67 results about "Extraction drug" patented technology

The invention discloses an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention discloses a molecular structure characterization-based drug-drug interaction prediction method, which comprises the following steps of: acquiring drug molecule data and drug-drug interaction data, performing standardized preprocessing on the data, and constructing a drug molecule map according to the drug molecule data; inputting the drug molecule map into a model, extracting drug molecule multi-scale features through a multi-scale map convolutional network, and fusing the drug molecule multi-scale features through a dynamic attention mechanism to obtain drug molecule features; based on the drug molecular characteristics, through a full-connection neural network, learning the relationship between the drug molecular characteristics and the drug-drug interaction, and obtaining the prediction probability of the drug-drug interaction; and training the model by adopting a joint loss function according to the drug-drug interaction data and the prediction probability, and applying the trained model to drug-drug interaction prediction. And through the multi-scale image convolutional network, dynamic attention fusion and joint optimization, the prediction accuracy is significantly improved.

The invention discloses an intelligent decision-making method for a cerebral ischemia drug administration time window, and relates to the technical field of intelligent decision-making. The method comprises the following steps: S1, acquiring parameters, and setting a cerebral ischemia drug configuration scheme; s2, extracting a medicine characteristic parameter set and a medicine taking influence parameter set; s3, calculating a drug combination risk parameter set; s4, performing hierarchical classification processing on the drug combination risk parameter set, and performing drug correlation analysis; s5, performing weighted fusion on a correlation analysis result in combination with a drug influence attenuation rule, generating a drug administration time window evaluation parameter, and adding a label; and S6, according to the parameters, the labels and the configuration scheme, generating a cerebral ischemia drug administration time window intelligent decision scheme, and performing storage and backup. According to the method, by combining directional extraction of the drug characteristic parameter set and the drug taking influence parameter set with Pearson correlation algorithm screening, decision errors caused by data deviation are avoided.

The invention discloses a drug target binding affinity prediction method based on collaborative attention, and belongs to the technical field of natural language processing, and the method comprises the steps: building a CLAT-DTA prediction model comprising an input data representation module, a feature extraction module, an information fusion module and a prediction module; converting drug molecules into fingerprint representation, and pre-training protein sequence data by using ESM; extracting drug data by using Encoder, and extracting protein data by using Bi-LSTM (Bidirectional Long Short-Term Memory); fusing the drug target data using a collaborative attention mechanism; three-layer full ligation is used to predict drug target binding affinity. According to the method, important information can be better polymerized, and the binding affinity between the drug and the protein can be predicted.

The invention relates to the technical field of bioinformatics and artificial intelligence, and provides a drug-target interaction prediction method and system based on multi-modal feature fusion, and the method comprises the steps: carrying out the word segmentation coding of an obtained to-be-recognized drug sequence and a target sequence, and respectively extracting the subsequence features of a drug and a target; constructing a two-dimensional molecular diagram of the drug and a three-dimensional structure diagram of the target, and respectively extracting diagram structure characteristics of the drug and the target; fusing the subsequence features and graph structure features of the drug and the target through a cross attention mechanism; and carrying out interactive fusion by adopting a bidirectional collaborative attention mechanism to obtain a prediction result of the binding affinity of the drug and the target. According to the invention, by combining the multi-modal complementary information of the drug and the target, deep interaction between different modal features is deeply mined; meanwhile, a two-way collaborative attention mechanism is introduced into interaction modeling of the drug and the target, and the accuracy of drug-target binding affinity prediction is effectively improved.

The invention discloses a drug-target interaction prediction method and system, and belongs to the technical field of biological information. The method comprises the following steps: firstly, acquiring molecular structure data of a drug and sequence and structure data of a target spot; then, respectively extracting molecular map structure characteristics and SMILES sequence characteristics of the medicine, and amino acid sequence characteristics and three-dimensional space structure characteristics of a target spot; further, taking drugs and targets as nodes, taking the fused multi-modal features as node features, and combining known interaction and similarity information to construct an initial heterogeneous graph; inputting the heterogeneous graph into a dynamic graph neural network, dynamically learning an inter-node connection weight by using a graph attention mechanism, and iteratively updating node representation through multi-layer message transmission to obtain depth feature representation of drugs and targets; and finally, splicing the depth features, inputting the depth features into a multi-layer perceptron classifier, and predicting the drug-target interaction probability. According to the method, through multi-modal feature fusion and dynamic graph structure learning, the prediction accuracy and robustness are remarkably improved.

The invention discloses a drug incompatibility detection method based on fusion of a map and a large language model. Comprising the following steps: standardizing drug names in a prescription, mapping the drug names into uniform national standard codes, and generating a drug pair set; and extracting a drug interaction triple in combination with a large language model in the medical field, and constructing a heterogeneous drug knowledge graph. Based on a graph neural network and a comparative learning mechanism, embedded representation learning is performed on the knowledge graph, and the discrimination capability of drug node features is improved. Path retrieval and risk scoring are carried out on the drug pairs with the explicit interaction paths; and for a drug pair without an explicit path, screening alternative drugs by using chemical component similarity, inputting a pre-trained large language model through structured prompt, predicting a potential interaction relationship and credibility, and supplementing implicit risk information. According to the method, the problems of insufficient coverage and limited reasoning ability of the traditional knowledge graph are effectively solved, and the comprehensiveness and reliability of detection are improved.

The invention provides a drug target prediction method based on cross-modal attention and uncertainty evaluation, and belongs to the technical field of drug target prediction. In order to solve the technical problems that the existing drug target prediction lacks quantitative evaluation on the reliability of a prediction result and a nonlinear interaction relationship between a drug and a target is difficult to establish, the method comprises the following steps: collecting data, and fusing graph structure features and sequence features of extracted drug molecules to obtain a final code of the drug molecules; extracting amino acid sequence characteristics of the target protein, and constructing protein sequence characteristic expression; inputting the drug molecular features and the protein sequence features into a cross-modal attention module, and aligning and fusing the drug features and the protein features by using a bidirectional cross attention mechanism to obtain drug-target combined feature representation; introducing an uncertainty quantification mechanism, and outputting uncertainty estimation of a drug-target interaction prediction label and a prediction result; the method is used for predicting drug target interaction.

The invention discloses a cross-modal interactive fusion structure diagram feature extraction method, and the method employs a multilayer diagram isomorphic network to extract local neighborhood features and global topological features of drug molecules, thereby improving the capability of a model for capturing the change of a fine structure. A convolutional neural network and a multi-head convolutional self-attention mechanism are integrated to comprehensively capture local features and long-range dependency relationships in a protein sequence; and interaction information between a drug and a target is fully modeled through a cross-modal interaction fusion module based on a gating mechanism, so that more accurate DTI prediction is realized.

The application relates to a drug-lncRNA relationship prediction method and system based on embedding constraints, and the method comprises the following steps: collecting lncRNA-drug association data and preprocessing, and constructing a data set; based on the data set, an lncRNA-drug association matrix is constructed, and an lncRNA-drug bipartite graph is extracted; based on the lncRNA-drug association matrix, an lncRNA similarity matrix and a drug similarity matrix are obtained, and dimension reduction processing is performed; the lncRNA-drug bipartite graph and the dimension reduction processed lncRNA similarity matrix and drug similarity matrix are input into an LDA-GNN model and an LDA-DHG model with an embedding constraint strategy, and the probability score of lncRNA and drug association is obtained; and based on the probability score, the relationship prediction of the drug-lncRNA is realized. Through the embedding constraint strategy, the prediction performance and the distinguishing ability of the model are significantly improved.

The present application belongs to the technical field of biological information, and particularly relates to a drug-target interaction prediction method based on multi-granularity representation; the method comprises the following steps: extracting an enhanced molecular representation of a drug molecule by using a hierarchical network; modeling first-order information and second-order information in an amino acid sequence respectively based on adjacent residues in the amino acid sequence, so as to extract multi-order sequence features; processing multiple sequence information by using a Pconsc4 tool to obtain spatial structure information representation of a protein; splicing the enhanced molecular representation, the multi-order sequence information and the spatial structure information representation of the protein to obtain fusion features; inputting the fusion information into an interaction prediction network to obtain a drug-target interaction prediction result; the present application can not only solve the weakness of existing methods that only focus on single-granularity information, improve prediction accuracy, but also show interpretability.

The application discloses a DDI prediction method based on a Siamese structure and graph contrast learning, comprising the following steps: collecting drug-drug interaction text data and physical and chemical property data files and targeting relationship data files of drugs; extracting the physical and chemical property characteristics and the targeting relationship characteristics of the drugs, fusing the characteristics to obtain initial characteristics based on the physical and chemical properties and the targeting relationship; calculating a drug-drug interaction adjacency matrix, combining the initial characteristics to construct a drug-drug interaction heterogeneous graph; inputting the heterogeneous graph into a Siamese structure-based graph contrast learning model to learn and obtain embedding characteristics of drug nodes; and using a link prediction method to calculate the score of edges between any two drug nodes. The application can alleviate the problem that the drug targeting relationship characteristics and the drug-drug interaction text data are difficult to be used alone when the data are sparse and have an impact on the performance of the model, improves the accuracy of drug-drug interaction prediction, and can be applied to identifying potential interactions between drugs.

The invention relates to a drug-lncRNA relationship prediction method and system based on embedding constraints, and the method comprises the steps: collecting and preprocessing lncRNA-drug associated data, and constructing a data set; based on the data set, constructing an lncRNA-drug association matrix, and extracting an lncRNA-drug bipartite graph; based on the lncRNA-drug association matrix, obtaining an lncRNA similarity matrix and a drug similarity matrix, and carrying out dimension reduction processing; inputting the lncRNA-drug bipartite graph and the lncRNA similarity matrix and the drug similarity matrix which are subjected to dimension reduction processing into an LDA-GNN model and an LDA-DHG model which are provided with an embedded constraint strategy, and obtaining a probability score associated with the lncRNA and the drug; and based on the probability score, realizing drug-lncRNA relationship prediction. According to the method, through an embedded constraint strategy, the prediction performance and the distinguishing capability of the model are remarkably improved.

The application discloses a drug synergistic effect prediction method based on a biomedical knowledge graph and a Transformer, and relates to a drug synergistic effect prediction method. In order to solve the problem that a traditional drug combination discovery process mainly depends on clinical trials, which is not only time-consuming and laborious, but also high in cost and risky to patients, the application comprises the following steps: extracting drug data samples, generating a data set, dividing training and test sets, performing network training and testing; constructing a biomedical knowledge graph; converting a sequence list of a drug structure into a graph by using Rdkit; mining a subgraph of the knowledge graph by using a multi-hop subgraph mining network; learning feature representations of the knowledge network and the drug molecular graph by using a relation-aware Transformer, and performing fusion; performing synergistic effect prediction between drug pairs by using a multilayer perception machine; inputting drug pairs in a training set into the above model; inputting drug pairs in a test set into the prediction model to obtain a prediction result. The application belongs to the technical field of drug synergistic effect prediction.

The application discloses a drug interaction prediction method based on DS evidence theory. First, multi-modal data of drugs are acquired to construct a drug interaction dataset; then, multi-modal features of the drugs are extracted as inputs of a deep neural network, a multi-modal drug interaction prediction model is constructed, and the prediction model is trained; then, the trained prediction model is used to predict drug interactions. The prediction model uses Dirichlet distribution to calculate classification probability and uncertainty value, and obtains multi-modal prediction results and overall uncertainty value through Dempster-Shafer evidence theory fusion. Compared with existing prediction methods, the application uses multi-modal drug data, has a lower prediction error, can provide meaningful uncertainty information for guiding multi-modal prediction fusion and expressing prediction confidence, and has higher reliability and robustness.

The invention belongs to the technical field of intelligent medical treatment, and discloses a drug relocation method and system based on implicit interactive adjacency matrix remodeling, and the method comprises the following steps: S1, constructing a fusion drug-disease incidence matrix; s2, extracting drug domain features and disease domain features by adopting a parameter sharing double-path self-attention mechanism; s3, mapping the drug / disease features to the dimension space of the opposite side through the trainable projection matrix, and generating an implicit interaction prediction matrix; and S4, dynamically adjusting the contribution degree of the positive and negative samples through the weight lambda to solve the problem of data sparsity. According to the method, drug domain and disease domain feature decoupling learning is realized through a dynamic adjacency matrix by adopting a double-path self-attention mechanism, cross-dimensional implicit interaction prediction is completed by utilizing a trainable projection matrix, feature representation is iteratively optimized in combination with a recurrent neural network, the noise influence of artificial similarity data can be effectively reduced, and the accuracy of the method is improved. And the method has remarkable advantages in actual scenes such as Parkinson's disease drug relocation and the like.

The application is based on a method for predicting anticancer drug sensitivity by deep factor decomposition machine, characterized in that it comprises the following steps: step 1, constructing a data set and preprocessing; step 2, constructing a deep factor decomposition machine DeepFM model: the main idea of the deep factor decomposition machine DeepFM model is to construct a factor decomposition machine FM model, construct a deep neural network model DNN, and fuse the factor decomposition machine FM and the deep neural network to construct a deep factor decomposition machine DeepFM model; step 3, training and evaluating the model results of each stage, and dividing the anticancer drug sensitivity data obtained in step 1 into 5 parts, performing 5-fold cross-validation, and training the model results of each stage in this stage. The application separately models the low-order feature combination of the drug, and fuses the high-order feature combination to construct a model that fuses the deep neural network model and the factor decomposition machine model, better extracts the drug feature structure, and makes the constructed model more accurate and higher in precision.

The invention provides an intelligent drug interaction prediction method and system based on a knowledge graph, and relates to the technical field of medical information, and the method comprises the steps: extracting drug molecular structure and metabolic data, calculating the conformation overlapping degree, determining a metabolic pathway overlapping site, calculating the competitive binding probability, analyzing the drug distribution state, and judging a metabolic cycle overlapping interval. Determining a concentration accumulation range, evaluating a metabolic inhibition level, generating early warning data and updating the knowledge graph. The drug interaction can be accurately predicted, the medication safety is improved, and the adverse reaction risk is reduced.

The application discloses a brain hemorrhage perioperative medication scheme intelligent recommendation method and system, acquires physiological parameters and operation time sequence characteristics of a patient, constructs a patient risk image, carries out risk matching with a drug knowledge base to generate a candidate drug set, extracts drug attribute characteristics to determine a drug function grouping, carries out adaptability scoring based on the patient risk image to generate a grouping adaptability matrix, carries out drug interaction analysis to identify synergistic pairs and antagonistic conflict pairs, carries out combination optimization on the synergistic pairs to generate a synergistic scheme, carries out conflict exclusion screening on the antagonistic conflict pairs to generate safety constraints, constructs a medication scheme topology, carries out dose sensitivity anomaly detection and correction to generate a correction factor to generate a time-sharing drug administration scheme, carries out real-time monitoring to carry out perfusion evaluation to determine a safety level and trigger dose adjustment, forms an optimized medication scheme, and realizes intelligent recommendation of perioperative medication.

The invention discloses application of a safflower liver-clearing thirteen-ingredient pill in preparation of a medicine for repairing liver injury, and belongs to the field of medicines. The safflower liver-clearing thirteen-ingredient pill consists of safflower, cape jasmine, clove, lotus seed, dwarf lilyturf tuber, costus root, medicine terminalia fruit, szechwan chinaberry fruit, red sandalwood, musk, buffalo horn concentrated powder, bezoar and vermilion. The lotus seeds and the costustoot in the safflower liver-clearing thirteen-ingredient pill play a role in repairing liver injury by activating HNF-4alpha, and the ratio of the lotus seeds to the costustoot is 4: (1-3). The research finds that the lotus seed and costus root components in the HHQG can activate the HNF-4alpha, the optimal proportion for activating the HNF-4alpha is preferably selected, and a scientific basis is provided for extracting useful components in medicines and developing natural active small molecular substances.

The application discloses a drug synergistic effect prediction method, system, device and medium based on heterogeneous graph tensor decomposition, and the prediction method comprises the following steps: obtaining a SMILES sequence of a drug, extracting a molecular structure feature of the drug, and obtaining a molecular structure feature representation of the drug; constructing a drug pair heterogeneous graph in each cell line, and obtaining a local interaction feature of the drug through a heterogeneous graph conversion network; performing Tucker decomposition on a three-channel heterogeneous graph relationship tensor, splicing the decomposition result with the local interaction feature of the drug, and extracting a global interaction feature vector of the drug; and predicting a synergistic score of a current drug-drug combination in a cell line according to the molecular structure feature representation of the drug and the global interaction feature vector of the drug. The application integrates the SMILES sequence of the drug and gene expression information of the drug pair in the cell line, and also converts the SMILES sequence into a drug molecular structure graph by using a graph convolution network, so as to extract the molecular structure feature of the drug. Such a design enables TensoGraph to more accurately reflect the complexity and diversity of the drug interaction network in the real world, thereby improving the prediction performance.

The invention discloses a drug interaction prediction method based on similarity network fusion, and relates to the technical field of biomedicine, and the method comprises the steps: integrating drug data sets in a plurality of well-known drug databases, and extracting drug feature data; calculating similarity according to features extracted from the drug data set, and constructing a similarity matrix; the entropy value of each similarity matrix and the pairwise distance between the similarity matrixes are calculated, and the similarity matrix with the maximum information amount is preferentially selected; combining the selected similarity matrixes by adopting a similarity network fusion technology to obtain a fused similarity matrix; constructing an interaction matrix according to the obtained fusion similarity matrix; and training a convolutional neural network (CNN) model by using the fusion similarity matrix and the interaction matrix of the drug, and realizing drug interaction prediction on test data by using the trained CNN model. By combining the similar network fusion technology and the convolutional neural network model, the accuracy and reliability of drug interaction prediction are remarkably improved.

A drug combination risk prediction method, apparatus, device, and medium based on multi-source feature fusion and contrastive learning relate to the technical field of drug combination prediction. The drug combination risk prediction method comprises: obtaining a drug dataset. Based on the drug dataset, obtaining a feature graph. Based on the feature graph, the molecular graph features of the drug are obtained by adaptively extracting features of the molecular graph structure. The molecular graph features are defined as structural features. The molecular graph features are input into a multi-layer cascaded graph convolutional network to extract high-order topological features in the drug network and obtain relational features. Based on the drug dataset, similarity features are calculated and interactive embedding features are extracted. Based on the structural features and similarity features, the potential associations and differences between the features are mined through a contrastive learning mechanism to obtain structural contrastive learning features and similarity contrastive learning features. Feature fusion and feature alignment are performed on the above features to obtain fused features. Based on the fused features, the drug combination risk is predicted.

The invention discloses a drug IC50 prediction method and system based on molecular structure and gene expression, and the method comprises the steps: carrying out the comprehensive characterization of the molecular structure of a drug, extracting the chemical structure and characteristic information of the drug through the modes of molecular fingerprints, molecular maps and the like, carrying out the fusion with a gene expression matrix of cells, building a unified characteristic expression system, and carrying out the prediction of the drug IC50. And inferring a gene regulatory network reflecting a potential regulatory relationship between genes based on a variational auto-encoder (VAE). On the basis, a multi-layer feature extraction mechanism combining global and local information is constructed, the global information learns an overall regulation structure among genes in the whole regulation network through a graph neural network, and the local information captures a local action relationship between drugs and key regulation factors by dividing and analyzing sub-graphs. According to the method, the influence mechanism of the drug on the cell system can be described more accurately, the prediction precision of the IC50 value and the interpretability of the model are remarkably improved, and the method has good adaptability and wide application prospects.

The invention discloses a medicine relocation system and method based on a quantum graph Fourier convolution network. The method comprises the following steps: integrating proteins, genes, microorganisms, metabolites, drugs, diseases and multi-source associated information of the proteins, the genes, the microorganisms, the metabolites, the drugs, the diseases to construct a heterogeneous biological information network and a local subgraph network; the method comprises the following steps: innovatively designing a quantum graph Fourier convolution network, extracting spectrum characteristics and topological information of protein, gene, microorganism, metabolite, medicine and disease nodes from a heterogeneous biological information network, updating medicine and disease node information, and obtaining discriminative characteristic representation; a multi-layer sensor is used for learning and depicting medicine-disease joint feature representation, and then the unknown curative effect of the medicine is predicted. According to the method, data mining and knowledge discovery are carried out based on the multi-source heterogeneous biological information network, the quantum Fourier graph convolutional network is innovatively used for modeling the multi-source heterogeneous biological network, high-order semantic dependence and a global structure mode between drugs and diseases can be effectively extracted, the performance of a drug relocation prediction system is improved, and the system is suitable for large-scale popularization and application. And the method has good expandability and practical application prospects.

The invention relates to a drug-target interaction prediction method, which comprises the following steps: preprocessing drug molecule data to construct a drug molecule graph, and preprocessing a target sequence to construct a target residue topological graph; inputting the two features into a prediction model, respectively extracting global features of the drug and the target by the model through two branches of a GAT network, acquiring residue-level features of the target through a GCN network, and matching functional group categories through a DiffPool layer clustering category number to obtain drug functional group features; the functional group features and the residue-level features interact through the cross attention module to generate interaction features, then the interaction features and the global features are spliced into joint features, and the joint features are input into a full connection layer to output a prediction result. According to the method, chemical prior knowledge and deep learning are deeply fused, the bottleneck of a traditional model is broken through, and a high-reliability technical solution is provided for computer-aided drug design.

The application discloses an antibody conjugated drug binding prediction method based on a virtual graph and multi-scale features, proposes a drug carrier, a linker, an antibody heavy chain and a light chain, and an antigen target protein initial feature construction method, proposes a 1DCNN for extracting drug sequence features, designs a drug feature extraction method based on a graph virtual node, introduces a virtual node and a virtual edge into a molecular structure graph, takes a Graph Transformer as a graph feature extractor, takes a virtual node feature as a drug representation, then inputs protein and drug features into a feature fusion module, adds an attention mechanism and a gated skip connection mechanism in feature fusion, captures potential interactions while fusing feature information of different hidden layers, and realizes higher precision affinity prediction. The application can solve the technical problems that existing methods are difficult to extract structural features of antibody conjugated drugs and features of different components are difficult to fuse.

The application discloses a drug-target interaction prediction method and device based on multi-view feature fusion and a medium, and comprises the following steps: (1) extracting drug features: first, generating a drug molecule graph, extracting molecular fragments to construct multiple subgraph structures, and then using a graph encoder to encode to obtain drug features; (2) extracting target features: first, extracting the protein pocket of the target, dividing the protein pocket into multiple subpockets to obtain multiple subgraph structures, and then using a graph encoder to encode to obtain the pocket features of the target; in addition, a self-attention module is used to extract the sequence features of the target, and a cross-attention is used to fuse the pocket features and the sequence features to obtain the target features; (3) using a bilinear attention to fuse the drug features and the target features, and calculating the interaction probability between the drug and the target. The application can effectively mine the biochemical attribute features in the drug molecules and the target protein structures, and improve the prediction accuracy of the drug-target interaction.

The invention discloses a drug relocation prediction method fusing multi-modal information and a heterogeneous structure. The method mainly comprises the following steps: constructing a homograph of drugs, diseases and proteins; node features of drugs, diseases and proteins are extracted through a graph convolutional network (GCN); constructing a heterogeneous hypergraph, and representing multiple relationships among nodes through hyperedges; a self-defined hypergraph convolutional network (HGCN) is applied to the heterogeneous hypergraph; and splicing and fusing the features obtained by the GCN and the HGCN to obtain the final node representation of the drug and the disease. Compared with the prior art, the method has the advantages that (1) multi-mode heterogeneous information of drugs, diseases and proteins is fully utilized; and (2) combining information of a local structure (GCN) and a high-order structure (HGCN). And (3) constructing a unified hypergraph framework to improve the accuracy and interpretability of drug relocation prediction.

The application provides a drug-disease interaction prediction method fusing multi-neighborhood correlation information, first, drug and disease information in a drugomic database is preprocessed to construct a drug-disease correlation relationship network; second, node aggregation and linear fusion are introduced, and feature information of drugs and diseases in multiple neighborhood spaces is extracted in parallel; then, contrast learning is used to perform parallel fusion on single-domain features and multi-neighborhood features, and a hierarchical attention mechanism is used to aggregate feature representations to obtain general features of drugs and diseases; finally, the extracted feature information of drugs and diseases is sent into a classification model composed of a full connection neural network for training, a hybrid optimization strategy combining prediction loss and negative example generation loss is proposed to perform overall optimization on the model, the model is saved, and the relationship between drugs and diseases is predicted. The application has better performance when predicting the drug-disease correlation relationship, and reduces the dependence on prior knowledge.