Medicine collaborative development system capable of improving efficiency
By using metal sulfide adsorbents, the problem of real-time correlation between dynamic changes in hydrogen bond networks and activity data in existing technologies has been solved. This has enabled the alignment of the timeliness dimension of hydrogen bond network characteristics and protein binding energy in drug co-development systems, improving the real-time nature of prediction results and optimizing resource allocation in the validation pathway, thereby shortening the drug development cycle.
Patent Information
- Application Number
- CN202511112086.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-08
- Publication Date
- 2025-12-30
AI Technical Summary
In existing drug co-development systems, it is difficult to capture the real-time correlation between dynamic changes in hydrogen bond networks and activity data, protein binding energy does not match the dimensions of hydrogen bond features, validation of path-dependent empirical rules leads to insufficient prediction confidence, and cross-validation of metabolic stability data is lagging, making it difficult to meet the needs of efficient drug co-development.
The timeliness dimension of hydrogen bond network features and protein binding energy is aligned through a multimodal data fusion module. Time-sensitive attention mechanism and cross-modal attention mechanism are used to dynamically adjust weight factors. Combined with metabolic stability feedback, the topology of the verification path is dynamically reconstructed to optimize resource allocation and experimental data timeliness.
It improves the cross-modal correlation accuracy between hydrogen bond network features and protein binding energy, ensuring that the prediction results reflect the latest experimental results in real time, shortening the drug development cycle, and providing technical support for the screening and validation of complex drug combinations. It solves the problem of path dependency in the reserve validation of technical problems in the prior art, and provides dynamic optimization of the reserve validation path for generating and reconstructing the reserve validation path.
Smart Images
Figure CN121237257A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data fusion technology, specifically to a drug co-development system that improves efficiency. Background Technology
[0002] In current drug co-development, molecular dynamics simulations, in vitro screening, and experimental validation data are often processed in a fragmented manner, lacking a dynamic correlation framework across modalities. Existing systems mostly employ static weighted fusion models, which struggle to capture the real-time correlation between dynamic changes in hydrogen bond networks (such as bond length fluctuations and breakage / recombination frequencies) and activity data, leading to significant biases in structure-activity relationship modeling. For example, traditional methods often ignore the timeliness differences in experimental feedback at different time stamps when integrating three-dimensional conformations and activity parameters, causing historical data to interfere with current predictions.
[0003] Furthermore, the mismatch between the dimensions of protein binding energy and hydrogen bond features has long been a problem. Existing alignment methods rely on fixed mapping rules and cannot dynamically adjust weighting factors, resulting in insufficient confidence in synergistic effect predictions. During the validation phase, cross-validation of metabolic stability data and synergistic effect parameters lags behind, and validation paths rely on empirical rules for generation, resulting in a fixed topology that cannot optimize node order based on resource consumption and real-time feedback. Although some studies have attempted to introduce attention mechanisms to optimize data alignment, joint modeling of time decay effects and dynamic topology reconstruction is still lacking, making it difficult to meet the needs of efficient drug synergistic development. Summary of the Invention
[0004] The purpose of this invention is to provide an efficient drug co-development system to address the problems mentioned in the background. Specific technologies include how to align the timeliness dimension of hydrogen bond network features with protein binding energy readings to solve the problem of dynamic weight factor lag in cross-modal data fusion; and how to dynamically reconstruct the verification path topology based on metabolic stability feedback to solve the mismatch between verification node resource allocation and experimental data timeliness.
[0005] To achieve the above objectives, the present invention provides the following technical solution:
[0006] This efficient drug co-development system includes a multimodal data fusion module, a heterogeneous data alignment module, a synergistic effect prediction module, and a validation path generation and reconstruction module, wherein:
[0007] The multimodal data fusion module integrates molecular dynamics three-dimensional conformation data, in vitro screening activity data and experimental verification feedback data in real time. The time stamp alignment algorithm is used to achieve millisecond-level data synchronization. The hydrogen bond interaction analysis algorithm is used to extract the density distribution of hydrogen bond sites, the dynamic range of bond length changes and the frequency of breakage and recombination, and constructs a hydrogen bond feature matrix to characterize the strength of intermolecular interactions.
[0008] Furthermore, the multimodal data fusion module performs high-order tensor decomposition on the hydrogen bond feature matrix and the real-time captured activity parameter matrix using a bilinear interactive calculation method, introduces molecular structure complementarity constraints to generate an association matrix, and characterizes the strength of the synergistic effect. At the same time, a time-sensitive attention mechanism is used to exponentially decay the influence weight of historical verification results, ensuring the spatiotemporal consistency of the fused data and providing high-precision input for the generation of dynamic weight factors.
[0009] The heterogeneous data alignment module compresses the hydrogen bond feature matrix into a hydrogen bond latent vector that matches the dimension of the protein binding energy reading through a bilinear mapping layer. It then uses an adaptive pooling algorithm to resample the protein binding energy reading into time slices synchronized with the molecular dynamics simulation, generating a protein binding energy vector. Subsequently, a cross-modal attention mechanism is used to calculate the cosine similarity between the hydrogen bond latent vector and the protein binding energy vector, generating a dynamic weighting factor. At the same time, a time-sensitive decay function is used to encode the timestamps of the experimental feedback data into exponential decay coefficients to correct the timeliness of the dynamic weighting factor, thus solving the problem of dimensional alignment lag between hydrogen bond network features and protein binding energy readings.
[0010] The synergistic effect prediction module performs channel scaling on the hydrogen bond features of candidate compound combinations through a gating graph attention layer, captures the nonlinear correlation between conformational complementarity and activity synergistic gain using a cross-modal cross-attention mechanism, and dynamically adjusts the training weights based on the timestamps of experimental validation data during backpropagation to improve the model's sensitivity to timely data.
[0011] Furthermore, the synergy prediction module maps the combined features to the synergy space through a nonlinear projection layer, and uses a dual-channel normalized exponential function to output the conformation matching probability and the active synergy strength. The product of the two generates a joint confidence score. Based on the timeliness threshold, the confidence score of historical validation is exponentially decayed to ensure that the prediction results reflect the latest experimental feedback in real time and output a high-confidence synergy assessment.
[0012] The generation unit in the verification path generation and reconstruction module integrates the synergistic effect confidence and compound metabolic stability data through a priority embedding algorithm. It prioritizes the selection of combinations whose synergistic effect confidence exceeds the preset confidence threshold and whose metabolic stability meets the standard as the first-round verification nodes, and generates an initial verification path with resource consumption assessment parameters as edge weights.
[0013] The reconstruction unit in the verification path generation and reconstruction module analyzes the statistical correlation between metabolic stability data and dynamic weight factors through rank correlation coefficient. When it is detected that the dynamic weight factor is in a high range but the metabolic stability is lower than the preset threshold, topology reconstruction is triggered. At the same time, a time-sensitive attention mechanism is used to recalibrate the decay coefficient of the dynamic weight factor of the newly added nodes to ensure that the temporal sequence of the reconstructed network is synchronized with the experimental verification feedback, and to dynamically optimize the resource allocation path.
[0014] Compared with the prior art, the beneficial effects of the present invention are:
[0015] By using multimodal data fusion and time-sensitive dynamic weight alignment technology, the cross-modal correlation accuracy of hydrogen bond network features and protein binding energy readings is improved, effectively eliminating redundant interference from historical data; a dynamic reconstruction mechanism for the verification path based on metabolic stability feedback optimizes resource allocation priority in real time and reduces invalid experimental steps.
[0016] Meanwhile, by employing cross-modal attention mechanisms and time-sensitivity decay correction, the prediction results are ensured to accurately reflect the latest experimental feedback; the topology adaptive adjustment capability of the verification node network is further shortened, thus providing full-process technical support for the efficient screening and validation of complex drug combinations. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the overall modules of the present invention;
[0018] Figure 2 This is a schematic diagram of the verification path generation and reconstruction module unit of the present invention.
[0019] In the diagram: 100, Multimodal data fusion module; 200, Heterogeneous data alignment module; 300, Synergistic effect prediction module; 400, Validation path generation and reconstruction module; 401, Generation unit; 402, Reconstruction unit. Detailed Implementation
[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Next, please refer to Figure 1 The present invention provides a technical solution: a drug co-development system with improved efficiency, including a multimodal data fusion module 100, a heterogeneous data alignment module 200, a synergistic effect prediction module 300, and a validation path generation and reconstruction module 400.
[0022] The multimodal data fusion module 100 captures in real time three-dimensional conformational data generated by molecular dynamics simulation, activity data of in vitro screening systems (e.g., dose-response activity data), and experimental verification feedback data through a distributed interface; it uses a time stamp alignment algorithm to eliminate acquisition delay between devices and ensure that each data stream is synchronized within millisecond precision; it uses a hydrogen bond interaction analysis algorithm to extract hydrogen bond interaction network features from the three-dimensional conformational data, specifically including the density distribution of hydrogen bond sites, the dynamic range of bond length changes, and the frequency of breakage and recombination, and constructs a hydrogen bond feature matrix characterizing the strength of intermolecular interactions;
[0023] An activity parameter matrix is generated by capturing in vitro screening activity data under different concentration gradients in real time. Through deep feature fusion technology, the hydrogen bond interaction network features and the concentration gradient changes of the activity data are matched across modes. A bilinear interactive calculation method is used to perform high-order tensor decomposition on the hydrogen bond feature matrix and the activity parameter matrix to capture the nonlinear correlation pattern between the two.
[0024] Based on this, the molecular structure complementarity constraint is introduced, and a graph structure neural network is used to mine molecular combinations with spatial conformational complementarity to generate an association matrix characterizing the intensity of synergistic effect. Each element is calculated through a nonlinear transformation function, reflecting the conformational matching degree and activity synergistic potential of a specific molecular pair.
[0025] The time-aligned experimental verification feedback data is injected into the correlation matrix through a dynamic decay mechanism to construct spatiotemporal fusion features. Specifically, a time-sensitive attention mechanism is adopted to calculate the decay coefficient based on the time stamp of the experimental verification feedback data, and to exponentially decay the influence weight of historical verification results. Through feature correction and superposition operations, fusion data containing spatiotemporal consistency is finally formed, providing downstream models with multi-dimensional inputs that simultaneously cover spatial structure characteristics, changes in activity intensity, and the timeliness of experimental feedback.
[0026] The multimodal data fusion module 100 integrates 3D conformational data, activity data, and experimental feedback data in real time through a time-stamped alignment algorithm, achieving millisecond-level synchronization (solving the data dispersion problem); it uses a hydrogen bond interaction analysis algorithm to extract hydrogen bond density distribution, bond length dynamic range, and breakage and recombination frequency to construct a hydrogen bond feature matrix (solving the problem that static models cannot capture dynamic changes); it combines bilinear interactive calculation with molecular structure complementarity constraints to generate an association matrix, and uses a time-sensitive attention mechanism to exponentially decay historical verification results (eliminating historical data interference) to form spatiotemporally consistent fused data, providing cross-modal dynamic association input for downstream applications.
[0027] The heterogeneous data alignment module 200 first employs tensor mapping technology to perform multi-scale feature transformation between the hydrogen bond interaction network features (including a three-dimensional spatial feature matrix of hydrogen bond density distribution, bond length dynamic changes, and breakage frequency) and the protein binding energy readings (one-dimensional time-series scalar sequences) in the experimental verification feedback data. Specifically, this includes:
[0028] By constructing a bilinear mapping layer, the hydrogen bond feature matrix is compressed into a hydrogen bond potential vector that matches the dimension of the protein binding energy reading. At the same time, an adaptive pooling algorithm is used to resample the protein binding energy readings at different sampling frequencies into millisecond-level time slices synchronized with molecular dynamics simulations, which serve as the protein binding energy vector.
[0029] Next, a cross-modal attention mechanism is adopted to generate a dynamic weighting factor by calculating the cosine similarity between the hydrogen bond potential vector and the protein binding energy vector. A time-sensitive decay function is introduced to encode the timestamp of the experimental feedback data into an exponential decay coefficient to correct the timeliness of the dynamic weighting factor.
[0030] Finally, a differentiable gating mechanism is used to inject the spatiotemporally decayed dynamic weighting factor into the feature channel of the hydrogen bond network, thereby achieving adaptive calibration of structural features and experimental readings in the spatiotemporal dimension. This ensures that the weights of early experimental results decay exponentially with data aging, while the latest validation results have a dominant influence on feature representation.
[0031] The heterogeneous data alignment module 200 compresses the hydrogen bond feature matrix into a potential vector that matches the protein binding energy readings through a bilinear mapping layer (solving the dimension mismatch problem). It then combines an adaptive pooling algorithm to resample the protein binding energy readings by time slices (synchronizing temporal differences). A cross-modal attention mechanism is used to calculate cosine similarity to generate dynamic weight factors. The experimental timestamps are encoded through a time-sensitive decay function to dynamically correct the timeliness of the weight factors (solving the weight lag problem), thus achieving spatiotemporal adaptive calibration of structural features and experimental readings.
[0032] The synergistic effect prediction module 300 employs a multi-stage iterative training mechanism, iteratively training the fused data and dynamic weight factors to output the confidence level of the synergistic effect of candidate compound combinations, specifically including:
[0033] For each candidate compound combination, spatiotemporal fusion features (such as the three-dimensional distribution of hydrogen bond network, activity correlation matrix and time decay correction) and dynamic weight factors are input into the graph structure neural network. The hydrogen bond features of molecules within the combination are channel-scaled through the gated graph attention layer to enhance the structural interaction mode that is highly relevant to the current experimental verification.
[0034] By utilizing a cross-modal cross-attention mechanism, the weighted hydrogen bond features and activity parameter matrix of each candidate compound combination are bidirectionally interacted. Multi-head attention calculation is used to capture the nonlinear correlation between the conformational complementarity (such as the spatial matching degree of hydrogen bond sites) and the synergistic gain of activity (such as the synergistic offset of the dose-response curve) of the combination, while preserving the original feature distribution to maintain the combination specificity.
[0035] In backpropagation, the training weights of each candidate compound combination are dynamically adjusted based on the timestamps of the experimental validation data. The prediction error of recent combinations is given a higher penalty weight, forcing the model to prioritize fitting the latest validation results.
[0036] By mapping the combined features to the synergistic effect space through a nonlinear projection layer, a dual-channel normalized exponential function is used to output the conformational matching probability (reflecting the three-dimensional structural complementarity) and the activity synergy strength (quantifying the activity gain of the combined drug) of the combination. The product of the two generates the joint confidence score. At the same time, a timeliness threshold (such as experimental data within 72 hours) is introduced to exponentially decay the confidence scores of historically validated combinations, ensuring that the final output results simultaneously cover the spatial adaptation potential of molecule pairs, the activity synergy efficiency, and the timeliness of experimental evidence. The synergistic effect confidence scores of candidate compound combinations are output, providing an interpretable ranking of the synergistic effect confidence scores of candidate compound combinations.
[0037] The synergistic effect prediction module 300 utilizes a gated graph attention layer to enhance the correlation of hydrogen bond feature channels and captures the nonlinear correlation between conformational complementarity and activity synergistic gain through a cross-modal cross-attention mechanism (improving prediction accuracy). It combines a nonlinear projection layer and a dual-channel normalized exponential function to output the joint confidence of conformational matching probability and activity synergistic strength, and performs exponential decay correction on historical validation results based on a timeliness threshold (solving the problem of insufficient prediction timeliness), outputting a high-confidence synergistic effect assessment that reflects experimental feedback in real time.
[0038] Please see Figure 2 The generation unit 401 in the verification path generation and reconstruction module 400 constructs a verification node network containing resource consumption assessment parameters based on the synergistic effect confidence level, and generates an initial verification path containing compound metabolic stability data, specifically including:
[0039] Based on the list of candidate compound combinations ranked by synergistic effect confidence, and combined with resource consumption assessment parameters (such as experimental reagent costs, equipment occupancy time, and man-hours), a network topology with verification nodes is constructed. Each node corresponds to the verification task of a specific compound combination. The node attributes include compound metabolic stability data (such as liver microsomal half-life), experimental resource allocation requirements, and dynamic weighting factors (time-sensitive calibration coefficients from the heterogeneous data alignment module 200).
[0040] By using a priority embedding algorithm, the confidence level of the synergistic effect is fused with the compound metabolic stability data. The combination of the synergistic effect confidence level exceeding the preset confidence threshold and the compound metabolic stability data exceeding the preset stability threshold is selected as the first-round verification node. At the same time, graph embedding technology is used to encode the resource consumption parameters as edge weights, forming an initial verification path that minimizes the total resource cost and includes the compound metabolic stability data.
[0041] The reconstruction unit 402 in the verification path generation and reconstruction module 400 cross-validates the compound metabolic stability data with dynamic weighting factors, and triggers the topology adjustment of the verification node network based on the cross-validation results, specifically including:
[0042] Cross-validation is achieved by constructing a cross-validation model based on metabolic stability. The metabolic stability data of compounds carried in the initial validation path are spatiotemporally aligned and validated with dynamic weighting factors. Statistical correlation analysis is used to detect the correlation between metabolic stability data and dynamic weighting factors. When the dynamic weighting factor is in a high range and the corresponding metabolic stability data is lower than the preset stability threshold, the topology of the validation node network is adjusted.
[0043] The topology adjustment of the verification node network involves removing mismatched nodes using a subgraph pruning algorithm. Based on the similarity of the embedding vectors of the graph convolutional network, candidate combinations that meet the metabolic stability criteria and match hydrogen bond features are retrieved from historical data as new nodes. A dynamic edge weight redistribution mechanism combined with a multi-objective optimization algorithm is used to optimize the experimental resource allocation requirements and the updated metabolic stability data, regenerating a reconstruction path that meets resource consumption constraints and maintains the integrity of spatiotemporal fusion features. At the same time, a time-sensitive attention mechanism is used to recalibrate the attenuation coefficient of the dynamic weight factors of the new nodes to ensure that the timestamps of each node in the reconstructed network are synchronized with the timeliness of the experimental verification feedback data.
[0044] The generation unit 401 in the validation path generation and reconstruction module 400 uses a priority embedding algorithm to fuse synergistic effect confidence and metabolic stability data, and selects qualified combinations to generate the initial validation path with optimal resource consumption (solving the problem of validation path experience dependence); the reconstruction unit 402 triggers topology adjustment through rank correlation coefficient analysis, combines subgraph pruning algorithm to remove mismatched nodes, dynamically reallocates edge weights and introduces time-effect decay coefficient calibration (solving the problem of metabolic stability feedback delay), and realizes dynamic synchronous optimization of validation network and experimental data.
[0045] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. An efficient drug synergistic development system, characterized by, The application relates to a multi-modal data fusion module (100), a heterogeneous data alignment module (200), a synergistic effect prediction module (300) and a verification path generation and reconstruction module (400), wherein: The multi-modal data fusion module (100) is used for real-time integration of three-dimensional conformation data of molecular dynamics simulation, activity data of in-vitro screening and experimental verification feedback data, hydrogen bond network characteristics in the three-dimensional conformation data are analyzed, cross-modal correlation matching is carried out with the activity data, and fusion data containing time-space consistency are generated. The heterogeneous data alignment module (200) aligns the hydrogen bond network characteristics with the protein binding energy readings in the experimental verification feedback data in the dimension, generates a dynamic weight factor, and dynamically adjusts the timeliness of the dynamic weight factor according to the time stamp of the experimental verification feedback data. The synergistic effect prediction module (300) iteratively trains the fusion data and the dynamic weight factor, and outputs the synergistic effect confidence of the candidate compound combination. The verification path generation and reconstruction module (400) constructs a verification node network containing resource consumption evaluation parameters based on the synergistic effect confidence, generates an initial verification path containing compound metabolic stability data, and cross- verifies the compound metabolic stability data and the dynamic weight factor, and triggers the topology structure adjustment of the verification node network based on the cross-verification result.
2. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The multi-modal data fusion module (100) realizes millisecond-level synchronization of the three-dimensional conformation data, the activity data and the experimental verification feedback data through a time mark alignment algorithm, extracts the density distribution of the hydrogen bond site, the dynamic change range of the bond length and the fracture and recombination frequency as the hydrogen bond network characteristics by using a hydrogen bond effect analysis algorithm, and constructs a hydrogen bond feature matrix representing the intermolecular interaction strength.
3. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The multi-modal data fusion module (100) generates an activity parameter matrix by capturing the activity data of in-vitro screening under different concentration gradients in real time, performs high-order tensor decomposition on the hydrogen bond feature matrix and the activity parameter matrix by using a bilinear interaction calculation method, introduces a molecular structure complementarity constraint condition, generates a correlation matrix representing the synergistic effect strength, and forms fusion data containing time-space consistency by exponentially attenuating the influence weight of the historical verification result through a time-sensitive attention mechanism.
4. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The heterogeneous data alignment module (200) compresses the hydrogen bond feature matrix into a hydrogen bond potential vector matched with the dimension of the protein binding energy readings through a bilinear mapping layer, and resamples the protein binding energy readings into a time slice synchronized with the molecular dynamics simulation by using an adaptive pooling algorithm to generate a protein binding energy vector.
5. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The heterogeneous data alignment module (200) generates a dynamic weight factor by calculating the cosine similarity of the hydrogen bond potential vector and the protein binding energy vector through a cross-modal attention mechanism, and encodes the time stamp of the experimental feedback data into an exponential attenuation coefficient by using a time-sensitive attenuation function to correct the timeliness of the dynamic weight factor.
6. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The synergistic effect prediction module (300) scales the hydrogen bond features of the candidate compound combination through a gated graph attention layer, captures the nonlinear correlation between conformational complementarity and active synergistic gain using a cross-modal cross-attention mechanism, and dynamically adjusts the training weights according to the timestamps of the experimental validation data during backpropagation.
7. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The synergistic effect prediction module (300) maps the combination features to the synergistic effect space through a nonlinear projection layer, adopts a double-channel normalized exponential function to output the conformational matching probability and the active synergistic strength, the product of the two generates the joint confidence, and based on the timeliness threshold, the confidence of the historical verification is exponentially decayed and corrected, and the synergistic effect confidence of the candidate compound combination is output.
8. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The verification path generation and reconstruction module (400) includes a generation unit (401), which fuses the synergistic effect confidence and the compound metabolic stability data through a priority embedding algorithm, preferentially selects combinations with synergistic effect confidence exceeding a preset confidence threshold and compound metabolic stability data exceeding a preset stability threshold as first-round verification nodes, and generates an initial verification path with resource consumption evaluation parameters as edge weights.
9. The efficiency-improved drug synergistic development system according to claim 1, characterized by, The verification path generation and reconstruction module (400) includes a reconstruction unit (402), which triggers network reconstruction through metabolic stability cross-validation, and adopts a time-sensitive attention mechanism to recalibrate the decay coefficient of the dynamic weight factor of the new node, to ensure that the timestamp of the reconstructed network is synchronized with the experimental validation feedback data.
10. The efficiency-improved drug synergistic development system according to claim 9, characterized by, The cross-validation analyzes the statistical correlation between the metabolic stability data and the dynamic weight factor using the rank correlation coefficient, and triggers topology reconstruction when the dynamic weight factor is detected to be in a high interval and the corresponding metabolic stability data is lower than the preset stability threshold.