree of change in the intensity of the effect; the ratio calculation is used to represent the division of two values; the connection weight coefficient represents the strength factor of the relationship between nodes; the preset positive direction refers to the specified baseline direction of change; and the connection starting point is used to represent the source node of the effect transmission.

[0105] Specifically, this step is performed after analyzing nodes moving in the same direction to calculate the interaction between nodes changing in opposite directions. When two key network nodes change in opposite directions, it indicates that they have an antagonistic relationship. The connection weight coefficient is obtained by calculating the ratio of the change rates of the two nodes, with the ratio calculated by dividing the change rate with the larger absolute value by the smaller change rate. The node whose change direction is a preset positive direction is identified as the connection starting point; the positive direction is usually defined as the direction of enhanced effect. This calculation method ensures a quantitative expression of antagonistic effects.

[0106] In some embodiments, the determination of the reverse node connection relationship can be achieved in several ways: Optionally, first, the oppositeity of the node change direction is confirmed; then, the ratio of the change rate is calculated as a weight; finally, the starting point is determined according to a preset positive direction. Optionally, first, the sign of the change rate is determined; then, the intensity ratio is obtained through numerical calculation; finally, the initial relationship of the action is determined. It is understood that other methods can also be used to determine the connection relationship of the reverse nodes, which are not limited here.

[0107] Step 433: Multiply the connection weight coefficients by the preset weight benchmark values ​​to obtain the connection weights between key network nodes, and perform connectivity analysis on each connection starting point to determine the connection direction between key network nodes.

[0108] Among them, the connection weight coefficient represents the strength factor of the interaction relationship between nodes; the weight benchmark value refers to the reference value of the standardized weight; the connection weight is used to represent the actual intensity of the interaction; the connection origin represents the source node of the interaction transmission; connectivity analysis refers to the systematic examination of the relationship between nodes; and the connection direction is used to represent the direction of the influence of the interaction.

[0109] Specifically, this step is performed after calculating the weight coefficients to determine the final connectivity relationships between network nodes. The calculated connection weight coefficients are multiplied by a pre-defined weight baseline value to obtain the standardized connection weights. The weight baseline value is set based on biological significance to ensure that the final connection weights have practical reference value. Connectivity analysis is performed on all connection origins, including examining direct and indirect connections between nodes to determine the connection directions between each node. Connectivity analysis needs to consider the hierarchical relationships between nodes and the action propagation paths.

[0110] In some embodiments, the final determination of the connection relationships can be achieved in several ways: Optionally, the weight coefficients are first standardized; then the connectivity relationships between nodes are analyzed; and finally, a complete connection network is constructed. Optionally, the quantification standard of the weights is first determined; then a hierarchical system of nodes is established; and finally, a directed connection graph is formed. It is understood that other methods can also be used to determine the connection relationships between nodes, which are not limited here.

[0111] Reference Figure 2 This application provides a molecular network localization and navigation system for traditional Chinese medicine. The system includes: a data acquisition module, a concentration value determination module, a network connectivity tensor determination module, and a molecular network localization and navigation module, wherein: The data acquisition module is used to acquire traditional Chinese medicine molecular data on the target tissue and organ sites where the target drug prescription acts. The traditional Chinese medicine molecular data includes the molecular weight, lipid-water partition coefficient and molecule-target binding constant of multiple chemical components. The concentration value determination module is used to calculate the concentration values ​​of the corresponding chemical components at different time sampling points based on the molecular weight and lipid-water partition coefficient of each chemical component. The network connectivity tensor determination module is used to calculate the effective intensity of the target chemical component on the target protein at different time sampling points based on the concentration values ​​of each chemical component at different time sampling points and the molecular-target binding constant of each chemical component; and to fill the effective intensity of the target chemical component into a tensor structure with the target protein as the dimension according to the corresponding time sampling points to obtain the network connectivity tensor. The molecular network positioning and navigation module is used to perform spatiotemporal difference calculation on the network connectivity tensor to obtain network topology data, construct the molecular network navigation path graph corresponding to the network topology data, and output the molecular network positioning and navigation result of the target drug prescription in the molecular network navigation path graph.

[0112] Based on the above embodiments, the concentration value determination module is further used to obtain the time sampling interval and the total sampling duration, and generate multiple time sampling point sequences based on the time sampling interval; for each chemical component, the corresponding drug metabolism rate parameter is determined according to the molecular weight of the chemical component, and the corresponding tissue distribution characteristic parameter is determined based on the lipid-water partition coefficient; combining the drug metabolism rate parameter and tissue distribution characteristic parameter of each chemical component, the theoretical distribution concentration of each chemical component at each time sampling point is calculated; based on the physiological characteristic parameters of the target tissue organ site, the theoretical distribution concentration of each chemical component is site-specifically corrected to obtain the concentration values ​​of each chemical component at different time sampling points.

[0113] Based on the above embodiments, the network connectivity tensor determination module is further used to obtain the distribution density and types of target proteins within the target tissue / organ site; for each chemical component, the molecular-target binding constant corresponding to the chemical component is extracted, and based on the molecular-target binding constant and the types of target proteins, the binding affinity level between the chemical component and the target protein is determined; at each time sampling point, the concentration values ​​of each chemical component are matched with the distribution density of the target protein to obtain the molecular-target binding saturation of the corresponding chemical component at the time sampling point; the target chemical component in each chemical component is determined, and based on the binding affinity level of the target chemical component and the molecular-target binding saturation corresponding to each time sampling point, the effective effect intensity of the target chemical component on the target protein at each time sampling point is calculated.

[0114] Based on the above embodiments, the network connectivity tensor determination module is further used to obtain the total volume parameter of the target tissue / organ site; calculate the total amount of target protein within the target tissue / organ site based on the total volume parameter and the distribution density of the target protein; for the time sampling point, calculate the total number of molecules of each chemical component within the target tissue / organ site based on the concentration value of each chemical component and the total volume parameter; normalize the total number of molecules of each chemical component to the total amount of target protein and calculate the ratio to obtain the molecular-target binding saturation corresponding to each chemical component.

[0115] Based on the above embodiments, the network connectivity tensor determination module is further configured to: select chemical components among the chemical components whose binding affinity level is greater than or equal to the affinity level threshold as target chemical components; determine whether the molecular-target binding saturation of the target chemical component at each of the time sampling points exceeds the saturation threshold; when the molecular-target binding saturation at the time sampling point exceeds the saturation threshold, use the binding affinity level of the target chemical component as the baseline action intensity, and adjust the baseline action intensity according to the molecular-target binding saturation by a preset ratio to obtain the effective action intensity of the target chemical component on the target protein; when the molecular-target binding saturation value at the time sampling point does not exceed the saturation threshold, set the effective action intensity to a preset minimum action intensity.

[0116] Based on the above embodiments, the molecular network localization and navigation module is further configured to perform differential operations on the network connectivity tensor in the time dimension to calculate the rate of change of connection strength between the target proteins as network topology data; based on the network topology data, identify target protein pairs whose rate of change of connectivity exceeds the rate of change threshold, and designate the target protein pairs as key network nodes; determine the connection weights and connection directions between the key network nodes according to the rate of change values ​​and direction of change of the key network nodes; and construct a molecular network navigation path graph based on the connection weights and connection directions of the key network nodes.

[0117] Based on the above embodiments, the molecular network positioning and navigation module is further configured to acquire the change rate values ​​and change direction identifiers corresponding to any two key network nodes; when the change direction identifiers corresponding to any two key network nodes are the same, a connection weight coefficient is calculated based on the product of the change rate values ​​of the two key network nodes, and the key network node with the larger change rate value is taken as the connection starting point; when the change direction identifiers of any two key network nodes are opposite, a connection weight coefficient is calculated based on the ratio of the change rate values ​​of the two key network nodes, and the key network node with the change direction being a preset positive direction is taken as the connection starting point; the connection weight coefficient is multiplied by a preset weight benchmark value to obtain the connection weight between the key network nodes, and connectivity analysis is performed on each connection starting point to determine the connection direction between the key network nodes.

[0118] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided above belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0119] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0120] The communication bus 302 is used to enable communication between these components.

[0121] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0122] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0123] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0124] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a molecular network positioning and navigation method for traditional Chinese medicine.

[0125] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a traditional Chinese medicine molecular network positioning and navigation method. When executed by one or more processors 301, the electronic device 300 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0126] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0127] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0131] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical disclosure.

[0132] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A traditional Chinese medicine molecular network positioning and navigation method, characterized in that, The method comprises the following steps: Obtain Chinese medicine molecular data of a target drug prescription acting on a target tissue or organ site, wherein the Chinese medicine molecular data comprises molecular weights, lipid-water partition coefficients and molecular-target protein binding constants of multiple chemical components; Calculate concentration values of the corresponding chemical components at different time sampling points based on the molecular weights and lipid-water partition coefficients of each chemical component; Calculate effective action strengths of target chemical components on target proteins at different time sampling points according to the concentration values of each chemical component at different time sampling points and the molecular-target protein binding constants of each chemical component; Fill the effective action strengths of the target chemical components into a tensor structure with target proteins as dimensions according to the corresponding time sampling points to obtain a network connectivity tensor; Perform spatiotemporal difference calculation on the network connectivity tensor to obtain network topology data, construct a molecular network navigation path diagram corresponding to the network topology data, and output a molecular network positioning navigation result of the target drug prescription in the molecular network navigation path diagram.

2. The Chinese medicine molecular network positioning navigation method according to claim 1, characterized in that, The method of calculating concentration values of the corresponding chemical components at different time sampling points based on the molecular weights and lipid-water partition coefficients of each chemical component comprises the following steps: Obtain a time sampling interval and a total sampling duration, and generate multiple time sampling point sequences based on the time sampling interval; For each chemical component, determine a corresponding drug metabolism rate parameter according to the molecular weight of the chemical component, and determine a corresponding tissue distribution characteristic parameter based on the lipid-water partition coefficient; Combine the drug metabolism rate parameter and the tissue distribution characteristic parameter of each chemical component to calculate the theoretical distribution concentration of each chemical component at each time sampling point; Based on the physiological characteristic parameters of the target tissue or organ site, perform site-specific correction on the theoretical distribution concentration of each chemical component to obtain the concentration values of each chemical component at different time sampling points.

3. The Chinese medicine molecular network positioning navigation method according to claim 1, characterized in that, The method of calculating effective action strengths of target chemical components on target proteins at different time sampling points according to the concentration values of each chemical component at different time sampling points and the molecular-target protein binding constants of each chemical component comprises the following steps: Obtain the distribution density and species of target proteins in the target tissue or organ site; For each chemical component, extract the molecular-target protein binding constant corresponding to the chemical component, and determine the binding affinity level of the chemical component and the target protein based on the molecular-target protein binding constant and the species of the target protein; At each time sampling point, match and calculate the concentration value of each chemical component with the distribution density of the target protein to obtain the molecular-target protein binding saturation degree of the corresponding chemical component at the time sampling point; Determine a target chemical component from each chemical component, and calculate the effective action strength of the target chemical component on the target protein at each time sampling point based on the binding affinity level of the target chemical component and the corresponding molecular-target protein binding saturation degree at each time sampling point.

4. The Chinese medicine molecular network positioning navigation method according to claim 3, characterized in that, The concentration value of each chemical component is matched with the distribution density of the target protein to obtain a molecular-target binding saturation degree of the corresponding chemical component at the time sampling point, including: Obtaining the total volume parameter of the target tissue organ site; Based on the total volume parameter and the distribution density of the target protein, the total amount of target protein in the target tissue organ site is calculated; For the time sampling point, based on the concentration value of each chemical component and the total volume parameter, the total number of molecules of each chemical component in the target tissue organ site is calculated; The total number of molecules of each chemical component is normalized and ratio calculated with the total amount of target protein to obtain the corresponding molecular-target binding saturation degree of each chemical component.

5. The Chinese medicine molecular network positioning navigation method according to claim 3, characterized in that, The target chemical component is determined from each of the chemical components, and the effective action strength of the target chemical component on the target protein at each time sampling point is calculated based on the binding affinity level of the target chemical component and the corresponding molecular-target binding saturation degree at each time sampling point, including: In each of the chemical components, the chemical component with a binding affinity level greater than or equal to an affinity level threshold is selected as a target chemical component; Determine whether the molecular-target binding saturation degree of the target chemical component at each time sampling point exceeds a saturation threshold; When the molecular-target binding saturation degree at the time sampling point exceeds the saturation threshold, the binding affinity level corresponding to the target chemical component is taken as a reference action strength, and the reference action strength is adjusted by a preset proportion according to the molecular-target binding saturation degree, to obtain the effective action strength of the target chemical component on the target protein; When the molecular-target binding saturation degree at the time sampling point does not exceed the saturation threshold, the effective action strength is set to a preset minimum action strength.

6. The Chinese medicine molecular network positioning navigation method according to claim 1, characterized in that, The network connectivity tensor is calculated by space-time difference to obtain network topology data, and a molecular network navigation path graph corresponding to the network topology data is constructed, including: Difference operation is performed on the network connectivity tensor in the time dimension to calculate the connection strength change rate between the target proteins as network topology data; Based on the network topology data, identify the target protein pairs with a connection degree change rate exceeding a change rate threshold, and take the target protein pairs as key network nodes; According to the change rate value and change direction of the key network nodes, determine the connection weight and connection direction between the key network nodes; Based on the connection weight and connection direction of the key network nodes, a molecular network navigation path graph is constructed.

7. The traditional Chinese medicine molecular network positioning navigation method according to claim 6, characterized in that, The connection weight and connection direction between the key network nodes are determined according to the change rate value and change direction of the key network nodes, including: Obtaining the change rate value and change direction identifier corresponding to any two key network nodes; When the change direction identifiers of any two of the key network nodes are the same, a connection weight coefficient is calculated based on the product of the change rate values of the two key network nodes, and the key network node with the larger change rate value is taken as the connection starting point; When the change direction identifiers of any two of the key network nodes are opposite, a connection weight coefficient is calculated based on the ratio of the change rate values of the two key network nodes, and the key network node with the preset positive direction is taken as the connection starting point; The connection weight coefficient is multiplied by a preset weight reference value to obtain the connection weight between the key network nodes, and connectivity analysis is performed on each connection starting point to determine the connection direction between the key network nodes.

8. A traditional Chinese medicine molecular network positioning navigation system, characterized in that, The system comprises: A data acquisition module configured to acquire traditional Chinese medicine molecular data of a target drug prescription acting on a target tissue or organ site, wherein the traditional Chinese medicine molecular data comprises molecular weights, lipid-water partition coefficients, and molecule-target binding constants of a plurality of chemical components; A concentration value determination module configured to calculate concentration values of the corresponding chemical components at different time sampling points based on the molecular weights and lipid-water partition coefficients of the chemical components; A network connectivity tensor determination module configured to calculate effective action strengths of target chemical components on target proteins at different time sampling points according to the concentration values of the chemical components at the different time sampling points and the molecule-target binding constants of the chemical components; fill the effective action strengths of the target chemical components into a tensor structure with the target proteins as dimensions according to the corresponding time sampling points to obtain a network connectivity tensor; A molecular network positioning and navigation module configured to perform spatiotemporal difference calculation on the network connectivity tensor to obtain network topology structure data, construct a molecular network navigation path diagram corresponding to the network topology structure data, and output a molecular network positioning and navigation result of the target drug prescription in the molecular network navigation path diagram.

9. An electronic device, comprising: An electronic device comprising a processor, a memory, a user interface, and a network interface, wherein the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform a traditional Chinese medicine molecular network positioning and navigation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform a traditional Chinese medicine molecular network positioning and navigation method according to any one of claims 1-7.

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