Cerebral disease drug curative effect analysis and prediction system and method

Abstract

The invention provides a brain disease drug curative effect analysis and prediction system and method, and relates to the technical field of drug analysis. Multi-source heterogeneous data such as genetic variation spectrum, neural image topological characteristics, metabolic trajectory vectors and microbiome dynamic distribution matrix are integrated, and cross-modal dynamic fusion is realized through decomposition. The system constructs a drug prediction coupling network based on dynamic causal inference, analyzes overlapped potential intervention nodes of multiple disease mechanisms, and generates a disease-drug-phenotype multi-dimensional mapping library. A short-term drug response toxicity threshold is predicted through a metabolic entropy change model, a long-term neurological function degeneration trajectory is evaluated in combination with epigenetic drift, and an anti-fact strategy gradient is utilized to dynamically optimize a drug administration scheme. According to the invention, accurate fusion and dynamic analysis of multi-modal data are realized, a balance mechanism of curative effect and risk is established, and accurate drug combination sequence and administration strategy support can be provided for individualized treatment of complex brain diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of drug analysis, and in particular to a system and method for analyzing and predicting the efficacy of drugs for brain diseases. Background Art

[0002] The current field of drug development for brain diseases mainly relies on single-dimensional biological data to build efficacy prediction models. Traditional methods usually use independent data sources such as genetic variation information, static imaging features or discrete metabolic indicators to establish linear association models for target screening or phenotypic classification. Most existing models use static superposition strategies to process multimodal data, which cannot achieve collaborative modeling of dynamic features across scales. This leads to obvious limitations in the cross-identification of multiple diseases and makes it difficult to support the development of multi-target collaborative intervention strategies. When analyzing intervention pathways in overlapping areas of multiple mechanisms, the coupling relationship between data is often ignored, and it is easy to fall into local optimal solutions. In the process of drug combination optimization, an effective dynamic equilibrium equation has not been established, which seriously restricts the ability to make accurate decisions for personalized treatment. Summary of the Invention

[0003] To achieve the above objectives, the present invention is implemented through the following technical solutions: A system and method for analyzing and predicting the efficacy of drugs for brain diseases, comprising:

[0004] A heterogeneous modality aggregation interface is used to receive and integrate multi-disease cross-pathology data streams and personalized biomarker time series. The multi-disease cross-pathology data streams include genetic variation spectra, neuroimaging topological features, metabolic oscillation patterns derived from metabolic trajectory vectors, and microbiome dynamic distribution matrices.

[0005] The multidimensional association analysis module performs implicit association mapping on the output of the heterogeneous modality aggregation interface based on the drug prediction coupling network to generate a disease-drug-phenotype multidimensional mapping library, where the drug prediction coupling network infers potential intervention nodes where multiple disease mechanisms overlap through dynamic causality.

[0006] A dual-domain risk stratification module configured to predict short-term drug response toxicity thresholds based on a metabolic entropy change model that constructs a dynamic energy dissipation equation using the blood-brain barrier permeability coefficient and the intensity of cellular stress signals, and to assess long-term neurodegeneration trajectories through epigenetic drift;

[0007] The adaptive intervention optimization module connects the multidimensional association analysis module and the dual-domain risk stratification module, generates personalized drug combination sequences through counterfactual policy gradients, and modifies the spatiotemporal parameters of drug administration in real time to balance the efficacy gain and the risk accumulation rate.

[0008] The heterogeneous modality aggregation interface realizes cross-modal dynamic fusion through the collaborative architecture of streaming topological embedding and tensor recursive decomposition. The streaming topological embedding uses an incremental graph neural network to dynamically encode the time series data stream, and maps the discrete sites of the genetic variation spectrum into a dynamic probabilistic graph structure through multi-source data channels aligned with timestamps. The graph attention mechanism calculates the node similarity of SNP clusters through multi-channel attention weights and filters topological correlation features based on preset edge weight thresholds. The tensor recursive decomposition decomposes the three-dimensional convolution kernel of the neuroimaging topological features into multi-scale gradient tensors through high-order tensor expansion and low-rank approximation algorithm, and transforms the gradient into a multi-scale gradient tensor through rotation invariance. Cortical connectivity fingerprints are generated to stabilize features under different spatial rotations. When constructing a latent variable space mapping through non-uniform sampling alignment of metabolic trajectory vectors, dynamic time warping is used to align the temporal phase difference between metabolic oscillation patterns and microbiome distribution matrices, and to learn cross-modal latent variable distributions. A mixed-dimensional projection strategy for microbiome data streams separates the discrete connectivity patterns of bacterial interaction networks from the continuous diffusion paths of metabolite concentration fields through graph sparse coding, where the sparsity constraint is implemented through L1 regularization optimization. A multimodal comparison framework adopts a weight allocation rule based on mutual information, dynamically calculating feature weights through the mutual information entropy ratio between modalities. The specific formula is: The mutual information between the i-th mode and the target variable is used to quantify the weight, and the calibration threshold is dynamically adjusted according to the KL divergence between the modes. The generation of the dynamic hypergraph structure defines the hyperedge as the joint probability distribution of multimodal feature clusters, the node connection rules are based on the spectral clustering results of the cross-modal feature similarity matrix, and the hyperedge weight is quantified by the feature fusion entropy value. The final output is a dynamic hypergraph that integrates genetic permeability, image topological entropy, metabolic phase angle and microbial mobility. Genetic permeability is calculated by the SNP cluster association strength, image topological entropy is quantified based on the information entropy of cortical connection fingerprints, metabolic phase angle extracts the main frequency phase of the oscillation mode, and microbial mobility is evaluated by the diffusion rate of the bacterial interaction network, thereby achieving a reproducible fusion of the entire link from feature alignment to structure generation.

[0009] Preferably, the drug prediction coupling network of the multidimensional association analysis module constructs a causal topology through dynamic causal inference, specifically using a structural equation model to quantify the interaction path weights of genetic permeability and imaging topological entropy; the causal edge weight adjustment is based on the dynamic update of the phase angle of the metabolic trajectory vector, which is mathematically expressed as: ,in , is the coupling parameter between the blood-brain barrier permeability coefficient and the cell stress signal intensity, is the metabolic phase angle, The cross-organ interaction gradient is calculated as the tensor product of microbial mobility; the initial causal graph is generated by projection into the latent variable space, mapping the graph attention features of the genetic variation spectrum and the multi-scale gradient tensor of the neuroimaging to a shared latent space, and using variational inference to optimize the projection consistency; the spatiotemporal coupling relationship between the metabolic oscillation pattern and the cortical connectivity fingerprint is constrained by the partial differential equation: , where D b is the blood-brain barrier permeability coefficient, k is the metabolic-neural coupling strength, is the topological entropy of the cortical connectivity fingerprint, f and Ø are the main frequency and phase angle of metabolic oscillation, respectively. The boundary condition is set as the metabolite concentration gradient does not exceed the cell membrane ion flux threshold to ensure a dynamic balance between energy dissipation rate and drug penetration efficiency.

[0010] The tensor convolution operation of the drug prediction coupling network adopts a three-dimensional separable convolution kernel design. The convolution kernel size is adaptively adjusted according to the spatiotemporal resolution of the multimodal causal topology, and the kernel weight is initialized by the causal strength of the intervention efficacy propagation path. The calculation formula for the intervention efficacy distribution is:

[0011] , where T i is the causal topological tensor, M is the drug molecule interaction matrix, λ is the toxicity attenuation coefficient fed back by the metabolic entropy change model, and Δt is the dosing time interval; the metabolic entropy change model dynamically corrects the activation state of the causal edge by real-time monitoring of the cellular stress signal intensity. The specific feedback mechanism is: when the toxicity threshold exceeds the preset safety range, the weight of the relevant causal path is reduced by the backpropagation algorithm, and the epigenetic drift assessment module is triggered to recalibrate the impact of the neurological function degeneration trajectory on the topological structure; the construction of the disease-drug-phenotype three-dimensional mapping library is achieved by integrating the intervention efficacy distribution and the probabilistic fusion of the toxicity threshold. The therapeutic effect gain is calculated by the integral of the causal path propagation efficiency, and the risk accumulation rate is quantified by the real-time numerical solution of the dynamic energy dissipation equation to ensure that the drug combination optimization process simultaneously meets the maximum efficacy and controllable risk.

[0012] Preferably, the metabolic entropy change model construction process in the dual-domain risk stratification module is to first perform non-uniform sampling alignment on the time series data of metabolic oscillation and microbial migration, and establish a dynamic energy dissipation equation based on the blood-brain barrier permeability coefficient and cell stress signal intensity. ; Among them, the value of the diffusion coefficient D is calculated based on the experimental data of the porosity of the blood-brain barrier and the molecular weight of the metabolites through the modified Stokes-Einstein equation; the signal intensity coefficient σ is determined by quantifying the cell stress response, such as the nonlinear regression fitting of the fluorescent labeling signal or the dynamic monitoring of the single-cell transcriptome; then, the metabolic phase angle and the oscillation period phase parameter extracted by Fourier transform are fused with the energy transfer path of the microbial interaction network through differential manifold modeling. The specific method is to map the microbial network topology to the Riemannian manifold space, and use the covariant derivative to define the geodesic equation, thereby generating a dynamic entropy change field that describes the coupling relationship between the cross-organ metabolite gradient and cell stress. During the process, the diffusion path of the metabolite concentration field is optimized through gradient optimization to adjust the connection weights of the bacterial interaction network. At the same time, the blood-brain barrier permeability coefficient is embedded in the metabolic oscillation equation in the form of a phase modulation function to achieve spatiotemporal dynamic regulation. The numerical integration of the neurodegeneration trajectory adopts the fourth-order Runge-Kutta method, and its discretization step size is controlled by an adaptive strategy, that is, the step size is dynamically adjusted according to the local truncation error and the preset tolerance threshold, such as 1e-6. The calibration of the toxicity threshold is constrained by the dynamic distribution matrix of the microbiome to minimize the residual between the predicted value and the clinical observation value. The convergence condition is set as the relative change rate of the parameter is less than 1e-5 or the maximum number of iterations reaches 500 times. Finally, the epigenetic drift assessment results are integrated, such as the dynamic correlation between CpG methylation time series data and neurological function scores, and a metabolic entropy change model calibrated by multimodal data is output to ensure a balance between short-term toxic response and long-term neurodegeneration risk prediction, thereby improving clinical credibility.

[0013] Preferably, the adaptive intervention optimization module generates a candidate drug combination sequence under the constraint of the dynamic energy dissipation equation through the counterfactual strategy gradient; first, a cross-organ drug penetration efficiency model is constructed based on the tensor product of the metabolic phase angle and the microbial migration rate, combined with the diffusion equation driven by the blood-brain barrier permeability coefficient. , where C is the drug concentration field, D is the diffusion coefficient, which is determined by the porosity of the blood-brain barrier and the molecular weight of the metabolite, k is the clearance rate, and S(x, t) is the time-space dependent drug penetration efficiency source term, which is calculated by the tensor product; then, dynamic causal inference is used to simulate the intervention efficacy propagation path of different drug administration strategies in the causal topology, and the cell stress signal intensity fed back by the metabolic entropy change model is used to calibrate the drug response toxicity gradient in real time, where the objective function is designed to be the weighted sum of the efficacy gain and the risk accumulation rate, that is, , weights α and β are determined by Bayesian optimization driven by clinical data to ensure a balance between efficacy and risk; the update rule of the counterfactual policy gradient is based on the gradient ascent direction of the policy parameters, and the partial derivatives of the objective function with respect to the policy parameters are calculated by the chain rule. The policy gradient is corrected in combination with the results of epigenetic drift assessment, such as the gradient signal of the neurodegeneration trajectory. The specific form is , where Q_efficacy and Q_risk are the cumulative return functions of efficacy and risk respectively; the feedback mechanism of the neurological degeneration trajectory is realized through back propagation, which converts the sensitivity of the degradation rate to the strategy parameters A gradient update process is embedded to dynamically adjust the exploration direction to inhibit long-term neurological damage. Finally, through iterative optimization, the coupling equation of the spatiotemporal distribution of drug concentration and the policy gradient is solved to output an individualized dosing regimen that satisfies the dynamic energy dissipation constraint and balances short-term efficacy with long-term risks. Its verifiability is ensured by comparing simulation predictions with clinically measured drug concentrations and functional score data.

[0014] The present invention provides a system and method for analyzing and predicting the efficacy of drugs for brain diseases, which has the following beneficial effects:

[0015] 1. The present invention solves the cross-modal fusion of multi-source heterogeneous data through a heterogeneous modality aggregation interface and adopts a collaborative architecture of streaming topology embedding and tensor recursive decomposition; eliminates dimensional drift and temporal asynchrony, generates a dynamic hypergraph structure, and significantly improves the comprehensiveness and accuracy of pathological mechanism analysis.

[0016] 2. This invention accurately identifies cross-modal causal intervention pathways and generates intervention efficacy distributions covering overlapping regions of multiple mechanisms, thereby improving the coverage and accuracy of drug target predictions and providing a scientific basis for personalized treatment of complex brain diseases.

[0017] 3. The present invention uses a metabolic entropy change model and counterfactual strategy gradient to calculate the short-term drug response toxicity threshold and long-term neurological function degeneration trajectory in real time. It can dynamically adjust the drug administration strategy, balance the drug penetration efficiency and the energy dissipation rate of cell stress signals, and ultimately output an individualized drug combination sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] This system achieves accurate prediction of the efficacy and risks of brain disease drugs through multimodal data collection, cross-domain fusion, dynamic modeling and optimized decision-making.

[0021] The system first obtains raw data from multi-source biomedical databases and clinical equipment; the genetic variation spectrum is generated by a whole-genome sequencer, including the patient's single nucleotide polymorphism site data; for example, the APOE gene ε4 allele frequency of a patient is 0.28, and the genotype of TOMM40 locus rs157580 is GT; neuroimaging topological features are collected by a 7T magnetic resonance imaging device, and the original DICOM format images are three-dimensionally reconstructed to extract the hippocampal volume of 3200 cubic millimeters and the prefrontal cortex thickness of 2.7 mm; metabolic trajectory vectors are continuously detected in blood samples by liquid chromatography-mass spectrometry to generate a time-series curve of β-hydroxybutyrate concentration, with a peak concentration of 45 μmol / L and a trough value of 12 μmol / L; microbiome data are obtained by a metagenomic sequencer, with a relative abundance of Bacteroides in the intestinal flora of 32% and a short-chain fatty acid concentration of 8.5 μM in the cerebrospinal fluid.

[0022] After data collection, preprocessing was performed. Timestamp alignment was based on the first MRI scan time T0, and the metabolite sampling time T0+72 hours and the microbial sequencing time T0+48 hours were synchronized to a unified time axis. Dimension normalization used the Z-score algorithm to map the SNP site frequency from the 0-1 interval to a standardized distribution, and the metabolite concentration unit was unified to μmol / L. For example, the peak concentration of β-hydroxybutyrate was 1.8 after standardization, and the microbial abundance was 4.2 after logarithmic transformation. Preprocessing solved the problems of temporal asynchrony and dimensional differences in multi-source data, providing standardized input for subsequent cross-modal fusion.

[0023] A heterogeneous modality aggregation interface uses streaming topological embedding and tensor recursive decomposition to achieve deep integration of genetic, imaging, metabolic, and microbial data. Genetic variation spectra are dynamically encoded using an incremental graph neural network, with nodes representing single nucleotide polymorphisms (SNPs) and edge weights calculated by linkage disequilibrium coefficients. For example, the linkage disequilibrium coefficient between APOE ε4 and TOMM40 is 0.92, forming a strongly correlated subgraph. Neuroimaging features are decomposed into multiscale gradient tensors using a three-dimensional convolution kernel. The hippocampal volume gradient tensor has dimensions of 128×128×64 and is transformed to generate a cortical connectivity fingerprint using rotational invariance. Its information entropy value is 0.75, reflecting topological stability. Metabolic trajectory vectors are aligned with microbiome time series data using a dynamic time warping algorithm. The trough of β-hydroxybutyrate concentration corresponds to a decrease in Bacteroidetes abundance of 25%, with a negative correlation coefficient of 0.68. Microbiome data are separated into a sparse bacterial interaction network and a continuous metabolite concentration field using mixed dimensional projection. The L1 regularization parameter of the sparse matrix is set to 0.01 to optimize the network structure.

[0024] The multimodal comparison framework dynamically assigns feature weights based on the mutual information entropy ratio; the genetic permeability weight is 0.35, calculated from the risk effect value of the APOE ε4 locus; the imaging topology entropy weight is 0.28, based on the information entropy of the cortical connection fingerprint; the metabolic phase angle weight is 0.22, determined by the phase angle of the β-hydroxybutyrate oscillation period; the microbial mobility weight is 0.15, assessed by the diffusion rate of the Bacteroides-Firmicutes symbiotic network; in the final dynamic hypergraph structure, the hyperedges represent the joint distribution of multimodal features. For example, the fusion entropy value of the hyperedge of APOE ε4 high permeability-hippocampal atrophy-β-hydroxybutyrate trough-Bacteroides low abundance is 1.2, and the node connection rules are determined by the spectral clustering results of the cross-modal similarity matrix; cross-modal fusion eliminates data heterogeneity, and the constructed dynamic hypergraph associates genetic risk, neurodegeneration, metabolic disorders and microbial imbalance, thereby comprehensively improving the analysis of pathological mechanisms.

[0025] The multidimensional association analysis module is based on dynamic causal inference to analyze potential intervention nodes where multiple disease mechanisms overlap. The structural equation model quantifies the interaction path between genetic permeability and imaging topological entropy. The path coefficient of the APOE ε4 locus on hippocampal atrophy is 0.57, and the metabolic phase angle dynamically adjusts the causal edge weight change by 0.12. The microbial migration rate calculates the cross-organ interaction gradient through tensor product. The intestinal-cerebrospinal fluid short-chain fatty acid diffusion gradient is 0.45, which is mapped as the key intervention edge in the causal graph.

[0026] The drug prediction coupling network convolves the causal topology tensor with the drug molecule interaction matrix; the causal topology tensor dimension is 256×256×128, and the drug molecule matrix includes the acetylcholinesterase inhibition coefficient of 0.85 for donepezil and the NMDA receptor antagonism coefficient of 0.72 for memantine; the three-dimensional convolution kernel size is adaptively adjusted to 16×16×8, and the kernel weight is initialized by the causal edge strength; in the intervention efficacy distribution calculation formula, the toxicity attenuation coefficient λ is set to 0.85, and the dosing time interval Δt is 24 hours; for example, the intervention efficacy of donepezil in the APOE ε4-hippocampal atrophy pathway is 0.78, and the efficacy of memantine in the microbial-metabolism coupling pathway is 0.63; the dynamic energy dissipation equation solves the risk accumulation rate in real time. When the cellular stress signal intensity reaches 50 units, the risk value is 0.35 per hour; the multi-mechanism coupling targets are accurately identified, the drug combination coverage is improved, and the mean square error of efficacy prediction is reduced.

[0027] The dual-domain risk stratification module predicts short-term toxicity thresholds using a metabolic entropy change model. A dynamic energy dissipation equation is constructed based on a blood-brain barrier permeability coefficient of 0.02 square centimeters per second and a cellular stress signal intensity of 65 units. The numerical solution of the partial differential equation shows a maximum concentration of β-hydroxybutyrate of 120 micromoles per liter. The connection weights of the bacterial interaction network are reduced by 15% via gradient descent optimization, inhibiting the activity of pro-inflammatory pathways. The long-term neurological degeneration trajectory integrates epigenetic drift data using the fourth-order Runge-Kutta method. The CpG methylation rate decays by 0.8% annually. Combined with the initial value of the hippocampal volume of 3200 cubic millimeters, the volume is predicted to be 2896 cubic millimeters after 5 years, with an annual atrophy rate of 2.1%. The toxicity threshold calibration module uses the clinical MMSE score as a benchmark, minimizing the root mean square error between the predicted and measured values to 1.8, and converges after 382 iterations. The short-term toxicity warning accurately predicts the time to exceed the cellular stress limit as 48 hours, and the long-term degeneration trajectory predicts a decrease in MMSE scores over 5 years, further improving the accuracy of risk stratification.

[0028] The adaptive intervention optimization module generates a medication regimen that balances efficacy and risk through counterfactual policy gradients. The objective function is designed to be 0.6 times the efficacy gain minus 0.4 times the risk accumulation rate, and the weights α and β are determined by Bayesian optimization. For example, the efficacy reward of the combination of 10 mg of donepezil and 20 mg of memantine is 1.5, the risk reward is 0.7, and the policy gradient is ; A real-time feedback mechanism dynamically adjusts dosing parameters; when the intensity of the cellular stress signal rises to 55 units, the dosing interval is shortened from 24 hours to 18 hours, and the memantine dose is reduced to 15 mg; the direction of the neurological function degeneration gradient backpropagation correction strategy, the hippocampal atrophy rate is reduced from 2.1% to 1.8% per year; the final output plan is donepezil 10 mg every 18 hours combined with memantine 15 mg every 24 hours, with a predicted efficacy gain of 1.8 corresponding to an improvement of 3 points in MMSE, a risk accumulation rate of 0.28 per hour, and a safety margin of 15%.

[0029] This system, centered on the data end, builds a complete closed loop from raw data to personalized dosing regimens through standardized preprocessing, cross-modal fusion, multi-mechanism modeling, dual-domain risk assessment, and dynamic optimization. Data such as SNP frequencies in genetic variation spectra, gradient tensors in neuroimaging, metabolic oscillation curves, and microbial networks are converted into actionable intervention strategies through streaming topological embedding and causal inference. Each module algorithm is implemented through open source frameworks such as TensorFlow and PyTorch, and deployed to the clinical decision support system after parameter configuration. This system improves the accuracy and efficiency of drug development for brain diseases, providing a reliable technical foundation for personalized treatment.

[0030] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A brain disease drug efficacy analysis and prediction system, characterized in that: include: A heterogeneous modality aggregation interface is used to receive and integrate multi-disease cross-pathology data streams and personalized biomarker time series. The multi-disease cross-pathology data streams include genetic variation spectra, neuroimaging topological features, metabolic oscillation patterns derived from metabolic trajectory vectors, and microbiome dynamic distribution matrices. The multidimensional association analysis module performs implicit association mapping on the output of the heterogeneous modality aggregation interface based on the drug prediction coupling network to generate a disease-drug-phenotype multidimensional mapping library, where the drug prediction coupling network infers potential intervention nodes where multiple disease mechanisms overlap through dynamic causality. A dual-domain risk stratification module configured to predict short-term drug response toxicity thresholds based on a metabolic entropy change model that constructs a dynamic energy dissipation equation using the blood-brain barrier permeability coefficient and the intensity of cellular stress signals, and to assess long-term neurodegeneration trajectories through epigenetic drift; The adaptive intervention optimization module connects the multidimensional association analysis module and the dual-domain risk stratification module, generates personalized drug combination sequences through counterfactual policy gradients, and modifies the spatiotemporal parameters of drug administration in real time to balance the efficacy gain and the risk accumulation rate.

2. The brain disease drug efficacy analysis and prediction system according to claim 1, characterized in that: The heterogeneous modality aggregation interface achieves cross-modal dynamic fusion through a collaborative architecture of streaming topological embedding and tensor recursive decomposition. The discrete sites of the genetic variation spectrum are mapped into a dynamic probabilistic graph structure based on a timestamp-aligned multi-source data channel, and the topological association features of SNP clusters are extracted through a graph attention mechanism. After the neuroimaging topological features are decomposed into multi-scale gradient tensors through three-dimensional convolution kernels, cortical connectivity fingerprints are generated through rotational invariance transformation; metabolic trajectory vectors are aligned through non-uniform sampling to construct the latent variable space mapping relationship between metabolic oscillation patterns and microbiome dynamic distribution matrices; the microbiome data stream is decoupled into a sparsely constrained bacterial interaction network and a continuously diffuse metabolite concentration field through a mixed dimensional projection strategy; the heterogeneous modality aggregation interface drives cross-domain feature calibration through a multimodal comparison framework, uses weight distribution to eliminate dimensional drift between modalities, and outputs a dynamic hypergraph structure that integrates genetic permeability, image topological entropy, metabolic phase angle, and microbial mobility.

3. The brain disease drug efficacy analysis and prediction system according to claim 2, characterized in that: The drug prediction coupling network in the multidimensional association analysis module constructs a causal topology of potential intervention nodes on the interactive path of genetic permeability and image topological entropy through dynamic causal inference. The dynamic causal inference dynamically adjusts the weight distribution of causal edges by analyzing the graph attention features of the genetic variation spectrum and the latent variable space projection relationship of the multi-scale gradient tensor of the neuroimaging, combining the phase angle of the metabolic trajectory vector, and calculates the cross-organ interaction strength using the tensor product of microbial mobility to generate an initial causal graph with overlapping multiple disease mechanisms. The drug prediction coupling network further analyzes the sparse connection pattern of the bacterial interaction network in the dynamic distribution matrix of the microbiome, aligns it in time with the continuous diffusion path of the metabolite concentration field, and generates a cross-modal causal intervention channel based on the spatiotemporal coupling relationship between the metabolic oscillation pattern constrained by the blood-brain barrier permeability coefficient and the cortical connection fingerprint. The causal inference path is dynamically corrected through the toxicity threshold fed back by the metabolic entropy change model, and the long-term impact of neurological degeneration on the causal topology is calibrated using the results of epigenetic drift assessment. Ultimately, a causal network structure is constructed that balances the risk accumulation rate with a dynamic energy dissipation equation.

4. The brain disease drug efficacy analysis and prediction system according to claim 3, characterized in that: The drug prediction coupling network performs a tensor convolution operation on the multimodal causal topology and the drug molecular interaction matrix to generate an intervention efficacy distribution covering the overlapping region of multiple mechanisms. The dynamic energy dissipation equation is constructed by the blood-brain barrier permeability coefficient and the cell stress signal intensity to balance the drug penetration efficiency and the energy dissipation rate of the cell membrane ion flux. The multidimensional association analysis module forms a three-dimensional disease-drug-phenotype mapping library by integrating the intervention efficacy distribution and the toxicity threshold output by the metabolic entropy change model. The efficacy gain of the drug combination is simulated through dynamic causal inference to simulate the intervention efficacy propagation path, and the risk accumulation rate is solved in real time through the dynamic energy dissipation equation.

5. The brain disease drug efficacy analysis and prediction system according to claim 1, characterized in that: The construction of the metabolic entropy change model in the dual-domain risk stratification module includes: first, non-uniform sampling alignment of the time series data of metabolic oscillation and microbial migration, and establishing a dynamic energy dissipation equation based on the blood-brain barrier permeability coefficient and cell stress signal intensity. , where D is the diffusion coefficient, C is the metabolite concentration, and S is the stress signal intensity; the metabolic phase angle is then integrated with the energy transfer path of the microbial interaction network through differential manifold modeling to generate a dynamic entropy change field that describes the coupling relationship between cross-organ metabolite gradients and cellular stress. , where J is the metabolite flux, ρ is the concentration, and Q is the cellular stressor term; then, the bacterial interaction network connection weights are optimized based on the diffusion path of the metabolite concentration field, and the metabolic oscillations are phase-modulated by the blood-brain barrier permeability coefficient; finally, the epigenetic drift assessment results are integrated, and the Runge-Kutta method is used to numerically integrate the neurodegeneration trajectory, outputting the toxicity threshold calibrated by the dynamic distribution of the microbiome to form a metabolic entropy change model.

6. The brain disease drug efficacy analysis and prediction system according to claim 1, characterized in that: The adaptive intervention optimization module generates a sequence of candidate drug combinations through counterfactual strategy gradients under the constraints of dynamic energy dissipation equations, including: calculating cross-organ drug penetration efficiency based on the tensor product of metabolic phase angle and microbial mobility, and constructing the spatiotemporal distribution of drug concentration in combination with the blood-brain barrier permeability coefficient; simulating the intervention efficacy propagation path of different drug administration strategies in the causal topology through dynamic causal inference, and using the cell stress signal intensity fed back by the metabolic entropy change model to calibrate the drug response toxicity gradient in real time; the adaptive intervention optimization module uses epigenetic drift assessment results to perform backpropagation correction on the trajectory of neurological function degeneration, dynamically adjusts the exploration direction of the counterfactual strategy gradient, and ultimately outputs an individualized drug administration plan that balances efficacy gain and risk accumulation rate.

7. A method for analyzing and predicting the efficacy of brain disease drugs according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Receives and integrates multi-disease cross-pathology data streams and personalized biomarker time series, including genetic variation profiles, neuroimaging topological features, metabolic oscillation patterns derived from metabolic trajectory vectors, and microbiome dynamic distribution matrices. S2. Based on the drug prediction coupling network, implicit association mapping is performed on the output of the heterogeneous modality aggregation interface to generate a disease-drug-phenotype multidimensional mapping library, where the drug prediction coupling network infers potential intervention nodes where multiple disease mechanisms overlap through dynamic causality; S3. Predict short-term drug response toxicity thresholds based on a metabolic entropy change model, and assess long-term neurodegeneration trajectories through epigenetic drift. The metabolic entropy change model constructs a dynamic energy dissipation equation based on the blood-brain barrier permeability coefficient and the intensity of cellular stress signals. S4. Connect the multidimensional association analysis module with the dual-domain risk stratification module to generate personalized drug combination sequences through counterfactual policy gradients, and modify the spatiotemporal parameters of drug administration in real time to balance the efficacy gain and the risk accumulation rate.

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