Drug research and development and individualized treatment platform based on artificial neural network and multi-mode artificial intelligence technology
By using artificial neural networks and multimodal artificial intelligence technology, drug molecules are generated, drug targets and personalized treatment plans are predicted, solving the problems of long drug development cycles, high costs and insufficient personalized treatment plans in traditional drug development, and realizing efficient, safe drug development and precise personalized treatment.
Patent Information
- Application Number
- CN202512040097.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-05-19
AI Technical Summary
Traditional drug development is characterized by long cycles, high costs, and low success rates. Personalized treatment plans cannot meet the needs of every patient, and data privacy and cross-institutional collaboration challenges limit broad cooperation in drug development.
Employing artificial neural networks and multimodal artificial intelligence technologies, including variational autoencoders (VAE), generative adversarial networks (GAN), graph neural networks (GNN), reinforcement learning (RL), self-supervised learning (SSL), federated learning, and differential privacy, we can generate drug molecules, predict drug targets and personalized treatment plans, and achieve secure data sharing and collaboration.
Accelerate the drug discovery process, improve drug generation efficiency and quality, optimize target prediction and personalized treatment, ensure data privacy, enhance treatment efficacy and data security, and support cross-institutional collaboration.
Smart Images
Figure CN122067594A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of pharmaceutical science, specifically relating to a drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology. Background Technology
[0002] With the deepening of biomedical research and the development of modern technology, drug development and personalized treatment are facing increasing challenges. Traditional drug development is characterized by long cycles, high costs, and low success rates, especially in areas such as new drug design, drug target prediction, and adverse drug reaction detection. The complexity and uncertainty of drug development make the process of bringing new drugs to market fraught with risk. Furthermore, due to significant individual differences, existing standardized treatment plans often fail to meet the needs of every patient, making precise personalized treatment plans a pressing issue for the medical community.
[0003] Against this backdrop, modern artificial intelligence technologies, especially machine learning, deep learning, and federated learning, are gradually becoming effective means to solve these problems. AI technologies can significantly improve the efficiency of drug molecule generation, optimize drug target prediction, and achieve optimal treatment outcomes for each patient through real-time adjustments to personalized treatment. Furthermore, how to leverage cross-institutional data collaboration while ensuring data privacy and security is also a key issue in current drug development and clinical applications.
[0004] Through the integrated application of technologies such as artificial neural networks, multimodal intelligence, reinforcement learning, federated learning, and differential privacy, drug development and personalized medicine are entering a new era of intelligence. This platform not only improves the efficiency of drug molecule generation and the accuracy of target prediction, but also significantly enhances treatment efficacy and data security through personalized treatment optimization and data collaboration, providing strong technical support for future drug development and precision medicine. Summary of the Invention
[0005] The purpose of this invention is to provide a drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology, the technical features of which are:
[0006] To achieve the above technical objectives, this invention provides the following technical solution: a drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology, comprising the following specific steps:
[0007] S1. Data preparation and preprocessing: Collect multimodal drug data, target data, and clinical data, and perform standardized processing.
[0008] S2. Model building and training: Use VAE and GAN to generate drug molecules, use GNN to predict drug-target interactions, optimize individualized treatment plans through reinforcement learning, use self-supervised learning to predict adverse drug reactions, and utilize federated learning and differential privacy to protect sensitive data and achieve cross-institutional collaboration.
[0009] S3. Model Validation and Optimization: Validate model performance through laboratory and clinical trials, and optimize it based on feedback data;
[0010] S4. System Deployment and Application: Integrate all modules into the drug development and treatment platform to improve the efficiency and accuracy of drug development and personalized treatment.
[0011] Furthermore, step 1 includes the following steps:
[0012] S11. Collect multimodal data from different sources, including multimodal drug data (describes the chemical structure information of drug molecules, such as molecular formula, atom type, bond length, angle, etc.), target data (describes the protein target information of drug molecules, such as protein sequence, structure, functional annotation, etc.), and clinical data (including patients' individual characteristics, medical history, genomic data, drug treatment plans and corresponding treatment effects).
[0013] S12. Data cleaning: To ensure data consistency and integrity, multiple interpolation methods are used. Fill in missing values in the data, where, Indicates missing values; It represents the weight of relevant data, calculates the weighted average to infer missing values, and fills in the missing data based on the closest known data;
[0014] S13. Perform Z-score standardization on data with different dimensions. In this formula, x represents the original data, μ represents the mean of the feature, and σ represents the standard deviation of the feature. By using this formula, we convert each data point into a standard normal distribution with a mean of 0 and a standard deviation of 1.
[0015] Furthermore, step 2 includes the following steps;
[0016] S21. Using VAE and GAN to generate drug molecules, the variational autoencoder (VAE) generates new drug molecules from the potential space of known drug molecules, and the generative adversarial network (GAN) generates drug molecules that meet the efficacy criteria through adversarial training of the generator and discriminator. By using VAE and GAN in combination, this platform can generate a variety of candidate drug molecules and can continuously improve during training to screen out molecules that meet the efficacy and safety criteria.
[0017] S22. Using GNN to predict drug-target interactions, drug molecules and protein targets can be modeled as a graph structure, where drug molecules and protein targets are nodes in the graph, and the edges between nodes represent chemical bonds between molecules or interactions between proteins and drugs. Graph Neural Network (GNN) It can propagate information between nodes, learn the interaction between drugs and targets, and thus predict the effect of drugs on specific targets. This represents the feature representation of node v at the k-th layer. This represents the set of neighboring nodes of node v, that is, other nodes connected to node v. This represents the weight matrix of the k-th layer, which is applied to the feature representation of the nodes. It is the bias term of the k-th layer. These are activation functions, such as ReLU or Sigmoid, used to introduce non-linear features to enhance the model's expressive power and ensure that the features of nodes can capture complex interactions at different levels.
[0018] S23. Through reinforcement learning, the system can dynamically adjust drug dosage and treatment plan based on the patient's real-time health status, optimizing the individualized treatment path (Q-Learning). This is a policy optimization method used in reinforcement learning. By updating the Q-function, the system can learn the long-term reward of taking a certain action in a specific state, thereby finding the optimal action plan. It indicates the current status (such as the patient's health status, including disease parameters, vital signs, etc.). This indicates the current action being taken (such as adjusting the medication dosage or changing the treatment plan). This indicates an immediate reward (such as the degree of improvement in a patient's condition). This reward reflects the short-term impact of the current action on the patient's health. γ represents the discount factor, ranging from 0 to 1, reflecting the degree of emphasis on future rewards. A larger γ value indicates an emphasis on long-term returns. Indicates the next state At that time, take action The maximum achievable Q value. This is the system's estimate of the optimal outcome of future actions;
[0019] S24. In situations lacking sufficient labeled data, self-supervised learning can construct effective pre-training tasks, helping the model better understand the structure and properties of drug molecules. This, in turn, allows for efficient prediction of adverse drug reactions after fine-tuning. By designing auxiliary tasks (such as masking part of the drug structure information), the model can learn the characteristics of drug molecules from unlabeled data. This indicates drug molecules after partially concealing information. This means that certain features of the drug molecule are randomly masked as targets that the model needs to predict. This represents the true molecular characteristics of the drug, serving as a benchmark for comparing the model's prediction results. It is the cross-entropy loss function, used to measure the difference between the model's prediction and the true value;
[0020] S25. By leveraging federated learning and differential privacy to protect sensitive data, collaborative training among multiple organizations can be achieved by allowing institutions to share model parameters rather than the data itself. This effectively addresses the balance between data privacy and model performance. Differential privacy further enhances privacy protection, ensuring data security for all participants even during parameter sharing. The combination of federated learning and differential privacy provides a secure and effective solution for cross-institutional collaboration on sensitive data.
[0021] Furthermore, the use of VAEs and GANs to generate drug molecules includes the following specific aspects:
[0022] Variational autoencoders (VAEs) learn a latent spatial representation of molecular features from known drug molecule data and generate new drug molecules by sampling within this space. It is responsible for transforming the input drug molecule x into distribution parameters (mean μ and standard deviation σ) in the latent space to capture the intrinsic characteristics of the drug molecule, where drug molecule x is mapped to the latent space to generate its latent variable z. Let x represent the mean of the numerator. This represents its standard deviation; the latent space is a multidimensional normal distribution used to generate new drug molecules. Decoder The latent variables z generated by the encoder are sampled to reconstruct and generate a new drug molecule x′. The decoder, controlled by parameter θ, transforms the latent variables back into observable molecular forms. The loss function of VAE is... It includes reconstruction error and Kullback-Leibler (KL) divergence, where reconstruction error represents a measure of the similarity between the generated molecule x and the input molecule x, and KL divergence represents the measure of ensuring that the latent spatial distribution of the encoder output approximates a standard normal distribution. The first term... This is used to measure the similarity between the generated molecule x′ and the input x, ensuring that the generated molecule has the expected efficacy. (Second item) Ensure that the latent spatial distribution of the encoder is consistent with the standard normal distribution p(z) to guarantee the generation of diverse molecules;
[0023] Generative Adversarial Networks (GANs) generate drug molecules that meet efficacy criteria through adversarial training of a generator and a discriminator. Potential drug molecules are generated from random noise z, and the generation results are continuously improved to deceive the discriminator. Here, z is random noise sampled from the latent space, and the generator uses z to generate new molecular structures. (Discriminator) The discriminator's task is to distinguish whether the input molecule x comes from a real drug molecule dataset or is a spurious molecule generated by the generator. By improving its ability to identify spurious molecules, the discriminator drives the generator to produce results that more closely resemble real drug molecules. The loss function of a GAN... Used to train the generator and discriminator, enabling the generator to produce realistic drug molecules and the discriminator to accurately distinguish between real and fake molecules. (First item) The score representing the discriminator's judgment of real drug molecules, the second item. This represents the discriminator's judgment score for the fake molecules generated by the generator. By maximizing the first term and minimizing the second term, the generator and discriminator compete with each other during training, and the generator continuously learns to generate more realistic molecules.
[0024] Furthermore, federated learning and differential privacy include the following specific aspects:
[0025] Federal Learning This involves multiple institutions collaboratively training a global model by sharing model parameters without sharing local data. Each institution trains its model locally using its own data and sends the trained model parameters to a central server for aggregation. The central server updates the global model by aggregating the model parameters from all participants and distributes it to each institution. This process can be iterated repeatedly until the global model converges. This represents the global model parameters after the t-th training round. These parameters are calculated by weighted averaging of the local model parameters from each institution. This represents the model parameters trained locally by the k-th institution. This represents the amount of local data at the k-th institution, and N is the total amount of data across all participating institutions.
[0026] Differential privacy This ensures that even if an attacker obtains the model parameters, they cannot infer the specific content of the original data. Here, M(D) represents the model processing procedure applying differential privacy protection, and f(D) represents the data processing function. This represents normally distributed noise with a mean of 0 and a variance of . Noise is added to the model parameters to protect privacy data.
[0027] Furthermore, step 3 includes the following steps:
[0028] S31, Accuracy The model measures the proportion of correct predictions, where TP represents true positives (the number of samples correctly predicted as positive), TN represents true negatives (the number of samples correctly predicted as negative), FP represents false positives (the number of samples incorrectly predicted as positive), and FN represents false negatives (the number of samples incorrectly predicted as negative).
[0029] S32, Accuracy Recall is the proportion of true positives among samples predicted as positive. The proportion of all positive samples that are correctly predicted as positive is measured in relation to the F1 score. The above measures the performance of the balanced model on imbalanced datasets, particularly for predicting adverse drug reactions or evaluating treatment regimens.
[0030] S33. The ROC curve is used to evaluate the performance of a binary classification model under different decision thresholds. AUC is the area under the ROC curve. The closer the value is to 1, the better the model performance.
[0031] S34. Optimize through online learning or batch updates, applying regularization techniques. Where λ is the regularization coefficient. These are the model parameters. Regularization reduces model complexity and prevents overfitting by constraining the size of these parameters.
[0032] Furthermore, step 4 includes the following:
[0033] This system integrates multiple sub-modules (such as drug generation, target prediction, adverse reaction prediction, and personalized treatment optimization) into a single system, enabling them to work collaboratively and achieve a complete drug development process. It creates independent API interfaces for each model, allowing modules to communicate and exchange data through standardized interfaces. After platform deployment, it continuously monitors model performance and system operation, building an automated model update pipeline. The platform should have good scalability, supporting the addition of new modules or updates to existing ones. A real-time monitoring system is deployed to monitor the operational status, resource consumption, and model performance of each module. It provides users (such as doctors and researchers) with a simple and easy-to-use interface, supporting operations such as querying and modifying treatment plans and generating reports, thereby improving the efficiency of drug development and optimizing personalized treatment pathways for patients.
[0034] The beneficial effects of this invention are as follows:
[0035] 1. Drug generation and optimization: Through variational autoencoders (VAE) and generative adversarial networks (GAN), the platform can generate new drug molecules with target characteristics, which greatly shortens the time for new drug development and improves the diversity and quality of candidate drugs.
[0036] 2. Target prediction: Using a graph neural network (GNN) model, the platform can effectively learn the complex interactions between drug molecules and protein targets, thereby improving the accuracy of drug target prediction and helping to discover new targets.
[0037] 3. Personalized treatment: Through reinforcement learning (RL), the platform can dynamically adjust the treatment plan based on the patient's real-time data, ensuring that each patient receives the best treatment path tailored to their individual needs, thereby improving treatment effectiveness and reducing unnecessary drug side effects.
[0038] 4. Adverse reaction prediction: Self-supervised learning (SSL) learns the characteristics of drug molecules in unlabeled data, helping the model to accurately predict potential adverse drug reactions. Especially when data is insufficient, SSL can effectively improve prediction performance and ensure drug safety.
[0039] 5. Data Collaboration and Privacy Protection: Federated learning allows multiple institutions to share model parameters for collaborative training without sharing data, thus protecting the privacy of sensitive information. Differential privacy technology further ensures that data is not leaked during model training by adding noise, achieving data security while improving model performance.
[0040] 6. Model Optimization: The platform continuously optimizes model parameters and enhances its adaptability through laboratory testing and clinical feedback data, ensuring that the platform can maintain efficient operation in different data sources and complex environments.
[0041] 7. System Integration and Automation: The platform integrates various modules of drug development and uses automated tools to achieve real-time monitoring and updates, which greatly improves development efficiency and can quickly respond to the individualized needs of different patients, thereby improving the accuracy of treatment.
[0042] In summary, this invention not only accelerates the drug discovery process but also improves the accuracy and safety of drug development through artificial intelligence technology, particularly in optimizing personalized treatment plans, which significantly enhances patient outcomes. Furthermore, the platform protects data privacy while enabling cross-institutional collaboration, making drug development more efficient and safer. Attached Figure Description
[0043] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation
[0044] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0045] Drug development is characterized by its lengthy process, high costs, significant individual variability, and unpredictable adverse drug reactions. Traditional methods are inefficient in addressing these challenges, particularly in drug design, target prediction, and personalized treatment planning, where they consume substantial resources. Furthermore, data privacy and inter-institutional collaboration issues limit broad cooperation in drug development.
[0046] A drug discovery and personalized treatment platform based on artificial neural networks (ANNs) and multimodal artificial intelligence technologies utilizes techniques such as variational autoencoders (VAEs), generative adversarial networks (GANs), graph neural networks (GNNs), reinforcement learning (RL), and self-supervised learning (SSL) to accelerate drug generation, improve target prediction accuracy, optimize personalized treatment plans, and accurately predict adverse drug reactions. Through federated learning and differential privacy technologies, data privacy and security are ensured, supporting cross-institutional collaborative training. System integration and optimization further improve drug development efficiency and treatment precision. In one embodiment, such as... Figure 1 As shown, the drug discovery and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology includes the following specific steps:
[0047] S1. Data preparation and preprocessing: Collect multimodal drug data, target data, and clinical data, and perform standardized processing.
[0048] S2. Model building and training: Use VAE and GAN to generate drug molecules, use GNN to predict drug-target interactions, optimize individualized treatment plans through reinforcement learning, use self-supervised learning to predict adverse drug reactions, and utilize federated learning and differential privacy to protect sensitive data and achieve cross-institutional collaboration.
[0049] S3. Model Validation and Optimization: Validate model performance through laboratory and clinical trials, and optimize it based on feedback data;
[0050] S4. System Deployment and Application: Integrate all modules into the drug development and treatment platform to improve the efficiency and accuracy of drug development and personalized treatment.
[0051] Furthermore, step 1 includes the following steps:
[0052] S11. Collect multimodal data from different sources, including multimodal drug data (describes the chemical structure information of drug molecules, such as molecular formula, atom type, bond length, angle, etc.), target data (describes the protein target information of drug molecules, such as protein sequence, structure, functional annotation, etc.), and clinical data (including patients' individual characteristics, medical history, genomic data, drug treatment plans and corresponding treatment effects).
[0053] S12. Data cleaning: To ensure data consistency and integrity, multiple interpolation methods are used. Fill in missing values in the data, where, Indicates missing values; It represents the weight of relevant data, calculates the weighted average to infer missing values, and fills in the missing data based on the closest known data;
[0054] S13. Perform Z-score standardization on data with different dimensions. In this formula, x represents the original data, μ represents the mean of the feature, and σ represents the standard deviation of the feature. By using this formula, we convert each data point into a standard normal distribution with a mean of 0 and a standard deviation of 1.
[0055] Furthermore, step 2 includes the following steps;
[0056] S21. Using VAE and GAN to generate drug molecules, the variational autoencoder (VAE) generates new drug molecules from the potential space of known drug molecules, and the generative adversarial network (GAN) generates drug molecules that meet the efficacy criteria through adversarial training of the generator and discriminator. By using VAE and GAN in combination, this platform can generate a variety of candidate drug molecules and can continuously improve during training to screen out molecules that meet the efficacy and safety criteria.
[0057] S22. Using GNN to predict drug-target interactions, drug molecules and protein targets can be modeled as a graph structure, where drug molecules and protein targets are nodes in the graph, and the edges between nodes represent chemical bonds between molecules or interactions between proteins and drugs. Graph Neural Network (GNN) It can propagate information between nodes, learn the interaction between drugs and targets, and thus predict the effect of drugs on specific targets. This represents the feature representation of node v at the k-th layer. This represents the set of neighboring nodes of node v, that is, other nodes connected to node v. This represents the weight matrix of the k-th layer, which is applied to the feature representation of the nodes. It is the bias term of the k-th layer. These are activation functions, such as ReLU or Sigmoid, used to introduce non-linear features to enhance the model's expressive power and ensure that the features of nodes can capture complex interactions at different levels.
[0058] S23. Through reinforcement learning, the system can dynamically adjust drug dosage and treatment plan based on the patient's real-time health status, optimizing the individualized treatment path (Q-Learning). This is a policy optimization method used in reinforcement learning. By updating the Q-function, the system can learn the long-term reward of taking a certain action in a specific state, thereby finding the optimal action plan. It indicates the current status (such as the patient's health status, including disease parameters, vital signs, etc.). This indicates the current action being taken (such as adjusting the medication dosage or changing the treatment plan). This indicates an immediate reward (such as the degree of improvement in a patient's condition). This reward reflects the short-term impact of the current action on the patient's health. γ represents the discount factor, ranging from 0 to 1, reflecting the degree of emphasis on future rewards. A larger γ value indicates an emphasis on long-term returns. Indicates the next state At that time, take action The maximum achievable Q value. This is the system's estimate of the optimal outcome of future actions;
[0059] S24. In situations lacking sufficient labeled data, self-supervised learning can construct effective pre-training tasks, helping the model better understand the structure and properties of drug molecules. This, in turn, allows for efficient prediction of adverse drug reactions after fine-tuning. By designing auxiliary tasks (such as masking part of the drug structure information), the model can learn the characteristics of drug molecules from unlabeled data. This indicates drug molecules after partially concealing information. This means that certain features of the drug molecule are randomly masked as targets that the model needs to predict. This represents the true molecular characteristics of the drug, serving as a benchmark for comparing the model's prediction results. It is the cross-entropy loss function, used to measure the difference between the model's prediction and the true value;
[0060] S25. By leveraging federated learning and differential privacy to protect sensitive data, collaborative training among multiple organizations can be achieved by allowing institutions to share model parameters rather than the data itself. This effectively addresses the balance between data privacy and model performance. Differential privacy further enhances privacy protection, ensuring data security for all participants even during parameter sharing. The combination of federated learning and differential privacy provides a secure and effective solution for cross-institutional collaboration on sensitive data.
[0061] Furthermore, the use of VAEs and GANs to generate drug molecules includes the following specific aspects:
[0062] Variational autoencoders (VAEs) learn a latent spatial representation of molecular features from known drug molecule data and generate new drug molecules by sampling within this space. It is responsible for transforming the input drug molecule x into distribution parameters (mean μ and standard deviation σ) in the latent space to capture the intrinsic characteristics of the drug molecule, where drug molecule x is mapped to the latent space to generate its latent variable z. Let x represent the mean of the numerator. This represents its standard deviation; the latent space is a multidimensional normal distribution used to generate new drug molecules. Decoder The latent variables z generated by the encoder are sampled to reconstruct and generate a new drug molecule x′. The decoder, controlled by parameter θ, transforms the latent variables back into observable molecular forms. The loss function of VAE is... It includes reconstruction error and Kullback-Leibler (KL) divergence, where reconstruction error represents a measure of the similarity between the generated molecule x and the input molecule x, and KL divergence represents the measure of ensuring that the latent spatial distribution of the encoder output approximates a standard normal distribution. The first term... This is used to measure the similarity between the generated molecule x′ and the input x, ensuring that the generated molecule has the expected efficacy. (Second item) Ensure that the latent spatial distribution of the encoder is consistent with the standard normal distribution p(z) to guarantee the generation of diverse molecules;
[0063] Generative Adversarial Networks (GANs) generate drug molecules that meet efficacy criteria through adversarial training of a generator and a discriminator. Potential drug molecules are generated from random noise z, and the generation results are continuously improved to deceive the discriminator. Here, z is random noise sampled from the latent space, and the generator uses z to generate new molecular structures. (Discriminator) The discriminator's task is to distinguish whether the input molecule x comes from a real drug molecule dataset or is a spurious molecule generated by the generator. By improving its ability to identify spurious molecules, the discriminator drives the generator to produce results that more closely resemble real drug molecules. The loss function of a GAN... Used to train the generator and discriminator, enabling the generator to produce realistic drug molecules and the discriminator to accurately distinguish between real and fake molecules. (First item) The score representing the discriminator's judgment of real drug molecules, the second item. This represents the discriminator's judgment score for the fake molecules generated by the generator. By maximizing the first term and minimizing the second term, the generator and discriminator compete with each other during training, and the generator continuously learns to generate more realistic molecules.
[0064] Furthermore, federated learning and differential privacy include the following specific aspects:
[0065] Federal Learning This involves multiple institutions collaboratively training a global model by sharing model parameters without sharing local data. Each institution trains its model locally using its own data and sends the trained model parameters to a central server for aggregation. The central server updates the global model by aggregating the model parameters from all participants and distributes it to each institution. This process can be iterated repeatedly until the global model converges. This represents the global model parameters after the t-th training round. These parameters are calculated by weighted averaging of the local model parameters from each institution. This represents the model parameters trained locally by the k-th institution. This represents the amount of local data at the k-th institution, and N is the total amount of data across all participating institutions.
[0066] Differential privacy This ensures that even if an attacker obtains the model parameters, they cannot infer the specific content of the original data. Here, M(D) represents the model processing procedure applying differential privacy protection, and f(D) represents the data processing function. This represents normally distributed noise with a mean of 0 and a variance of . Noise is added to the model parameters to protect privacy data.
[0067] Furthermore, step 3 includes the following steps:
[0068] S31, Accuracy The model measures the proportion of correct predictions, where TP represents true positives (the number of samples correctly predicted as positive), TN represents true negatives (the number of samples correctly predicted as negative), FP represents false positives (the number of samples incorrectly predicted as positive), and FN represents false negatives (the number of samples incorrectly predicted as negative).
[0069] S32, Accuracy Recall is the proportion of true positives among samples predicted as positive. The proportion of all positive samples that are correctly predicted as positive is measured in relation to the F1 score. The above measures the performance of the balanced model on imbalanced datasets, particularly for predicting adverse drug reactions or evaluating treatment regimens.
[0070] S33. The ROC curve is used to evaluate the performance of a binary classification model under different decision thresholds. AUC is the area under the ROC curve. The closer the value is to 1, the better the model performance.
[0071] S34. Optimize through online learning or batch updates, applying regularization techniques. Where λ is the regularization coefficient. These are the model parameters. Regularization reduces model complexity and prevents overfitting by constraining the size of these parameters.
[0072] Furthermore, step 4 includes the following:
[0073] This system integrates multiple sub-modules (such as drug generation, target prediction, adverse reaction prediction, and personalized treatment optimization) into a single system, enabling them to work collaboratively and achieve a complete drug development process. It creates independent API interfaces for each model, allowing modules to communicate and exchange data through standardized interfaces. After platform deployment, it continuously monitors model performance and system operation, building an automated model update pipeline. The platform should have good scalability, supporting the addition of new modules or updates to existing ones. A real-time monitoring system is deployed to monitor the operational status, resource consumption, and model performance of each module. It provides users (such as doctors and researchers) with a simple and easy-to-use interface, supporting operations such as querying and modifying treatment plans and generating reports, thereby improving the efficiency of drug development and optimizing personalized treatment pathways for patients.
Claims
1. A drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology, characterized in that: The specific steps include the following: S1. Data preparation and preprocessing: Collect multimodal drug data, target data, and clinical data, and perform standardized processing. S2. Model building and training: Use VAE and GAN to generate drug molecules, use GNN to predict drug-target interactions, optimize individualized treatment plans through reinforcement learning, use self-supervised learning to predict adverse drug reactions, and utilize federated learning and differential privacy to protect sensitive data and achieve cross-institutional collaboration. S3. Model Validation and Optimization: Validate model performance through laboratory and clinical trials, and optimize it based on feedback data; S4. System Deployment and Application: Integrate all modules into the drug development and treatment platform to improve the efficiency and accuracy of drug development and personalized treatment.
2. The drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology according to claim 1, characterized in that, Step 1 includes the following steps: S11. Collect multimodal data from different sources, including multimodal drug data (describes the chemical structure information of drug molecules, such as molecular formula, atom type, bond length, angle, etc.), target data (describes the protein target information of drug molecules, such as protein sequence, structure, functional annotation, etc.), and clinical data (including patients' individual characteristics, medical history, genomic data, drug treatment plans and corresponding treatment effects). S12. Data cleaning: To ensure data consistency and integrity, multiple interpolation methods are used. Fill in missing values in the data, where, Indicates missing values; It represents the weight of relevant data, calculates the weighted average to infer missing values, and fills in the missing data based on the closest known data; S13. Perform Z-score standardization on data with different dimensions. In this formula, x represents the original data, μ represents the mean of the feature, and σ represents the standard deviation of the feature. By using this formula, we convert each data point into a standard normal distribution with a mean of 0 and a standard deviation of 1.
3. The drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology according to claim 1, characterized in that, Step 2 includes the following steps; S21. Using VAE and GAN to generate drug molecules, the variational autoencoder (VAE) generates new drug molecules from the potential space of known drug molecules, and the generative adversarial network (GAN) generates drug molecules that meet the efficacy criteria through adversarial training of the generator and discriminator. By using VAE and GAN in combination, this platform can generate a variety of candidate drug molecules and can continuously improve during training to screen out molecules that meet the efficacy and safety criteria. S22. Using GNN to predict drug-target interactions, drug molecules and protein targets can be modeled as a graph structure, where drug molecules and protein targets are nodes in the graph, and the edges between nodes represent chemical bonds between molecules or interactions between proteins and drugs. Graph Neural Network (GNN) It can propagate information between nodes, learn the interaction between drugs and targets, and thus predict the effect of drugs on specific targets. This represents the feature representation of node v at the k-th layer. This represents the set of neighboring nodes of node v, that is, other nodes connected to node v. This represents the weight matrix of the k-th layer, which is applied to the feature representation of the nodes. It is the bias term of the k-th layer. These are activation functions, such as ReLU or Sigmoid, used to introduce non-linear features to enhance the model's expressive power and ensure that the features of nodes can capture complex interactions at different levels. S23. Through reinforcement learning, the system can dynamically adjust drug dosage and treatment plan based on the patient's real-time health status, optimizing the individualized treatment path (Q-Learning). This is a policy optimization method used in reinforcement learning. By updating the Q-function, the system can learn the long-term reward of taking a certain action in a specific state, thereby finding the optimal action plan. It indicates the current status (such as the patient's health status, including disease parameters, vital signs, etc.). This indicates the current action being taken (such as adjusting the medication dosage or changing the treatment plan). This indicates an immediate reward (such as the degree of improvement in a patient's condition). This reward reflects the short-term impact of the current action on the patient's health. γ represents the discount factor, ranging from 0 to 1, reflecting the degree of emphasis on future rewards. A larger γ value indicates an emphasis on long-term returns. Indicates the next state At that time, take action The maximum achievable Q value. This is the system's estimate of the optimal outcome of future actions; S24. In situations lacking sufficient labeled data, self-supervised learning can construct effective pre-training tasks, helping the model better understand the structure and properties of drug molecules. This, in turn, allows for efficient prediction of adverse drug reactions after fine-tuning. By designing auxiliary tasks (such as masking part of the drug structure information), the model can learn the characteristics of drug molecules from unlabeled data. This indicates drug molecules after partially concealing information. This means that certain features of the drug molecule are randomly masked as targets that the model needs to predict. This represents the true molecular characteristics of the drug, serving as a benchmark for comparing the model's prediction results. It is the cross-entropy loss function, used to measure the difference between the model's prediction and the true value; S25. By leveraging federated learning and differential privacy to protect sensitive data, collaborative training among multiple organizations can be achieved by allowing institutions to share model parameters rather than the data itself. This effectively addresses the balance between data privacy and model performance. Differential privacy further enhances privacy protection, ensuring data security for all participants even during parameter sharing. The combination of federated learning and differential privacy provides a secure and effective solution for cross-institutional collaboration on sensitive data.
4. The drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology according to claim 3, characterized in that, The use of VAEs and GANs to generate drug molecules includes the following specific aspects: Variational autoencoders (VAEs) learn a latent spatial representation of molecular features from known drug molecule data and generate new drug molecules by sampling within this space. It is responsible for transforming the input drug molecule x into distribution parameters (mean μ and standard deviation σ) in the latent space to capture the intrinsic characteristics of the drug molecule, where drug molecule x is mapped to the latent space to generate its latent variable z. Let x represent the mean of the numerator. This represents its standard deviation; the latent space is a multidimensional normal distribution used to generate new drug molecules. Decoder The latent variables z generated by the encoder are sampled to reconstruct and generate a new drug molecule x′. The decoder, controlled by parameter θ, transforms the latent variables back into observable molecular forms. The loss function of the VAE is... It includes reconstruction error and Kullback-Leibler (KL) divergence, where reconstruction error represents a measure of the similarity between the generated molecule x and the input molecule x, and KL divergence represents the measure of ensuring that the latent spatial distribution of the encoder output approximates a standard normal distribution. The first term... This is used to measure the similarity between the generated molecule x′ and the input x, ensuring that the generated molecule has the expected efficacy. (Second item) Ensure that the latent spatial distribution of the encoder is consistent with the standard normal distribution p(z) to guarantee the generation of diverse molecules; Generative Adversarial Networks (GANs) generate drug molecules that meet efficacy criteria through adversarial training of a generator and a discriminator. Potential drug molecules are generated from random noise z, and the generation results are continuously improved to deceive the discriminator. Here, z is random noise sampled from the latent space, and the generator uses z to generate new molecular structures. (Discriminator) The discriminator's task is to distinguish whether the input molecule x comes from a real drug molecule dataset or is a spurious molecule generated by the generator. By improving its ability to identify spurious molecules, the discriminator drives the generator to produce results that more closely resemble real drug molecules. The loss function of a GAN... Used to train the generator and discriminator, enabling the generator to produce realistic drug molecules and the discriminator to accurately distinguish between real and fake molecules. (First item) The score representing the discriminator's judgment of real drug molecules, the second item. This represents the discriminator's judgment score for the fake molecules generated by the generator. By maximizing the first term and minimizing the second term, the generator and discriminator compete with each other during training, and the generator continuously learns to generate more realistic molecules.
5. The drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology according to claim 3, characterized in that, Federated learning and differential privacy include the following specific components: Federal Learning This involves multiple institutions collaboratively training a global model by sharing model parameters without sharing local data. Each institution trains its model locally using its own data and sends the trained model parameters to a central server for aggregation. The central server updates the global model by aggregating the model parameters from all participants and distributes it to each institution. This process can be iterated repeatedly until the global model converges. This represents the global model parameters after the t-th training round. These parameters are calculated by weighted averaging of the local model parameters from each institution. This represents the model parameters trained locally by the k-th institution. This represents the amount of local data at the k-th institution, and N is the total amount of data across all participating institutions. Differential privacy This ensures that even if an attacker obtains the model parameters, they cannot infer the specific content of the original data. Here, M(D) represents the model processing procedure applying differential privacy protection, and f(D) represents the data processing function. This represents normally distributed noise with a mean of 0 and a variance of . Noise is added to the model parameters to protect privacy data.
6. The drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology according to claim 1, characterized in that, Step 3 includes the following steps: S31, Accuracy The model measures the proportion of correct predictions, where TP represents true positives (the number of samples correctly predicted as positive), TN represents true negatives (the number of samples correctly predicted as negative), FP represents false positives (the number of samples incorrectly predicted as positive), and FN represents false negatives (the number of samples incorrectly predicted as negative). S32, Accuracy Recall is the proportion of true positives among samples predicted as positive. The F1 score measures the proportion of all positive samples that are correctly predicted as positive. The above measures the performance of the balanced model, specifically its performance on imbalanced datasets, particularly for predicting adverse drug reactions or evaluating treatment regimens. S33. The ROC curve is used to evaluate the performance of a binary classification model under different decision thresholds. AUC is the area under the ROC curve. The closer the value is to 1, the better the model performance. S34. Optimize through online learning or batch updates, applying regularization techniques. Where λ is the regularization coefficient. These are the model parameters. Regularization reduces model complexity and prevents overfitting by constraining the size of these parameters.
7. The drug development and personalized treatment platform based on artificial neural networks and multimodal artificial intelligence technology according to claim 1, characterized in that, Step 4 includes the following: This system integrates multiple sub-modules (such as drug generation, target prediction, adverse reaction prediction, and personalized treatment optimization) into a single system, enabling them to work collaboratively and achieve a complete drug development process. It creates independent API interfaces for each model, allowing modules to communicate and exchange data through standardized interfaces. After platform deployment, it continuously monitors model performance and system operation, building an automated model update pipeline. The platform should have good scalability, supporting the addition of new modules or updates to existing ones. A real-time monitoring system is deployed to monitor the operational status, resource consumption, and model performance of each module. It provides users (such as doctors and researchers) with a simple and easy-to-use interface, supporting operations such as querying and modifying treatment plans and generating reports, thereby improving the efficiency of drug development and optimizing personalized treatment pathways for patients.