123 results about "Extraction drug" patented technology

The invention discloses a drug combination risk prediction method and device based on multi-source feature fusion and comparative learning, equipment and a medium, and relates to the technical field of drug combination prediction. The drug combination risk prediction method comprises the following steps: acquiring a drug data set; and obtaining a feature map according to the drug data set. And according to the feature map, carrying out adaptive feature extraction on the molecular map structure to obtain molecular map features of the medicine. And defining molecular map features as structural features. And inputting the molecular graph features into a multi-layer cascaded graph convolutional network, extracting high-order topological features in a drug network, and obtaining relation features. According to the drug data set, similarity features are calculated, and interactive embedding features are extracted; according to the structural features and the similarity features, potential correlation and differences between the features are mined through a comparative learning mechanism, and structural comparative learning features and similarity comparative learning features are obtained; and performing feature fusion and feature alignment on the features to obtain fused features. And predicting the drug combination risk according to the fusion features.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

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 relates to the technical field of drug interaction prediction, and discloses a drug interaction prediction method based on multilayer attention and elastic message passing, which comprises the following steps: firstly, extracting graph structure information of drug molecules from a 3D structure of the drug molecules, including associated features of atoms and chemical bonds; on the atomic feature level, learning updating and denoising of different atom and chemical bond features in drug molecules are achieved by using an elastic graph neural network and fusing the attention mechanism of Transform and combining a 3D position coding method based on the molecular mass center; on the molecular feature level, the attention mechanism is further utilized, and through supervised learning, comparative learning and structured learning, the potential interaction relationship of different drug molecules is mined, and potential network attacks are detected and defended; and interaction prediction of any two drug molecules i and j is realized. The results of four indexes of ACC, AUC, AP and F1 all reach 98% or above, and the method exceeds the most advanced method at present.

According to the method for predicting the affinity of the drug and the target based on graph neural network topology enhancement, the physicochemical properties of protein, especially the structure and the dynamics of a pocket region, can be better described, and key modes and characteristics in DTA prediction can be better captured, so that the prediction accuracy is improved. Comprising the following steps: (1) expressing a drug and a target as a structure of a graph, constructing a protein graph by using an amino acid sequence of protein and a contact graph, and linearly inputting a standard character string into a molecular graph of the drug based on simplified molecules; (2) extracting representations of drugs and targets, integrating protein pocket information into a topological structure, creating virtual nodes, incorporating the virtual nodes into a residue graph, and extracting representations by using different graph neural network modules after obtaining an enhanced residue graph and a molecular graph; and (3) connecting the representations and inputting the representations into a multi-layer perceptron to predict affinity.

The invention provides a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

The invention relates to the technical field of bioinformatics, and discloses an adverse drug reaction prediction method based on comment text information enhancement, and the method comprises the following steps: constructing an adverse drug reaction dictionary; extracting adverse drug reaction information based on the comment text; integration of adverse drug reaction information; integrating the drug characteristic information of the public database; calculating the similarity; constructing a heterogeneous graph; building a graph convolutional network model; model training and evaluation; predicting adverse reaction cases of drugs; and analyzing the occurrence mechanism of adverse drug reaction. According to the adverse drug reaction prediction method based on comment text information enhancement, diversified data such as drug comments, clinical test data, actual medication experience and feedback are comprehensively utilized, patient features are added to enrich the relationship between drugs and adverse reactions, more comprehensive drug safety assessment is provided, and the prediction accuracy is improved. In addition, compared with other models, the GCN model has the advantages that the training and prediction are quicker, and the calculation difficulty can be reduced.

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 provides a drug target interaction method based on geometric characterization. The method comprises the following steps: acquiring drug structure features from a drug structure chart by using a geometric enhancement graph neural network GeoGNN; obtaining target point structure features from the target point structure diagram by using a geometric encoder; combining the drug comprehensive similarity, the target comprehensive similarity and the drug target incidence matrix to form a heterogeneous network, and performing feature extraction on the heterogeneous network by using a graph neural network GCN to obtain drug network features and target network features; performing multi-modal feature fusion on the drug structural features and the drug network features, and the target structural features and the target network features; extracting interaction information between the medicine and the target spot from the fused medicine multi-modal features and the fused target spot multi-modal features; based on the interaction information, better features of the drug and the target spot are obtained, and the probability of interaction between the drug and the target spot is predicted through a classifier. According to the method, the drug target interaction prediction accuracy is improved.

The invention discloses a drug target combinability prediction method based on variational coding, and belongs to the field of bioinformatics. According to the method, efficient extraction of drug and target features, construction of potential feature space of the drug and target features and prediction of the interaction relationship of the drug and target features are realized through a dual-stage coupled end-to-end neural network. The model is mainly composed of two stages, in the first stage, potential representations of drug and target features are obtained through variational compression and feature extraction, and in the second stage, a drug target action matrix is reconstructed through an up-down sampling comprehensive deep neural network. Through the architecture, the chemical characteristics of the drug and the biological characteristics of the target can be effectively integrated, and the accuracy of interactive relationship prediction is improved. The invention provides a novel and efficient solution for deep analysis of biological big data at the same time.

The invention discloses a drug interaction prediction method based on a double-view graph neural network, and the method comprises the steps: extracting features in a single drug view in a self-adaptive manner through a directional message passing neural network with a substructure attention mechanism, and extracting interaction features between the drug and the view through a graph attention network; and fusing to generate drug substructure features, so as to more accurately predict the interaction between drugs. And through the common attention mechanism module, drug interaction is converted into pairwise interaction between drug substructures, so that the capability of capturing drug molecular characteristics is greatly enhanced, and the prediction precision is improved. The invention can make full use of the interaction information between drugs and the interaction information of chemical substructures, studies the classification and identification problem of drug interaction prediction, and provides an efficient drug interaction prediction method.

The invention discloses a method for analyzing and predicting drug-target interaction by using deep learning, which comprises the following steps: encoding a drug and a target protein, and extracting the characteristics of the drug and the target protein for interaction; inputting the interaction characteristics of the drug and the target protein into a multi-head cross attention mechanism, and extracting the interaction characteristics of the drug and the target protein; obtaining independent characteristics of the drug and the target protein; fusing and re-classifying the independent characteristics of the drug, the independent characteristics of the target protein and the interaction characteristics of the drug and the target protein, and predicting drug-target interaction; the invention provides a network based on feature extraction between global molecules and local feature molecules of a drug-target, and the DTIs of the drug-target is predicted; according to the method, the interaction features of the drug target are extracted by adopting a cross attention mechanism, and the independent features of the drug target are extracted by using GAT and CNN, so that rich drug-target information is obtained, and the prediction performance is improved.

The invention discloses an antibody-coupled drug combination prediction method based on a virtual graph and multi-scale characteristics, provides a method for constructing initial characteristics of a drug carrier, a linker, an antibody heavy chain and light chain and an antigen target protein, provides a method for extracting drug sequence characteristics by using 1DCNN, and designs a drug characteristic extraction method based on graph virtual nodes. Virtual nodes and virtual edges are introduced into a molecular structure diagram, a Graph Transform is used as a diagram feature extractor, virtual node features are used as drug representation, then protein and drug features are input into a feature fusion module, an attention mechanism and a gating jump connection mechanism are added in feature fusion, and the protein and drug features are fused. And the feature information of different hidden layers is fused while the potential interaction is captured, so that higher-precision affinity prediction is realized. The method can solve the technical problem that the structural features of the antibody-coupled drug are difficult to extract in the prior art and the technical problem that the features of different components are difficult to fuse.

The invention relates to a drug relocation method, in particular to a drug relocation method using neighborhood information and a weighted fusion network. The invention aims to solve the problems that the existing drug relocation method is a shallow prediction model, the characteristics of drugs and disease nodes cannot be deeply known, and the relocation result of the existing drug relocation method is biased to common infectious diseases. The method comprises the following steps: extracting a data sample of the action relationship of drugs on diseases; in the drug-to-disease effect relation graph, learning interaction characteristics between drugs and disease nearest neighbors by using a weighted graph convolutional network; aggregating the learned characteristics of the drugs and the diseases; hidden high-order nonlinear features are obtained through a multi-layer fusion network; inputting the action relationship of the drugs in the training set on the diseases into the model, training the model, and obtaining a final prediction model after multiple iterations; and inputting the drug pairs in the test set into the prediction model to obtain a prediction result. The invention belongs to the technical field of drug relocation.

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.

A real-time drug interaction alert system using AI-powered analytics, consisting of: a wearable or implantable biosensor device configured to continuously measure physiological parameters of a subject, including at least heart rate, blood pressure, blood oxygen saturation, skin temperature, and biomarkers related to liver and kidney function; a communication module integrated into the device that can securely transmit physiological data to a cloud-based analysis engine via wireless protocols such as Bluetooth Low Energy (BLE), Wi-Fi, or LTE-M; a cloud-hosted artificial intelligence (AI) inference engine configured to perform multimodal drug-drug interaction risk analysis, the engine comprising at least a convolutional neural network (CNN) for time-series analysis of biosignal patterns, a natural language processing (NLP) module for extracting drug information from structured and unstructured medical sources, and a Bayesian reasoning module for modeling probabilistic risk interactions between drugs and patient-specific variables; a pharmaceutical input interface for capturing medication data by at least one of the following methods: manual entry, optical character recognition (OCR) of the packaging, or near field communication (NFC) scanning of embedded tags; an alert dissemination module configured to trigger multi-channel alerts, including haptic signals via the wearable device, audiovisual notifications via a paired smart device, and the secure transmission of interaction reports to designated healthcare professionals based on severity thresholds.

Systems and methods are provided for generating and training a relation extraction model configured to extract document-level relations. Systems obtain a knowledge database that comprises a plurality of entity tuples and a plurality of relation types, use the knowledge database to generate annotated relation instances based on relation instances that are identified in a set of unlabeled text, generate a training dataset comprising the annotated relation instances and the set of unlabeled text, and generate the machine learning model via modular self-supervision. Systems and methods are also provided for using a relation extraction model to extract document-level relations in specific use scenarios, such as for extracting drug response relations from full-text medical research articles.

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 discloses a drug interaction prediction method and system based on a graph neural network and multiple attention mechanisms. The method comprises the following steps: converting an SMILES sequence of a drug pair into graph data, and obtaining an embedding matrix of the drug pair by utilizing a multi-layer GIN operation; extracting a self-attention feature matrix of the drug pair from the embedding matrix of the drug through a self-attention mechanism encoder, calculating a cosine similarity matrix of the drug pair, and obtaining a self-attention feature vector of the drug pair after leveling operation; processing the embedded matrix of the drug pair through an interactive attention mechanism encoder to obtain a feature matrix with interactive attention coefficients, performing average pooling operation on the interactive attention coefficient feature matrixes of the two drugs, and then performing standardization to obtain an interactive feature vector of the drug pair; and carrying out splicing operation on the self-attention feature vectors and the interaction feature vectors of the drug pairs, and inputting a multi-layer perceptron to predict the interaction between the drug pairs. According to the invention, the interaction between drugs can be better predicted.

The invention relates to a drug-target interaction prediction method and system based on deep learning. The method comprises the following steps: determining an SMILES sequence of a drug molecule to be predicted and an amino acid sequence of protein; generating a drug molecular map, inputting the drug molecular map to a GPS layer, and extracting the feature representation of the drug by using GINE and multiple attention mechanisms; extracting the feature representation of the protein through an ESM-2 model; and performing cross attention mechanism feature fusion on the feature representation of the drug and the feature representation of the protein to obtain a fused feature, and predicting the binding probability of the drug and the protein based on the fused feature. The feature representation of the medicine is extracted by combining a GINE layer and a multi-head attention mechanism, and the feature representation of the protein is extracted by using the pre-trained ESM-2 model, so that the comprehensive extraction of the features can be realized, and the accuracy of the feature representation is improved; drug and protein features are effectively fused through a cross attention mechanism, and efficient and accurate prediction of drug-target interaction is realized.

The invention discloses a drug-drug interaction prediction method fusing multi-modal drug information and a static subgraph. The method mainly comprises the following steps: extracting biochemical similarity characteristics of a drug chemical substructure, a target spot and an enzyme; obtaining pharmacological features of the medicine based on a natural language processing model; using graph diffusion convolution to obtain learned node representation from each static sparse sub-graph; embedding of multi-modal feature fusion different drug features; a multi-layer perceptron is used to predict the category of action. According to the drug interaction event prediction method based on the diffusion static subgraph of the multi-modal graph, DDI multi-category prediction is achieved, and compared with an existing algorithm, the method has the following advantages that richer features are provided in combination with multi-modal features such as pharmacology and the like; (2) dividing into a plurality of static sparse sub-graphs by using random edges, learning node representation from each sub-graph, and providing global structure features and local structure features of the DDI network; and (3) richer potential representation is obtained by using graph diffusion convolution.

The invention provides a collaborative drug prediction method and system based on a heterogeneous hypergraph, and relates to the field of collaborative drug prediction.The collaborative drug prediction method comprises the steps that information of various cells and structures of various drugs are obtained, and corresponding cell fusion features and drug structure features are obtained; constructing a heterogeneous hypergraph based on the drug structure features and the cell fusion features, performing convolution processing to obtain drug node features, cell node features and hyperedge features, and training a neural network by using the extracted features to obtain a prediction model; and obtaining a to-be-detected drug, and inputting the to-be-detected drug into the prediction model to obtain a prediction result. According to the method, the heterogeneous hypergraph is constructed based on the collaborative information of the known drug combination in the cells, and the feature information of the drug nodes, the cell nodes and the hyperedges is extracted by adopting the difference of the multilayer hypergraph convolutional layers, so that the defects of excessive smoothness and redundancy of the information during feature extraction of the heterogeneous network by a common graph neural network are overcome; and a complex relation mode is difficult to capture.

According to the big data-based adverse drug reaction monitoring method and system provided by the invention, the drug action feature data and the patient reaction feature data are accurately extracted from clinical feedback big data, and the feature data are creatively fused to generate more comprehensive drug clinical feedback features, so that the clinical feedback big data is more accurate. And a solid foundation is laid for subsequent adverse drug reaction analysis. Furthermore, the characteristic combination meeting the application conditions of the medicine combination can be intelligently searched and identified in huge clinical feedback characteristics, and the precise prediction and evaluation of the adverse reaction under the combined use of the medicines are realized. Finally, based on the feature groups, the drug adverse reaction labels corresponding to the target drugs can be automatically generated, data support is provided for drug research and development, clinical application and supervision, the occurrence risk of adverse reactions in the drug use process is effectively reduced, and the drug use safety of patients is guaranteed.

The invention relates to the technical field of drugs, in particular to a drug relocation method for integrating multi-source biological data based on a meta-path, which comprises the following steps: acquiring an original data set; constructing a fusion heterogeneous network, and fusing the incidence matrixes of every two combinations of drugs, diseases and proteins and the respective similarity matrixes to obtain a heterogeneous graph; designing a multi-level association path based on meta-path and similarity negative sampling, fusing similarity information of same-level networks in the heterogeneous network based on a similarity principle, and selecting a negative link sample; extracting drug characteristics; constructing an SMS-MPF module, carrying out migration on the meta-paths through a heterogeneous network, extracting and fusing features among different meta-paths, and obtaining associated features of drugs and diseases; and inputting the disease phenotype similarity matrix, the drug characteristics and the associated characteristics of the drugs and the diseases into an associated prediction module to obtain final node representation. The method solves the problem that an existing method has significant defects in selection of positive and negative samples.

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.