Intelligent research and development platform, system and method for network medicine of intelligent medical science

Abstract

The invention relates to the technical field of biological medicine research and development, and discloses an intelligent research and development platform, system and method for network medicine of intelligent medicine. According to the method, an AI analysis module is utilized to construct a dynamic syndrome biological characteristic network based on clinical time sequence data; in combination with a time sequence dynamic syndrome biological simulation body, an evolutionary algorithm is executed through a data processing and control unit, and time sequence application and real-time monitoring are carried out on candidate network drug formulas. And the system evaluates the goodness of fit between a biological state path generated by each formula on the simulation body and an ideal recovery path based on a preset time sequence fitness function, and performs iterative evolution on a formula population through operations such as selection, crossover and variation until an optimal dynamic administration scheme is found. According to the method, the problem of network drug sequential optimization design is solved, an automatic closed-loop research and development system from data modeling to physical verification is constructed, and the efficiency and accuracy of drug research and development are improved.

Description

Technical Field

[0001] This invention relates to the field of biomedical research and development technology, and in particular to a smart medical network drug research and development platform, system and method. Background Technology

[0002] In modern drug development, particularly for complex diseases caused by multi-factor, multi-target interactions (such as the syndromes described in Traditional Chinese Medicine), computational models for drug formulation screening have become an important technique. Researchers analyze massive amounts of biomedical data to construct biomolecular interaction network models of diseases and perform virtual screening on these models to predict effective drug component combinations. Simultaneously, to verify the biological effects of screened candidate drugs, advanced in vitro simulation technologies such as organ-on-a-chip have also been applied, enabling high-precision replication of the physiological or pathological microenvironment of local human tissues in vitro.

[0003] However, existing technologies have several limitations in integrating the above-mentioned computational screening and in vitro validation processes.

[0004] First, regarding the design goals of drug formulations, existing computational models and screening methods mostly aim to obtain a static formulation with fixed components and dosages. While this approach simplifies the optimization problem to some extent, it presupposes that the biological network of the disease remains stable throughout its course. In reality, the pathophysiological process of complex diseases is a dynamic evolutionary process, with changes in the molecular network state and key regulatory nodes at different time stages. Therefore, a drug combination that exhibits optimal efficacy in the early stages of a disease may not be the optimal choice in the middle or later stages, and may even produce unintended effects. Current technologies lack effective means for time-series design of drug formulations, making it difficult to generate dynamic dosing regimens that accurately match the dynamic evolution of the disease.

[0005] Secondly, in terms of the execution of the R&D process, computational screening and biological validation are typically two separate and poorly connected stages. Candidate formulations generated by computational models require manual processing and decision-making before being transferred to independent in vitro experimental procedures for validation. This process not only interrupts the data flow and prolongs the R&D cycle, but also, due to the throughput limitations of in vitro experiments, only a few candidate formulations can be validated at a time. The disconnect between computational optimization and physical testing prevents the algorithm from using real-time biological data generated during experiments to adjust its optimization direction in real time, thus limiting the throughput and speed of the entire drug formulation iteration and screening process.

[0006] Finally, regarding the accumulation and utilization of R&D knowledge, existing technical processes generally lack a mechanism for systematically feeding experimental validation results back to the initial data model. The rich information contained in the experimental validation data generated from a complete R&D activity, especially data on poorly effective or failed candidate formulations, is not structured for automated correction and enhancement of the initial biological network model or clinical database. This makes it difficult for subsequent R&D activities to obtain systematic starting point gains from completed experiments, resulting in insufficient continuity and reusability of knowledge accumulation, and preventing iterative improvements in the overall performance of the R&D system. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent R&D platform, system and method for smart medicine and pharmaceuticals, which aims to solve the technical problems of insufficient attention to the time dimension and dynamic process of drug action in the existing R&D process of traditional Chinese medicine, as well as the poor connection and low efficiency of the various links in the R&D process.

[0008] The first aspect of this invention provides an intelligent medical and pharmaceutical R&D platform, comprising: A TCM clinical experience database module is configured to store and process TCM clinical case data and multi-omics data that include a time dimension; An AI analysis module, connected to the TCM clinical experience database module, is configured to construct a dynamic syndrome biological feature network based on the TCM clinical case data and multi-omics data to describe the evolution of TCM syndrome states over time. A network-based drug generation module; A virtual reality and formulation module; A closed-loop feedback and knowledge base growth module.

[0009] The network drug generation module is configured to: initialize a formulation population consisting of multiple candidate network drug formulations, wherein each candidate network drug formulation is defined as a pair list of time periods and drug combinations; and iteratively evolve the formulation population based on the fitness assessment of the virtual therapeutic effects of each candidate network drug formulation in the formulation population on the dynamic syndrome biological feature network to generate the optimal network drug formulation.

[0010] The virtual reality and formulation module is configured to: receive the optimal network drug formulation, create a digital drug prototype of the optimal network drug formulation and perform virtual preclinical evaluation to generate virtual validation data; and guide the generation of physical formulations and receive physical validation data generated by testing the physical formulations on a biological in vitro model.

[0011] The closed-loop feedback and knowledge base growth module is configured to send the optimal network drug formula, the virtual verification data, and the physical verification data back to the traditional Chinese medicine clinical experience database module to complete the iterative enhancement of R&D knowledge.

[0012] In one specific embodiment, the AI ​​analysis module is configured to: employ a hybrid model of graph neural networks and long short-term memory networks to perform time-series modeling on the multi-omics data. This time-series modeling utilizes the temporal information and TCM syndrome labels contained in the TCM clinical case data as constraints to construct the dynamic syndrome biological characteristic network. This network mathematically defines the core biomolecular nodes constituting a specific TCM syndrome, the interaction topology between nodes, and the evolution of the activity and weights of these nodes over time.

[0013] Preferably, the iterative evolution of the formulation population specifically includes: performing selection, crossover, and mutation operations on the network drug formulations in the formulation population based on the fitness assessment results. The selection operation is used to retain candidate network drug formulations with high fitness; the crossover operation is used to combine different time periods or drug combination portions of two candidate network drug formulations to generate new progeny formulations; the mutation operation is used to randomly adjust the duration of a certain time period of a single candidate network drug formulation or the component concentration of a certain drug combination.

[0014] Furthermore, the virtual verification data specifically refers to the evaluation data of the digital drug prototype, and the biological in vitro model specifically refers to a time-series dynamic syndrome biological simulator, while the physical verification data specifically refers to the multidimensional sensing data obtained by testing the physical preparation on the time-series dynamic syndrome biological simulator.

[0015] A second aspect of this invention provides an intelligent medical and pharmaceutical R&D system, comprising: A time-series dynamic syndrome biological simulator includes an organ-on-a-chip for three-dimensional cell co-culture and a multi-dimensional sensor array for real-time monitoring of the internal biological state of the time-series dynamic syndrome biological simulator. A timing control and rhythm simulation unit, connected to the timing dynamic syndrome biological simulator, is configured to programmatically change the culture environment of the timing dynamic syndrome biological simulator to simulate the staged evolution of physiological rhythms or TCM syndromes. A data processing and control unit is electrically connected to the multidimensional sensing array and the timing control and rhythm simulation unit.

[0016] The data processing and control unit is configured to: receive real-time monitoring data generated by the multidimensional sensing array; execute an evolutionary algorithm using the real-time monitoring data to generate and optimize a candidate network drug formulation consisting of a dual list of multiple time periods and drug combinations; and generate a control signal based on the candidate network drug formulation generated by the evolutionary algorithm to drive the time-series control and rhythm simulation unit to apply the candidate network drug formulation to the time-series dynamic syndrome biology simulator.

[0017] Preferably, the timing control and rhythm simulation unit programmatically changes the culture environment, specifically by: periodically changing the concentration of hormones or cytokines in the culture environment according to a preset environment script through a set of high-precision microfluidic pumps and microvalve arrays controlled by a central computer.

[0018] In one specific embodiment, the data processing and control unit executes the evolutionary algorithm, specifically including: based on the real-time monitoring data, calculating the fitness of each candidate network drug formulation through a time-series fitness function to guide the optimization direction of the evolutionary algorithm.

[0019] Furthermore, the calculation of the time-series fitness function can be defined as follows: F(S) = w end ·D end (S)+w path ·D path (S)+w cost ·C cost (S); in, F(S): Represents the fitness evaluation value of candidate network drug formulation S; S: Represents a candidate network drug formulation, whose structure is an ordered list: S={(C1,Δt1),(C2,Δt2),…,(C n ,Δt n )}; Where n is the total number of stages included in the candidate network drug formulation S; C i Let Δt be the drug combination for stage i, which can be defined as a vector containing multiple drug components and their corresponding concentrations; i This represents the duration of action of stage i.

[0020] D end (S): Represents the terminal effect term, used to assess the difference between the biological state of the time-series dynamic syndrome biological simulator at the end of treatment and the preset health state; D path(S): Represents the process path item, used to assess the degree of deviation between the biological state path of the time-series dynamic syndrome biological simulator and the preset ideal recovery path during the entire treatment process; C cost (S): Represents a cost item used to assess the total amount of medication used in formulation S; w end ,w path ,w cost : These represent the weighting coefficients for the terminal effect term, process path term, and cost term, respectively.

[0021] The terminal effect term D end The formula for calculating (S) is: D end (S)=∥X S (T)-X target ∥ 2 ; in: T: Represents the total treatment time, and X S (T): Represents the biological state vector at the end of total time T under the action of formulation S. This vector is composed of readings from the multidimensional sensor array. X target : Represents a preset health status vector; ∥·∥ 2 : Represents the square of the Euclidean distance.

[0022] The process path item D path The formula for calculating (S) is: Where t represents the time variable; X S P(t) represents the biological state vector at time t under the influence of candidate network drug formulation S; P(t) represents the preset ideal recovery path function, which defines the ideal trajectory of the biological state vector as a function of time; ω(t) represents the time weight function, which is used to assign different weights to the degree of path deviation at different time points.

[0023] Preferably, the data processing and control unit is further configured to send the data of the optimal network drug formulation generated by the evolutionary algorithm to a 3D printing device for rapidly producing physical samples.

[0024] A third aspect of this invention provides an intelligent medical and pharmaceutical research and development method, comprising the following steps: S1. Based on TCM clinical case data and multi-omics data that include the time dimension, a dynamic syndrome biological feature network is constructed to describe the evolution of TCM syndrome states over time. S2. Provide a time-series dynamic syndrome biological simulation body, which can simulate the stage evolution of physiological rhythms or TCM syndromes. S3. Based on the dynamic syndrome biological feature network, initialize a formulation population consisting of multiple candidate network drug formulations, wherein each network drug formulation is defined as a pair list of time period and drug combination. S4. Apply each candidate network drug formulation in the formulation population to the time-series dynamic syndrome biological simulator in parallel, and monitor the biological state of the time-series dynamic syndrome biological simulator in real time to obtain a set of real-time monitoring data. S5. Based on the set of real-time monitoring data, calculate the fitness of each candidate network drug formulation through a time-series fitness function to obtain a set of fitness evaluation values; S6. Based on the set of fitness evaluation values, iteratively evolve the formulation population until an optimal network drug formulation is obtained.

[0025] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention constructs a dynamic syndrome biological feature network and defines candidate network drug formulations as a dual list of time periods and drug combinations. This makes the final generated drug regimen no longer a static single formulation, but a dynamic combination that corresponds to the evolution of TCM syndromes, with phased and temporal changes. This allows drug intervention to target the specific biological state of the disease at different time points, rather than a single state throughout the entire course of the disease.

[0026] 2. This invention achieves an automated closed-loop process for drug formulation optimization and in vitro biological testing by integrating a data processing and control unit, an evolutionary algorithm, and a time-series dynamic syndrome biological simulator. Specifically, the evolutionary algorithm directly utilizes real-time monitoring data generated by a multi-dimensional sensor array on the simulator to calculate the time-series fitness function and guide the optimization direction, reducing manual intervention and data transfer steps, and enabling high-throughput automated execution of the candidate formulation screening and validation process.

[0027] 3. This invention establishes an iterative enhancement mechanism for R&D knowledge by setting up a closed-loop feedback and knowledge base growth module, which transmits data verified through virtual and physical means back to the TCM clinical experience database module. Each complete R&D process not only produces the optimal network drug formulation, but its process and result data can also be used to optimize the initial dynamic syndrome biological characteristic network model, so that the accuracy and starting point of subsequent R&D can be systematically and continuously improved. Attached Figure Description

[0028] Figure 1This is a functional block diagram of the intelligent medical and pharmaceutical R&D platform and system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the data processing and model building process according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the execution of the core algorithm in one embodiment of the present invention. Figure 4 This is a schematic diagram of the verification and feedback process according to an embodiment of the present invention; Figure 5 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0029] See attached document Figure 1 , Figure 1 This is a functional block diagram of an intelligent medical and pharmaceutical R&D platform and system according to an embodiment of the present invention. The intelligent medical and pharmaceutical R&D platform and system provided by the present invention may structurally include an intelligent medical and pharmaceutical R&D platform and an intelligent medical and pharmaceutical R&D system that work in concert with it.

[0030] The intelligent medical and pharmaceutical R&D platform logically includes: a traditional Chinese medicine clinical experience database module 10, an AI analysis module 20, a network drug generation module 30, a virtual reality and formulation module 40, and a closed-loop feedback and knowledge base growth module 80.

[0031] The Traditional Chinese Medicine (TCM) clinical experience database module 10 is configured to structurally store TCM clinical case data and related multi-omics data, including a time dimension. This module 10 is connected to the AI ​​analysis module 20. The AI ​​analysis module 20 receives data from module 10 and constructs a dynamic syndrome biological feature network based on this data to describe the evolution of TCM syndrome states over time.

[0032] The output of the AI ​​analysis module 20 is connected to the input of the network drug generation module 30. The network drug generation module 30, based on a dynamic syndrome biological feature network, executes an evolutionary algorithm to generate an optimal network drug formulation by iteratively optimizing a population of formulations composed of multiple candidate network drug formulations. Each candidate network drug formulation is defined as a pair list of time periods and drug combinations.

[0033] The network drug generation module 30 is connected to the virtual reality and formulation module 40. The virtual reality and formulation module 40 receives the optimal network drug formulation, and on the one hand, creates a digital drug prototype of the formulation and performs virtual preclinical evaluation to generate virtual validation data; on the other hand, the virtual reality and formulation module 40 is also configured to guide the generation of physical formulations.

[0034] The intelligent medical and pharmaceutical R&D system is a physical entity for execution and testing, comprising: a time-series dynamic syndrome biology simulator 50, a time-series control and rhythm simulation unit 60, and a data processing and control unit 70. The time-series dynamic syndrome biology simulator 50 includes an organ-on-a-chip for three-dimensional cell co-culture and a multi-dimensional sensor array for real-time monitoring of its internal biological state.

[0035] The timing control and rhythm simulation unit 60 is connected to the timing dynamic syndrome biology simulator 50 for programmatically altering its culture environment. The data processing and control unit 70 is electrically connected to the multidimensional sensor array within the timing dynamic syndrome biology simulator 50 and the timing control and rhythm simulation unit 60. The data processing and control unit 70 receives real-time monitoring data generated by the multidimensional sensor array, executes an evolutionary algorithm, and generates control signals to drive the timing control and rhythm simulation unit 60 to apply dynamic drug combinations to the simulator 50.

[0036] In the entire system, the virtual reality and formulation module 40 receives physical verification data generated from testing physical formulations on a time-series dynamic syndrome biology simulator 50. Finally, the closed-loop feedback and knowledge base growth module 80 connects to the virtual reality and formulation module 40, receives the optimal network drug formulation, virtual verification data, and physical verification data, and then transmits these data back to the TCM clinical experience database module 10 to update the database and optimize the model parameters of the AI ​​analysis module 20.

[0037] See attached document Figure 2 , Figure 2 This is a schematic diagram of the data processing and model building process according to an embodiment of the present invention. The data foundation and model building functions in this embodiment of the present invention are mainly executed by the Traditional Chinese Medicine Clinical Experience Database module 10 and the AI ​​Analysis module 20.

[0038] The Traditional Chinese Medicine Clinical Experience Database module 10 is configured to uniformly store and manage multi-source heterogeneous data. The data stored internally specifically includes: Traditional Chinese medicine clinical case data is structured to include patient identification, consultation timestamp, four diagnostic methods information records, syndrome diagnosis labels, and corresponding prescription and medication records; Specifically, this includes quantitative measurement data from the genome, transcriptome, proteome, and metabolome. Before the data is stored, the TCM clinical experience database module 10 also performs preprocessing operations, including normalizing the multi-omics data to eliminate dimensional differences and interpolating and imputing missing data.

[0039] The AI ​​analysis module 20 is connected to the data output end of the TCM clinical experience database module 10, and its function is to construct a dynamic syndrome biological characteristic network. The construction process of this network includes the following steps: First, for the data of a certain case at a specific time point t extracted from the database module 10, the AI ​​analysis module 20 maps the multi-omics data of that time point into a graph structure G. t In the graph structure G t =(V,E) t In this context, the set of nodes V represents biomolecules (e.g., genes, proteins), and the set of edges E... t The edge represents the interaction between biomolecules at time point t, and the weight of the edge is determined by the expression correlation between molecules or the known strength of the interaction.

[0040] Secondly, the AI ​​analysis module 20 uses a graph neural network (GNN) to analyze the static graph structure G at each time point t. t The graph neural network is configured to aggregate the neighborhood information of each node, generating a low-dimensional vector representation, or node embedding, for each biomolecular node in the graph. This node embedding vector contains the state information of the node within the network topology at the current time step. By processing the graph structure at all time points, module 20 obtains a time series of graph embedding vectors {G1, G2, ..., G...}. T}

[0041] Then, the AI ​​analysis module 20 inputs the obtained graph embedding vector time series into a Long Short-Term Memory (LSTM) network. This LSTM network is configured to learn the temporal dependencies in the sequence, i.e., to capture the symptom-related biological network from a state G at a given time point. t Let G be the state at the next time point. t+1 Assume the evolutionary pattern. During the model training process, the TCM syndrome label at each time point t is used as a supervision signal. A loss function is used to constrain the optimization of the model parameters, so that the learned dynamic evolution pattern of the network matches the actual evolution process of TCM syndromes.

[0042] Ultimately, the hybrid model of the trained AI analysis module 20 constitutes the dynamic syndrome biological feature network. This network is a parameterized mathematical model capable of predicting the network's state evolution trajectory at future points in time based on the initial biological network state and time information. The network's output data is transmitted to the network drug generation module 30, serving as the biological foundation model for virtual drug screening and dynamic formulation design.

[0043] In one specific implementation, the input node features of the graph neural network are the normalized expression levels or concentration values ​​of the corresponding biomolecules. The long short-term memory network uses the cross-entropy loss function during training, aiming to ensure that the syndrome label predicted by the model at any time point t is consistent with the actual syndrome label recorded in the TCM clinical case data.

[0044] The physical simulation and testing functions in this embodiment of the invention are mainly composed of a time-series dynamic syndrome biological simulation body 50 and a time-series control and rhythm simulation unit 60.

[0045] The temporal dynamic syndrome biology simulator 50 is a physical device with an organ-on-a-chip at its core. This organ-on-a-chip integrates a microfluidic channel network, one or more three-dimensional cell co-culture chambers, and a multidimensional sensing array on a substrate. The three-dimensional cell co-culture chambers are designed to support the co-growth of multiple cell types (e.g., hepatocytes and immune cells) within a three-dimensional hydrogel scaffold to reproduce the microenvironment structure and intercellular interactions of tissues and organs.

[0046] A multidimensional sensing array is integrated into the bottom or sidewall of a cell culture chamber for non-invasive, real-time monitoring of the biological state inside the chamber. The array specifically includes: A set of electrochemical sensors for measuring glucose, lactate, and dissolved oxygen concentrations to characterize the metabolic activity of cells; a set of microelectrodes for measuring transcellular impedance to characterize cell layer integrity and cell viability. A set of sensor sites immobilized with specific antibodies, combined with optical or electrochemical detection methods, is used for in situ quantitative detection of the concentration of specific cytokines (e.g., tumor necrosis factor-α, interleukin-6) in the culture environment. The data output terminals of these sensors are connected to the data processing and control unit 70.

[0047] Before the system begins testing, the multidimensional sensor array undergoes baseline calibration, which involves recording the initial readings of all sensors as a zero-point reference while injecting standard basal culture medium. During testing, the raw sensor data received by the data processing and control unit 70 (e.g., the raw impedance values ​​of the microelectrodes) first undergoes baseline correction and signal filtering, and is then converted into indicators with clear biological significance (e.g., the percentage of cell layer coverage calculated based on impedance changes), ultimately forming a biological state vector.

[0048] The timing control and rhythm simulation unit 60 is connected to the fluid inlet of the timing dynamic syndrome biology simulator 50. The timing control and rhythm simulation unit 60 includes multiple reservoirs storing different solutions (e.g., basal culture medium, high-concentration hormone solutions, high-concentration cytokine solutions, and solutions of different drug combinations), a set of high-precision microfluidic pumps, and a microvalve array. The operation of the microfluidic pumps and the microvalve array is precisely driven by control signals from the data processing and control unit 70.

[0049] During operation, the timing control and rhythm simulation unit 60 dynamically mixes solutions from different reservoirs by programmatically controlling the flow rate of each microfluidic pump and switching the on / off state of the microvalve array. This allows the concentration C of a specific substance (such as a hormone or cytokine) in the culture medium injected into the timing dynamic syndrome biological simulator 50 to change with time t in a preset manner, i.e., to achieve a specific concentration curve C(t), which can be used to simulate the staged evolution of a specific TCM syndrome at the biological level, or to apply a dynamically changing drug formulation.

[0050] See attached document Figure 3 , Figure 3 This is a flowchart illustrating the execution of the core algorithm according to an embodiment of the present invention. The core algorithm and control functions in this embodiment are jointly executed by the network drug generation module 30 and the data processing and control unit 70, which maintain consistency in their algorithmic logic.

[0051] The core of the network drug generation module 30 or the data processing and control unit 70 is an evolutionary algorithm configured to generate and optimize candidate network drug formulations. First, a candidate network drug formulation S is encoded internally as an ordered list with the data structure S = {(C1, Δt1), (C2, Δt2), ..., (C...}. n ,Δt n )}. Among them, C i It is a vector representing the specific concentration of each component in the drug combination administered in stage i; Δt i It is a scalar representing the duration of the i-th stage.

[0052] The algorithm execution begins by initializing a population P(0) of drug formulations from multiple such candidate networks. The C of each individual in the population (i.e., each formulation S) is... i and Δt i The values ​​are randomly generated within preset constraints (such as maximum concentration and total treatment time).

[0053] Subsequently, the algorithm enters an iterative evolution process. In each generation of evolution, the data processing and control unit 70 calculates the fitness value of each candidate network drug formulation S in the population based on real-time monitoring data using a temporal fitness function F(S). The specific formula for calculating this temporal fitness function can be defined as: F(S) = w end ·D end (S)+w path ·D path (S). Wherein, w end and w path These are the preset weighting coefficients.

[0054] In another embodiment, to control the cost of drug use, the time-series fitness function may also introduce a cost term C. cost (S), its complete form is F(S)=w end ·D end (S)+w path ·D path (S)-w cost ·C cost (S). Wherein, w cost This is the cost weighting coefficient, while cost item C... cost The formula for calculating (S) is: Here, m represents the number of component types in the drug combination, and c ij Let k be the concentration of the j-th component in the i-th stage. j Let be the unit cost coefficient for the j-th component. For the process path term D... path The integral operation in (S) is specifically approximated in the data processing and control unit 70 by numerical integration of the measurement data at discrete time points using the trapezoidal rule. The first component of the function is the terminal effect term D. end (S)=∥X S (T)-X target ∥ 2 This calculation is performed at the end of the total treatment duration T, determining the biological state vector X under the influence of formulation S. S (T) and the preset health state vector X target The square of the Euclidean distance between them. X S (T) consists of the readings of the multidimensional sensor array on the time-series dynamic symptom biology simulator 50 at time T, X target It is a target reading vector that is pre-set based on health sample data.

[0055] The second component of a function is the process path term. This calculation represents the real-time biological state path X throughout the entire treatment process (from t=0 to T). Sω(t) is the weighted integral deviation between the biological state and the predefined ideal recovery path P(t). P(t) is a predefined vector function that describes the ideal trajectory of biological state changes over time. ω(t) is a time weighting function configured to assign higher computational weights to path deviations at certain key time intervals.

[0056] To further clarify, the symbols in the aforementioned and related formulas are defined as follows: in, n is the total number of stages contained in the candidate network drug formulation S; T represents the total treatment time, and t represents the time variable, and its value ranges from 0 to T; X S (t) represents the biological state vector measured by the multidimensional sensing array on the time-series dynamic syndrome biology simulator 50 at time t under the action of the candidate network drug formulation S, and after processing. ∥·∥ 2 This represents the square of the Euclidean distance.

[0057] Furthermore, to further clarify vector C i Its component c ij The relationship is defined, and the relevant symbols are clarified. The following additional definitions are provided: i represents the index of the stage, and its value range is an integer from 1 to n; C i The vector representing the drug combination at stage i is of the form C. i =(c i1 ,c i2 ,…,c im ); m represents the total number of drug components available for selection; j represents the index of the drug component, and its value ranges from 1 to m. Weighting coefficient w end ,w path and w cost All are preset non-negative real numbers, and their relative magnitudes determine the degree of emphasis placed on terminal effects, process paths, and cost control in the optimization objective.

[0058] ω(t) represents a pre-defined, time-varying non-negative weighting function. In one specific implementation, this function can be set as a piecewise function; for example, during the last 20% of the treatment period (i.e., t∈[0.8T,T]), ω(t) has a value greater than 1 (e.g., 2), while its value is 1 during other periods. This setting is used to focus more on whether the path in the later stages of treatment can stably approach the ideal recovery path P(t) in fitness calculation.

[0059] After calculating the fitness of the entire population, the algorithm performs selection, crossover, and mutation operations on the formula population to generate the next generation population. The selection operation uses the roulette wheel selection method, where the probability of each candidate network drug formula being selected as a parent is proportional to its fitness evaluation value.

[0060] The crossover operation acts on two selected parent formulas and uses the single-point crossover method. The algorithm randomly selects an integer k (1 ≤ k < n) as the crossover point and swaps the parts of the two parent formulas after the k-th stage to generate two new offspring formulas.

[0061] The mutation operation acts on the offspring formulas with a preset low probability and specifically includes: Concentration mutation, randomly select a concentration value of a certain component in a drug combination vector C i and perform a small random perturbation on it; Time mutation, randomly select a duration Δt i and perform a small random increase or decrease on it; Structure mutation, randomly insert a new (C j , Δt j ) pair in the formula list or delete an existing pair.

[0062] The above iterative evolution process continues until the preset maximum number of evolution generations is reached, or the optimal fitness value no longer shows a significant increase for several consecutive generations. When the algorithm terminates, the candidate network drug formula with the highest fitness value in the current population is determined as the optimal network drug formula and is output to the virtual reality and preparation module 40 by module 30 or unit 70.

[0063] Refer to Appendix Figure 4 , Figure 4 which is a schematic diagram of the verification and feedback process according to an embodiment of the present invention. The verification and feedback functions in the embodiments of the present invention are mainly executed by the virtual reality and preparation module 40 and the closed-loop feedback and knowledge base growth module 80.

[0064] The data input end of the virtual reality and preparation module 40 is connected to the output ends of the network drug generation module 30 and the data processing and control unit 70. The virtual reality and preparation module 40 is first configured to perform virtual verification. It receives the optimal network drug formula S optimal generated by the aforementioned modules and uses this formula data as input to load the dynamic syndrome biological feature network model constructed by the AI analysis module 20 for a complete computational simulation. The output of this simulation is a predicted biological state evolution trajectory, and this trajectory data constitutes the virtual verification data.

[0065] Simultaneously, the virtual reality and formulation module 40 is also configured to guide the generation of physical formulations. It will optimize the network drug formulation S optimal The data structure is {(C1,Δt1),(C2,Δt2),…,(C n ,Δt n This is converted into a set of control instructions for a specific physical preparation device (e.g., a multi-material 3D printing device). This instruction set specifically defines the material combinations and their dosages that the device should use at different time intervals to prepare physical formulations capable of releasing different drug combinations sequentially.

[0066] In addition, the virtual reality and formulation module 40 also has a data receiving interface for receiving real-time monitoring data representing the actual biological response generated by a multi-dimensional sensor array when the physical formulation is tested on the time-series dynamic syndrome biology simulator 50. This data is stored as physical validation data after being received.

[0067] The data input terminal of the closed-loop feedback and knowledge base growth module 80 is connected to the virtual reality and formulation module 40. The function of the closed-loop feedback and knowledge base growth module 80 is to integrate the core data generated throughout a complete R&D process. Specifically, it will integrate the optimal network drug formulation S... optimal The corresponding virtual verification data (predicted trajectory) and physical verification data (actual trajectory) are collected and formatted into a unified data record.

[0068] After data integration, the closed-loop feedback and knowledge base growth module 80 transmits the unified data record back to the TCM clinical experience database module 10. This record is stored in the database as a new, high-value validation data point. In subsequent system iterations, this data record can be used as an incremental training sample for the AI ​​analysis module 20 to fine-tune and optimize the parameters of the hybrid model of graph neural networks and long short-term memory networks, thereby continuously improving the accuracy of the dynamic syndrome biological characteristic network model.

[0069] The following will refer to the appendix. Figure 5 The present invention provides a detailed description of the specific process of the intelligent medical network drug development method provided by the present invention. Figure 5 This is a flowchart of a method according to an embodiment of the present invention.

[0070] The method flow of this embodiment of the invention first executes step S1, constructing a dynamic syndrome biological feature network. This step is performed by the AI ​​analysis module 20, which reads clinical case data containing the time dimension and associated multi-omics data from the TCM clinical experience database module 10. Subsequently, the AI ​​analysis module 20 constructs a biomolecular interaction graph for the multi-omics data at each time point, and uses a hybrid model of graph neural network and long short-term memory network to model the time series of the graph structure, ultimately generating a parameterized model that can describe the evolution of a specific TCM syndrome state over time, namely the dynamic syndrome biological feature network.

[0071] Next, step S2 is performed to provide a time-series dynamic syndrome biological simulator 50. This step includes three-dimensional cell co-culture within the organ-on-a-chip of the simulator 50 until a stable tissue microstructure is formed. Subsequently, through the time-series control and rhythm simulation unit 60, specific concentrations of hormones or cytokines are precisely injected into the culture environment to induce the simulator 50 to a biological state corresponding to the initial stage of the TCM syndrome under study.

[0072] Then, step S3 is executed to initialize a formulation population consisting of multiple candidate network drug formulations. This step is performed by the data processing and control unit 70. Unit 70 randomly generates a set of candidate network drug formulations based on preset constraints such as population size, drug component range, maximum concentration limit, and total treatment duration. Each formulation uses a dual list of time periods and drug combinations, i.e., S = {(C1,Δt1),(C2,Δt2),…,(C…} n ,Δt n The data structure of )}.

[0073] Subsequently, step S4 is executed, whereby the candidate network drug formulations from the formulation population are applied in parallel to the time-series dynamic syndrome biology simulator 50, and monitored in real time. The data processing and control unit 70 converts each candidate formulation S into a set of time-series control signals and sends them to the time-series control and rhythm simulation unit 60. The time-series control and rhythm simulation unit 60 drives its internal microfluidic pumps and microvalve arrays, according to the drug combination C defined by formulation S. i and duration Δt i The drug is dynamically applied to the simulator 50. During this process, the multi-dimensional sensor array inside the simulator 50 continuously operates, collecting biological state data in real time and transmitting this data back to the data processing and control unit 70 to form a set of real-time monitoring datasets that correspond one-to-one with each candidate formulation.

[0074] Next, step S5 is executed to calculate the fitness of each candidate network drug formulation based on the obtained real-time monitoring data. The data processing and control unit 70 calculates the fitness for each set of monitoring data using a preset temporal fitness function F(S). Specifically, it calculates the actual state path X formed by the monitoring data. S (T) Substitute into the terminal effect term D end (S) and process path item D path The fitness evaluation value of the candidate formulation is obtained from the calculation formula of (S), and finally a set of fitness evaluation values ​​are formed.

[0075] Finally, step S6 is executed, iteratively evolving the formulation population based on the fitness evaluation values. The data processing and control unit 70 performs selection, crossover, and mutation operations on the current formulation population according to the fitness values, generating a new generation of formulation population. This new population will then undergo steps S4 and S5 again. The entire "application-monitoring-evaluation-evolution" cycle continues until a preset termination condition is met, such as completing a specified number of iterations. Upon cycle termination, the data processing and control unit 70 selects the individual with the highest fitness evaluation value from the final formulation population and outputs it as the optimal network drug formulation.

[0076] To make the technical solution, operation process, and technical effects of this invention clearer, a specific application scenario will be used as an example for illustration below. This example aims to demonstrate how the various components of this invention work together to complete a specific research and development task, and does not constitute any limitation on the scope of protection of this invention.

[0077] The application objective of this embodiment is to develop a time-series network drug formulation that matches the dynamic process of the evolution of "liver qi stagnation" syndrome from its initial to its intermediate stage.

[0078] First, the operators initiate the research and development process of this invention. The TCM clinical experience database module 10 has pre-stored clinical follow-up records of multiple patients with "liver qi stagnation" syndrome at different stages of the disease (e.g., week 1, week 4, and week 8). These records include structured information from the four diagnostic methods (inspection, auscultation and olfaction, and palpation), syndrome severity scores, and transcriptomic and metabolomic data from blood samples collected at the same time point.

[0079] The AI ​​analysis module 20 is invoked, taking this time-series data as input, and executing step S1. The graph neural network within module 20 constructs molecular network snapshots from the multi-omics data at each time point, while the long short-term memory network learns the evolution patterns of these network snapshots over time (from week 1 to week 8), ultimately constructing a dynamic syndrome biological characteristic network specifically describing the development of "liver qi stagnation" syndrome. This network mathematically defines the activity change trajectories of core biomarkers (e.g., key enzymes and inflammatory factors in specific neurotransmitter metabolic pathways).

[0080] Next, step S2 is executed on the physical system side. Technicians co-culture human primary hepatocytes and hepatic stellate cells in an organ-on-a-chip within a time-series dynamic syndrome biology simulator 50. After culture stabilization, a culture medium containing a high concentration of cortisol is continuously perfused into the culture environment via a time-series control and rhythm simulation unit 60 to simulate the critical stress state at the initial stage of the syndrome. A multidimensional sensor array inside the simulator 50 continuously monitors the process, and the simulator is ready when the readings of key biomarkers match the initial state defined in the aforementioned dynamic network model.

[0081] The data processing and control unit 70 executes step S3, initializing an initial population containing 100 candidate network drug formulations. Each formulation S is encoded in the form S = {(C1, Δt1), (C2, Δt2)}, where C1 and C2 are vectors of randomly combined and set concentrations from a preset drug component library (e.g., containing the effective components of herbs such as Bupleurum, Paeonia lactiflora, and Cyperus rotundus), and Δt1 and Δt2 are durations randomly set within the range of 12 to 36 hours.

[0082] Subsequently, the system enters the automated iterative optimization loop from steps S4 to S6. The data processing and control unit 70 converts the codes of 100 candidate formulations into control signals, driving the timing control and rhythm simulation unit 60 to apply the corresponding timing drug regimens in 100 parallel culture units. Throughout the application process, the multidimensional sensor array within each unit records the changes in biological state data in real time.

[0083] For each candidate formulation, the data processing and control unit 70 calculates the value of its temporal fitness function F(S) based on the real-time monitoring data it generates. In this scenario, X target The sensor reading vector P(t), which is set as a healthy liver cell model, is defined as an ideal recovery curve that smoothly transitions from the current initial state of "liver qi stagnation" to a healthy state.

[0084] Based on the calculated fitness values ​​of 100 parents, the evolutionary algorithm performs selection, crossover, and mutation operations to generate 100 candidate formulas for the next generation. For example, two parental formulas with high fitness, one showing significant performance in the first stage and the other performing better in the second stage, can be crossbred to produce a progeny formula that performs well in both stages. This cyclical process is set to run for 200 generations.

[0085] After the cycle ends, the data processing and control unit 70 outputs the individual with the highest fitness value in the final population as the optimal network drug formulation. For example, the result might be a specific two-stage protocol: the first stage (0-24 hours) administers a high concentration of a combination of anti-inflammatory active ingredients, while the second stage (24-60 hours) switches to another combination of active ingredients focused on restoring normal metabolic function. This result is then transmitted to the virtual reality and formulation module 40 and the closed-loop feedback and knowledge base growth module 80.

[0086] After receiving the optimal network drug formulation, the virtual reality and formulation module 40 converts its data structure into a standard format machine control file, such as a G-code file. This file is then sent to a multi-material 3D printing device connected to the system. Based on the file instructions, the device uses different biocompatible hydrogel materials to load different concentrations of the active pharmaceutical ingredient, printing a physical formulation sample capable of sequential drug release according to a two-stage protocol.

Claims

1. The intelligent medicine network drug intelligent research and development platform is characterized in that, The application comprises: a traditional Chinese medicine clinical experience database module configured to store and process traditional Chinese medicine clinical case data and multi-omics data containing a time dimension; an AI analysis module connected to the traditional Chinese medicine clinical experience database module and configured to construct a dynamic syndrome biological characteristic network for describing the evolution rule of traditional Chinese medicine syndrome states over time based on the traditional Chinese medicine clinical case data and multi-omics data; a network drug generation module configured to: initialize a formula population composed of multiple candidate network drug formulas, wherein each candidate network drug formula is defined as a pair of time period and drug combination; based on fitness evaluation of the virtual therapeutic effect of each candidate network drug formula in the formula population on the dynamic syndrome biological characteristic network, iteratively evolve the formula population to generate an optimal network drug formula; a virtual reality and preparation module configured to: receive the optimal network drug formula, create a digital drug prototype of the optimal network drug formula and perform virtual preclinical evaluation to generate virtual validation data, and guide the generation of a physical preparation and receive physical validation data generated by testing the physical preparation on a biological in vitro model; a closed-loop feedback and knowledge base growth module configured to return the optimal network drug formula, the virtual validation data and the physical validation data to the traditional Chinese medicine clinical experience database module together to complete the iterative enhancement of research and development knowledge. 2.The intelligent medicine network and drug intelligent research and development platform of claim 1, wherein, The AI analysis module is specifically configured to use a hybrid model of graph neural network and long short-term memory network to perform time series modeling on the multi-omics data, wherein the time series modeling uses the time information and traditional Chinese medicine syndrome labels contained in the traditional Chinese medicine clinical case data as constraint conditions to construct the dynamic syndrome biological characteristic network. 3.The intelligent medicine network and drug intelligent research and development platform of claim 1, characterized in that, The iterative evolution of the formula population specifically includes performing selection, crossover and mutation operations on the network drug formulas in the formula population according to the results of the fitness evaluation. 4.The intelligent medicine network and drug intelligent research and development platform of claim 1, wherein, The virtual validation data is specifically evaluation data of the digital drug prototype, and the biological in vitro model is specifically a time series dynamic syndrome biological simulation body, and the physical validation data is specifically multi-dimensional sensing data obtained by testing the physical preparation on the time series dynamic syndrome biological simulation body.

5. The intelligent medicine network drug intelligent research and development system, characterized in that, The application comprises: a time series dynamic syndrome biological simulation body comprising an organ chip for three-dimensional cell co-culture and a multi-dimensional sensing array for real-time monitoring of the biological state inside the time series dynamic syndrome biological simulation body; a time series control and rhythm simulation unit connected to the time series dynamic syndrome biological simulation body and configured to programmatically change the culture environment of the time series dynamic syndrome biological simulation body for simulating physiological rhythm or phased evolution of traditional Chinese medicine syndrome; a data processing and control unit electrically connected to the multi-dimensional sensing array and the time series control and rhythm simulation unit, the data processing and control unit being configured to: receive real-time monitoring data generated by the multi-dimensional sensing array; performing an evolutionary algorithm with the real-time monitoring data to generate and optimize a candidate network drug formula consisting of a list of pairs of time periods and drug combinations; generating a control signal based on the candidate network drug formula optimized by the evolutionary algorithm, for driving the timing control and rhythm simulation unit to apply the candidate network drug formula to the timing dynamic syndrome biology simulation body.

6. The intelligent medicine network and drug intelligent research and development system of claim 5, wherein, The timing control and rhythm simulation unit programmatically changes the culture environment, specifically including periodically changing the concentration of hormones or cytokines in the culture environment through a set of high-precision microfluidic pumps and microvalve arrays.

7. The intelligent medicine network and drug intelligent research and development system of claim 5, wherein, The data processing and control unit executes the evolutionary algorithm, specifically including calculating the fitness of each candidate network drug formula through a timing fitness function based on the real-time monitoring data, to guide the optimization direction of the evolutionary algorithm. 8.The intelligent medicine network and drug intelligent research and development system according to claim 7, characterized in that, The calculation of the timing fitness function includes: an end effect term for evaluating the difference between the biological state of the timing dynamic syndrome biology simulation body at the end of treatment and the preset healthy state; a process path term for evaluating the degree of deviation between the biological state path of the timing dynamic syndrome biology simulation body during the entire treatment process and the preset ideal recovery path. 9.The intelligent medicine network and drug intelligent research and development system of claim 5, wherein, The data processing and control unit is also configured to send the optimal network drug formula generated by the evolutionary algorithm to a 3D printing device for rapid production of physical samples.

10. The intelligent medicine network drug intelligent research and development method is characterized in that, The method comprises the following steps: S1, based on TCM clinical case data containing time dimension and multi-omics data, a dynamic syndrome biology feature network describing the evolution rule of TCM syndrome state over time is constructed; S2, a timing dynamic syndrome biology simulation body is provided, which can simulate physiological rhythm or stage evolution of TCM syndrome; S3, based on the dynamic syndrome biology feature network, a formula population consisting of multiple candidate network drug formulas is initialized, wherein each network drug formula is defined as a list of pairs of time periods and drug combinations; S4, each candidate network drug formula in the formula population is applied to the timing dynamic syndrome biology simulation body in parallel, and the biological state of the timing dynamic syndrome biology simulation body is monitored in real time to obtain a set of real-time monitoring data; S5, according to the set of real-time monitoring data, the fitness of each candidate network drug formula is calculated through a timing fitness function to obtain a set of fitness evaluation values; S6, based on the set of fitness evaluation values, the formula population is iteratively evolved until an optimal network drug formula is obtained.

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