A digital-twinmed pharmaceutical data analysis system

Abstract

The application relates to the field of artificial intelligence medical technology, and particularly discloses a digital-twin-based drug data analysis system, which comprises a data fusion module, a multi-layer digital-twin modeling module, a cross-domain causal and mechanism fusion module and an optimized decision module. The adaptive calibration mechanism and the lightweight generation fusion mechanism are introduced, the dynamic adjustment and update of the individual layer drug reaction trend are realized, and the drug reaction prediction of each individual is more accurate and personalized. The data fusion module is used to standardize, semantically map and feature align the data of the molecular layer, the individual layer, the group layer and the production quality layer, a unified drug data base is constructed, the utilization efficiency of multi-source heterogeneous data is improved, and a reliable foundation is provided for digital-twin modeling. The pharmacology and clinical prior knowledge are integrated into the multi-layer digital-twin model through the cross-domain causal and mechanism fusion module, the causal analysis of the drug-target-pathway-clinical outcome is realized, and the scientificity and interpretability of the prediction result are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence medical technology, in particular to a digital twin drug data analysis system. BACKGROUND

[0002] With the rapid development of artificial intelligence in the medical field, a large amount of multi-level, multi-source heterogeneous data has been accumulated in the process of drug research and development and clinical drug use, including drug molecule information, individual clinical data, population test data and production quality data. However, the existing drug reaction prediction technology still has the following problems:

[0003] 1. Individual differences lead to unpredictable drug reactions: traditional methods mainly predict drug reactions based on population average data, lack dynamic calibration and prediction ability for individual differences, and are difficult to accurately reflect the drug reaction trend of each individual;

[0004] 2. Difficulty in integrating multi-source heterogeneous drug data: the existing technology lacks a unified data standardization and feature alignment mechanism when processing molecular, individual, population and production quality layer data, resulting in low data utilization efficiency and affecting prediction accuracy;

[0005] 3. Difficulty in systematically integrating drug reaction mechanism knowledge: existing methods cannot effectively integrate pharmacological knowledge and clinical prior information, and drug reaction prediction results lack explainability and scientificity, limiting their application in personalized medicine. SUMMARY

[0006] In view of the above situation, in order to overcome the defects of the prior art, the present application provides a digital twin drug data analysis system, which, in order to overcome the problem of individual differences leading to unpredictable drug reactions, introduces an adaptive calibration mechanism and a lightweight generation fusion mechanism to realize dynamic adjustment and update of individual drug reaction trends, making drug reaction prediction for each individual more accurate and personalized; in order to overcome the problem of difficulty in integrating multi-source heterogeneous drug data, a data fusion module is used to standardize, semantically map and align the features of molecular, individual, population and production quality layer data, to build a unified drug data base and improve the utilization efficiency of multi-source heterogeneous data, providing a reliable foundation for digital twin modeling; in order to overcome the problem of difficulty in systematically integrating drug reaction mechanism knowledge, a cross-domain causal and mechanism fusion module is used to integrate pharmacological and clinical prior knowledge into a multi-layer digital twin model, to realize causal analysis of drug-target-pathway-clinical outcome and improve the scientificity and explainability of the prediction results, providing a basis for individualized control strategy.

[0007] The technical scheme adopted by the present application is as follows: the digital twin drug data analysis system provided by the present application comprises a data fusion module, a multi-layer digital twin modeling module, a cross-domain causal and mechanism fusion module and an optimization decision module, and specifically comprises the following contents:

[0008] The data fusion module collects drug data, including drug level data, individual level data, population level data, and production quality level data, processes the drug data using semantic mapping and feature alignment technology, and obtains a unified drug data base;

[0009] The multi-layer digital twin modeling module constructs a multi-level digital twin based on the unified drug data base, including molecular layer twin, individual layer twin, and population layer twin;

[0010] The cross-domain causal and mechanism fusion module introduces pharmacological and clinical prior knowledge in the multi-level digital twin, constructs a causal structure of drug-target-pathway-clinical outcome, and uses causal inference and counterfactual analysis methods for analysis to obtain causal inference results;

[0011] The optimization decision module introduces a multi-objective method based on the multi-level digital twin and the causal inference results, and outputs individualized regulation strategies.

[0012] Further, in the data fusion module, the drug data includes drug level data, individual level data, population level data, and production quality level data, and specifically includes the following contents:

[0013] Drug level data: drug molecular structure, physicochemical property data, pharmacokinetic parameters, and pharmacodynamic parameters;

[0014] Individual level data: genomics, proteomics, clinical test data, medical image data, pathological state, genetic background, and environmental factors;

[0015] Population level data: laboratory test data, clinical trial data, and clinical medication data;

[0016] Production quality level data: drug production process and quality control data.

[0017] Further, the multi-layer digital twin modeling module constructs a multi-level digital twin based on the unified drug data base, specifically including the following steps:

[0018] Step S1: Molecular layer feature extraction, calling the processed drug level data from the unified drug data base, combining molecular dynamics simulation, graph neural network, and feature encoding method, extracting molecular layer key action features, including binding affinity, metabolic pathway parameters, and toxicology indicators;

[0019] Step S2: Molecular layer twin modeling, based on the molecular layer key action features, combining quantum chemistry calculation and deep learning hybrid model, constructing a molecular layer digital twin, simulating drug-target interaction mechanism;

[0020] Step S3: Individual data representation, calling individual-level data from the unified drug data base, using multi-modal representation learning and mixed effect model to model individual differences, and obtaining individual-level drug response features;

[0021] Step S4: Individual twin modeling, based on individual-level drug response features, constructing individual-level digital twin, introducing adaptive calibration and lightweight generation fusion mechanism, and obtaining dynamically updated drug response prediction;

[0022] Step S5: Group data aggregation, calling group-level data from the unified drug data base, using hierarchical Bayesian modeling and multi-density clustering method to identify population drug differences and efficacy patterns;

[0023] Step S6: Group twin modeling, based on individual twin, constructing group digital twin, realizing long-term drug efficacy evaluation, epidemiological trend prediction and public health strategy support.

[0024] Further, step S4, specifically comprising the following steps:

[0025] Step S41: Call individual-level drug response features, standardize and multi-dimensional feature selection of individual-level drug response features, remove redundant features and enhance key effect factors, and obtain processed drug response features;

[0026] Step S42: Preliminary digital twin generation, constructing preliminary digital twin according to the processed drug response features, making preliminary prediction for each feature dimension, and generating individual-level drug response trend sequence;

[0027] Step S43: Introduce adaptive calibration mechanism, based on the preliminary digital twin, introduce recurrent convolutional long short-term memory network to construct adaptive calibration module, and gradually correct the predicted drug response trend sequence to obtain calibrated drug response features. After adaptive calibration mechanism, the calibrated twin model is obtained as the adaptive calibration result;

[0028] Step S44: Introduce lightweight generation fusion mechanism, in the calibrated twin model, construct lightweight generation fusion module, introduce double-branch architecture of candidate reaction construction unit and reaction effect evaluation unit, the candidate reaction construction unit generates candidate reaction sequence according to the calibrated drug response features, the reaction effect evaluation unit discriminates feedback, and the lightweight generation fusion result is obtained;

[0029] Step S45: Digital twin optimization, fusion of adaptive calibration results and lightweight generation fusion results, final optimization of the calibrated twin model, output of dynamic updated drug response prediction, including ADME process and adverse reaction dynamics of drugs in vivo.

[0030] Further, step S43 specifically includes the following steps:

[0031] Step S431: Sequence difference calculation, collect historical individual layer drug response trend sequence, combine individual layer drug response trend sequence, construct difference function, calculate residual value, the formula used is as follows:

[0032] ;

[0033] Wherein, represents the residual value, represents the individual layer drug response trend sequence, represents the historical individual layer drug response trend sequence, represents the time index;

[0034] Step S432: Adaptive weight fusion, introduce adaptive weight, fuse between historical individual layer drug response trend sequence and individual layer drug response trend sequence, get weighted individual layer drug response trend sequence, the formula used is as follows:

[0035] ;

[0036] Wherein, represents the weighted individual layer drug response trend sequence obtained, represents the adaptive weight;

[0037] Step S433: Gating calibration update, based on the residual value calculated in step S431 and the weighted individual layer drug response trend sequence fused in step S432, construct a gating calibration unit, dynamically adjust the gating parameter combined with the residual value, update the weighted individual layer drug response trend sequence, the formula used is as follows:

[0038] ;

[0039] Wherein, represents the calibrated individual layer drug response trend sequence, represents the gating parameter;

[0040] Step S434: calibrating the twin output, setting a residual threshold, gradually converging the preliminary digital twin, repeating the iteration step S433 until the residual value converges to the residual threshold, outputting the calibrated individual layer drug reaction trend sequence, mapping the generated calibrated drug reaction characteristics, and outputting the calibrated twin model obtained through the adaptive calibration mechanism.

[0041] Further, step S44 specifically includes the following steps:

[0042] Step S441: candidate reaction sequence construction, based on the calibrated drug reaction characteristics output in step S434, constructing a candidate reaction sequence, using a lightweight generation network for sequence generation, and the formula used is as follows:

[0043] ;

[0044] Wherein, is the candidate reaction sequence, is the lightweight generation network, is the calibrated drug reaction characteristics;

[0045] Step S442: reaction effect evaluation, constructing a reaction effect evaluation unit, discriminating and feeding back the candidate reaction sequence, and obtaining sequence discrimination feedback results;

[0046] Step S443: lightweight generation fusion, combining the candidate reaction sequence and the sequence discrimination feedback results to obtain the lightweight generation fusion candidate reaction sequence as the lightweight generation fusion result.

[0047] The beneficial effects achieved by the present application using the above-mentioned scheme are as follows:

[0048] (1) In view of the problem that individual differences lead to unpredictable drug reactions, the adaptive calibration mechanism and lightweight generation fusion mechanism are introduced to realize dynamic adjustment and update of individual layer drug reaction trends, making drug reaction prediction for each individual more accurate and personalized;

[0049] (2) In view of the problem of difficulty in integrating multi-source heterogeneous drug data, the data fusion module is used to standardize, semantically map and feature align the molecular layer, individual layer, population layer and production quality layer data, construct a unified drug data base, improve the utilization efficiency of multi-source heterogeneous data, and provide a reliable foundation for digital twin modeling;

[0050] (3) In view of the problem that drug reaction mechanism knowledge is difficult to be systematically integrated, through the cross-domain causal and mechanism fusion module, pharmacological and clinical prior knowledge is integrated into the multi-layer digital twin model, realizing causal analysis of drug-target-pathway-clinical outcome, improving the scientificity and explainability of the prediction results, and providing basis for individualized regulation strategy. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A schematic diagram of a digital twin drug data analysis system according to the present application.

[0052] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0054] Embodiment one, refer to Figure 1 The present application provides a digital twin drug data analysis system, which comprises a data fusion module, a multi-layer digital twin modeling module, a cross-domain causal and mechanism fusion module and an optimization decision module, and specifically comprises the following contents:

[0055] The data fusion module collects drug data, including drug level data, individual level data, population level data and production quality level data, and processes the drug data by using semantic mapping and feature alignment technology to obtain a unified drug data base;

[0056] The multi-layer digital twin modeling module constructs a multi-layer digital twin body according to the unified drug data base, including a molecular layer twin, an individual layer twin and a population layer twin;

[0057] The cross-domain causal and mechanism fusion module introduces pharmacological and clinical priori knowledge in the multi-layer digital twin body, constructs a causal structure of drug-target-pathway-clinical outcome, and analyzes by using causal inference and counterfactual analysis method to obtain a causal inference result;

[0058] The optimization decision module introduces a multi-objective method based on the multi-layer digital twin body and the causal inference result, and outputs an individualized regulation strategy.

[0059] Embodiment two, based on the above-mentioned embodiment, in the data fusion module, the drug data comprises drug level data, individual level data, population level data and production quality level data, and specifically comprises the following contents:

[0060] Drug level data: drug molecular structure, physicochemical property data, pharmacokinetic parameters and pharmacodynamic parameters;

[0061] Individual level data: genomics, proteomics, clinical test data, medical image data, pathological state, genetic background and environmental factors;

[0062] Population level data: laboratory test data, clinical trial data and clinical medication data;

[0063] Production quality level data: drug production process and quality control data.

[0064] In example three, based on the above examples, the multi-level digital twin modeling module constructs a multi-level digital twin according to the unified drug data base, specifically including the following steps:

[0065] Step S1: molecular layer feature extraction, calling processed drug level data from the unified drug data base, combining molecular dynamics simulation, graph neural network and feature encoding method, extracting molecular layer key action features, including binding affinity, metabolic pathway parameters and toxicology indicators;

[0066] Step S2: molecular layer twin modeling, based on the molecular layer key action features, combining quantum chemical calculation and deep learning hybrid model, constructing molecular layer digital twin, simulating drug-target interaction mechanism;

[0067] Step S3: individual data representation, calling individual level data from the unified drug data base, using multi-modal representation learning and hybrid effect model to model individual differences, obtaining individual layer drug response features;

[0068] Step S4: individual layer twin modeling, based on individual layer drug response features, constructing individual layer digital twin, introducing adaptive calibration and lightweight generation fusion mechanism, obtaining dynamic updated drug response prediction;

[0069] Step S5: population data aggregation, calling population level data from the unified drug data base, using hierarchical Bayesian modeling and multi-density clustering method, identifying population medication differences and efficacy patterns;

[0070] Step S6: population layer twin modeling, based on the individual layer twin, constructing the population layer digital twin, realizing drug long-term efficacy evaluation, epidemiological trend prediction and public health strategy support.

[0071] In this embodiment, the antihypertensive drug amlodipine is taken as an example, based on the unified drug data base, a multi-level digital twin is constructed, realizing drug response prediction from the molecular layer, individual layer to the population layer;

[0072] First, in the molecular layer modeling process, the molecular structure, physicochemical properties and pharmacokinetic parameters of amlodipine are called from the drug data base, combined with molecular dynamics simulation, the binding process of amlodipine with calcium ion channel target protein is dynamically simulated; at the same time, the molecular graph is encoded by using the graph neural network, and the key action features including binding affinity, metabolic pathway parameters and potential toxicology indexes are extracted, based on the above features, the molecular layer digital twin is constructed by a hybrid model of quantum chemical calculation and deep learning, so as to reproduce the interaction mechanism of amlodipine and calcium ion channel in the virtual environment;

[0073] In the individual layer modeling process, the genomic data, clinical test data, medical image and lifestyle information of patient A are called from the drug data base, fused by multi-modal representation learning method, and combined with mixed effect model to obtain the individual layer drug response characteristics of patient A, and the individual layer digital twin of patient A is constructed. On the basis of the preliminary predicted blood pressure response trend sequence, an adaptive calibration mechanism is introduced to iteratively correct the prediction results. When there is a residual error between the predicted blood pressure drop value and the actual monitoring value, the prediction sequence is dynamically updated through the gate calibration unit, and a lightweight generation fusion mechanism is introduced to generate possible blood pressure change candidate sequences, which are screened and fed back through the effect evaluation module, so as to obtain a more realistic individual response prediction.

[0074] In the population layer modeling process, the long-term follow-up data and multi-center clinical medication data of the clinical trial population are called from the drug data base, and the differences in drug response of different genetic background and lifestyle populations are identified by hierarchical Bayesian modeling and multi-density clustering method. Based on the modeling results of the individual layer twin, the population layer digital twin is constructed to simulate the long-term efficacy distribution of amlodipine in the population, predict its antihypertensive effect and potential adverse reaction trend in different populations, and provide basis for public health policy making and clinical medication guidance.

[0075] Embodiment four, based on the above embodiment, step S4, specifically includes the following steps:

[0076] Step S41: calling the individual layer drug response characteristics, standardizing and multi-dimensional feature screening the individual layer drug response characteristics, removing redundant features and enhancing key effect factors to obtain processed drug response characteristics;

[0077] Step S42: preliminary digital twin generation, constructing a preliminary digital twin according to the processed drug response characteristics, preliminarily predicting each feature dimension, and generating an individual layer drug response trend sequence;

[0078] Step S43: Introduce an adaptive calibration mechanism. On the basis of the preliminary digital twin, an adaptive calibration module is constructed by introducing a recurrent convolutional long short-term memory network to gradually correct the predicted drug response trend sequence, and the calibrated drug response characteristics are obtained. The calibrated twin model obtained through the adaptive calibration mechanism is used as the adaptive calibration result.

[0079] Step S44: Introduce a lightweight generation fusion mechanism. In the calibrated twin model, a lightweight generation fusion module is constructed, and a double-branch architecture of a candidate reaction construction unit and a reaction effect evaluation unit is introduced. The candidate reaction construction unit generates a candidate reaction sequence according to the calibrated drug response characteristics, and the reaction effect evaluation unit performs discriminative feedback to obtain a lightweight generation fusion result.

[0080] Step S45: Digital twin optimization. The adaptive calibration result and the lightweight generation fusion result are fused, and the calibrated twin model is finally optimized to output dynamic updated drug response prediction, including the ADME process of the drug in the body and adverse reaction dynamics.

[0081] In this embodiment, in a clinical study of a new antidepressant drug, the system selects the multi-source data of patient B as input, including gene sequencing results, basic liver and kidney function tests, past drug use history, blood concentration monitoring data, and clinical adverse reaction scores. First, these data are standardized and feature selected to eliminate redundant information and retain key factors closely related to drug metabolism, including drug metabolism enzyme activity, plasma half-life parameters, and adverse reaction sensitivity indicators.

[0082] On this basis, the system generates a preliminary digital twin of patient B to predict the metabolic trend of the drug in the body. The preliminary results show that the blood drug concentration of patient B will remain in the effective range within 12 hours after drug administration, but will drop below the efficacy threshold after 24 hours. The adaptive calibration mechanism is introduced, and the prediction result is corrected by a recurrent convolutional long short-term memory network. Combined with the patient's past history of antidepressant drug use and current liver function indicators, the model dynamically adjusts the metabolic rate prediction. The calibrated results show that the concentration of patient B starts to decrease at 16 hours after drug administration, and the probability of mild gastrointestinal adverse reactions increases.

[0083] In the calibrated twin model, a lightweight generation fusion mechanism is introduced. The candidate reaction construction unit generates multiple possible drug reaction trajectories, including "normal metabolism", "metabolic delay with mild adverse reactions", and "rapid metabolism with insufficient efficacy". The reaction effect evaluation unit discriminates and selects, and finally outputs the most likely two cases: one is that the drug concentration decreases after 16 hours, leading to insufficient efficacy, and the other is that the patient has a 30% probability of mild gastrointestinal discomfort within the first 24 hours.

[0084] The adaptive calibration result and the lightweight fusion result are comprehensively optimized, and a dynamically updated individualized drug reaction prediction model is output. Through the optimized digital twin, the doctor can intuitively obtain the drug concentration change trajectory of patient B at different time points, the ADME process and the potential adverse reaction risk, and adjust the drug administration scheme accordingly, including shortening the drug administration interval and reducing the single dose, to balance the efficacy and safety.

[0085] In the fifth embodiment, based on the above-mentioned embodiments, step S43 specifically comprises the following steps:

[0086] Step S431: sequence difference calculation, collecting the historical individual layer drug reaction trend sequence, combining the individual layer drug reaction trend sequence, constructing the difference function, and calculating the residual value, the formula used is as follows:

[0087] ;

[0088] Among them, Residual value, Individual layer drug reaction trend sequence, Historical individual layer drug reaction trend sequence, Time index;

[0089] Step S432: adaptive weight fusion, introducing adaptive weight, fusing between historical individual layer drug reaction trend sequence and individual layer drug reaction trend sequence, obtaining weighted individual layer drug reaction trend sequence, the formula used is as follows:

[0090] ;

[0091] Among them, Obtained weighted individual layer drug reaction trend sequence, Adaptive weight;

[0092] Step S433: gating calibration update, based on the residual value calculated in step S431 and the weighted individual layer drug reaction trend sequence fused in step S432, constructing a gating calibration unit, dynamically adjusting the gating parameter combined with the residual value, updating the weighted individual layer drug reaction trend sequence, the formula used is as follows:

[0093] ;

[0094] Among them, Calibrated individual layer drug reaction trend sequence, Gating parameter;

[0095] Step S434: Calibrate twin output, set residual threshold, step-by-step convergence of preliminary digital twin, repeat iteration step S433 until residual value converges to residual threshold, output calibrated individual layer drug reaction trend sequence, map generate calibrated drug reaction features, output calibrated twin model obtained through adaptive calibration mechanism.

[0096] In this embodiment, the code used is as follows:

[0097] Input:

[0098] H_t: historical individual layer drug reaction trend sequence

[0099] S_t: current individual observed drug reaction trend sequence

[0100] max_iter: maximum number of iterations

[0101] residual_threshold: residual convergence threshold

[0102] Output:

[0103] S_calibrated: calibrated individual layer drug reaction trend sequence

[0104] features: mapped drug reaction features

[0105] Steps:

[0106] 1. Initialization:

[0107] S_calibrated = S_t

[0108] residual = ∞

[0109] iter = 0

[0110] 2. Loop iteration until residual converges or maximum number of iterations is reached:

[0111] while residual > residual_threshold and iter < max_iter:

[0112] / / Step S431: Sequence difference calculation

[0113] R_t = S_calibrated - H_t

[0114] / / Step S432: Adaptive weight fusion

[0115] w_t = compute_adaptive_weight(R_t) / / dynamically compute weight according to residual

[0116] F_t = w_t * H_t + (1 - w_t) * S_calibrated

[0117] / / Step S433: gating update

[0118] G_t = gate_update(R_t) / / gating parameter, can be based on sigmoid or residual adjustment

[0119] S_calibrated = G_t * S_calibrated + (1 - G_t) * F_t

[0120] / / Update residual and record iteration

[0121] residual = compute_residual(S_calibrated, H_t)

[0122] iter += 1

[0123] 3. Step S434: calibrated twin output

[0124] features = map_to_features(S_calibrated) / / map calibrated sequence to generate drug response features

[0125] Return:

[0126] S_calibrated, features.

[0127] Embodiment six, which is based on the above embodiment, step S44, specifically includes the following steps:

[0128] Step S441: candidate response sequence construction, based on the calibrated drug response features output by step S434, construct a candidate response sequence, use a lightweight generation network for sequence generation, the formula used is as follows:

[0129] ;

[0130] wherein, is the candidate response sequence, is the lightweight generation network, is the calibrated drug response feature;

[0131] Step S442: Reaction effect evaluation, construct a reaction effect evaluation unit to judge and feedback the candidate reaction sequence, and obtain a sequence judgment feedback result;

[0132] Step S443: Lightweight generation fusion, combine the candidate reaction sequence and the sequence judgment feedback result to obtain the lightweight generation fusion result.

[0133] In this embodiment, the code used is as follows:

[0134] Input:

[0135] X_hat_t: calibrated drug reaction features output in step S434

[0136] G_theta: lightweight generation network model

[0137] max_iter: maximum number of iterations for judgment

[0138] Output:

[0139] C_fused: lightweight generation fusion candidate reaction sequence

[0140] Steps:

[0141] 1. Step S441: Candidate reaction sequence construction

[0142] C_t = G_theta(X_hat_t) / / Use lightweight generation network to generate candidate reaction sequence

[0143] 2. Step S442: Reaction effect evaluation

[0144] for iter in 1 to max_iter:

[0145] feedback = evaluate_effect(C_t) / / Construct a reaction effect evaluation unit to judge and feedback the sequence

[0146] C_t = update_sequence(C_t, feedback) / / Adjust the candidate sequence according to the feedback

[0147] 3. Step S443: Lightweight generation fusion

[0148] C_fused = fuse_sequence(C_t, feedback) / / Combine the candidate sequence and the judgment feedback result to obtain the final sequence

[0149] Return:

[0150] C_fused.

[0151] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. It is also possible in the present disclosure that steps can be executed in different sequence where is graphically or explicitly stated or indicated. Further, it is also possible in some instances, to combine or omit certain steps. Moreover, several actions can be executed in one action; similarly, one action can be distributed in several actions. Additionally, it should be understood that any figures which have been provided can be used to graphically illustrate implementation of methods only and should not be construed as limiting.

[0152] While the embodiments of the application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

[0153] The above description of the application and its embodiments is not intended to limit the application, as the application is only limited by the appended claims and their equivalents.

Claims

1. A digital twin-based drug data analysis system, characterized in that: It includes a data fusion module, a multi-layer digital twin modeling module, a cross-domain causal and mechanism fusion module, and an optimization decision-making module, specifically including the following: The data fusion module collects drug data, including drug-level data, individual-level data, group-level data, and production quality-level data. It processes the drug data using semantic mapping and feature alignment techniques to obtain a unified drug data foundation. The multi-layer digital twin modeling module constructs multi-layer digital twins based on a unified drug data foundation, including molecular-level twins, individual-level twins, and population-level twins. The cross-domain causality and mechanism fusion module introduces pharmacological and clinical prior knowledge into a multi-level digital twin to construct a causal structure of drug-target-pathway-clinical outcome. It then uses causal inference and counterfactual analysis methods to analyze the causal inference results. The optimization decision-making module is based on multi-level digital twins and causal inference results, introduces a multi-objective method, and outputs individualized control strategies. The multi-layer digital twin modeling module constructs a multi-layer digital twin based on a unified drug data foundation, specifically including the following steps: Step S1: Molecular-level feature extraction. Processed drug-level data is retrieved from a unified drug data base. Combining molecular dynamics simulation, graph neural networks, and feature encoding methods, key molecular-level action features are extracted, including binding affinity, metabolic pathway parameters, and toxicological indicators. Step S2: Molecular-level twin modeling. Based on the key role characteristics of the molecular level, and combining quantum chemical calculations and deep learning hybrid models, a molecular-level digital twin is constructed to simulate the drug-target interaction mechanism. Step S3: Individual data representation. Individual-level data is retrieved from a unified drug data foundation. Multimodal representation learning and mixed-effects models are used to model individual differences and obtain individual-level drug response characteristics. Step S4: Individual-level twin modeling. Based on individual-level drug response characteristics, construct individual-level digital twins, introduce adaptive calibration and lightweight generation fusion mechanisms, and obtain dynamically updated drug response predictions. Step S5: Population data aggregation. Population-level data is retrieved from a unified drug data base. Hierarchical Bayesian modeling and multi-density clustering methods are used to identify differences in drug use and efficacy patterns among populations. Step S6: Population-level twin modeling. Based on the individual-level twins, construct a population-level digital twin to enable long-term drug efficacy assessment, epidemiological trend prediction, and public health strategy support.

2. The digital twin drug data analysis system according to claim 1, characterized in that: The data fusion module includes drug-level data, individual-level data, population-level data, and production quality-level data, specifically including the following: Drug-level data: drug molecular structure, physicochemical properties, pharmacokinetic parameters, and pharmacodynamic parameters; Individual-level data: genomics, proteomics, clinical testing data, medical imaging data, pathological status, genetic background, and environmental factors; Population-level data: laboratory testing data, clinical trial data, and clinical drug use data; Production quality data: pharmaceutical manufacturing process and quality control data.

3. The digital twin drug data analysis system according to claim 1, characterized in that: Step S4 specifically includes the following steps: Step S41: Call up the individual-level drug response features, standardize and screen the individual-level drug response features using multidimensional features, remove redundant features and enhance key effect factors to obtain the processed drug response features; Step S42: Preliminary digital twin generation. Based on the processed drug response characteristics, a preliminary digital twin is constructed. Preliminary predictions are made for each feature dimension to generate an individual-level drug response trend sequence. Step S43: Introduce an adaptive calibration mechanism. Based on the initial digital twin, introduce a recurrent convolutional long short-term memory network to construct an adaptive calibration module. Gradually correct the predicted drug response trend sequence to obtain the calibrated drug response characteristics. The calibrated twin model is obtained through the adaptive calibration mechanism and used as the adaptive calibration result. Step S44: Introduce a lightweight generation fusion mechanism. In the calibrated twin model, construct a lightweight generation fusion module and introduce a dual-branch architecture of candidate response construction unit and response effect evaluation unit. The candidate response construction unit generates candidate response sequences based on the calibrated drug response characteristics, and the response effect evaluation unit performs discriminative feedback to obtain the lightweight generation fusion result. Step S45: Digital twin optimization, fusing adaptive calibration results with lightweight generation fusion results, performing final optimization on the calibrated twin model, and outputting dynamically updated drug response predictions, including the ADME process of the drug in vivo and adverse reaction dynamics.

4. The digital twin drug data analysis system according to claim 3, characterized in that: Step S43 specifically includes the following steps: Step S431: Sequence difference calculation. Collect historical individual-level drug response trend sequences, combine them with the individual-level drug response trend sequences, construct a difference function, and calculate the residual values. The formula used is as follows: ； in, Represents the residual value. This represents a sequence of individual-level drug response trends. This represents a historical individual-level trend sequence of drug response. Indicates a time index; Step S432: Adaptive weighted fusion. Adaptive weights are introduced to fuse the historical individual-level drug response trend sequence and the individual-level drug response trend sequence to obtain the weighted individual-level drug response trend sequence. The formula used is as follows: ； in, This represents the obtained weighted individual-level drug response trend sequence. Indicates adaptive weights; Step S433: Gated calibration update. Based on the residual values ​​calculated in step S431 and the weighted individual-level drug response trend sequence obtained by fusion in step S432, a gated calibration unit is constructed. The gate parameters are dynamically adjusted in conjunction with the residual values ​​to update the weighted individual-level drug response trend sequence. The formula used is as follows: ； in, This represents the calibrated individual-level drug response trend sequence. Indicates the gating parameters; Step S434: Calibrate the twin output, set the residual threshold, gradually converge the initial digital twin, repeat step S433 iteratively until the residual value converges to the residual threshold, output the calibrated individual-level drug response trend sequence, map to generate the calibrated drug response features, and output the calibrated twin model obtained through the adaptive calibration mechanism.

5. A digital twin drug data analysis system according to claim 3, characterized in that: Step S44 specifically includes the following steps: Step S441: Candidate response sequence construction. Based on the calibrated drug response features output in step S434, candidate response sequences are constructed. A lightweight generation network is used to generate the sequences, and the formula used is as follows: ； in, Candidate reaction sequences, To create lightweight generative networks, The calibrated drug response characteristics; Step S442: Response effect assessment, constructing a response effect assessment unit, discriminating and feeding back candidate response sequences, and obtaining sequence discrimination feedback results; Step S443: Lightweight generation fusion, combining the candidate reaction sequence and the sequence discrimination feedback result to obtain the candidate reaction sequence after lightweight generation fusion, which is used as the lightweight generation fusion result.

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