54 results about "Drug repositioning" patented technology

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention provides a drug relocation method and a drug relocation system. The method comprises the following steps: predicting an expression profile of a drug after cell line disturbance according to a chemical structure of the drug, dose information of the drug and an undisturbed expression profile. And calculating the differential expression profile of the gene in the cell line based on the expression profiles before and after the cell line is disturbed by the drug. And for each drug, according to the differential expression profiles of the genes, calculating an average value of the differential expression profiles of the genes after the drugs disturb different cell lines, and sorting the genes based on the average value to obtain a sorting list of the differential expression profiles of the drugs. And according to the gene characteristics of the target disease and the sorting list, calculating the enrichment score of each drug on the target disease, and according to the enrichment score, evaluating the potential efficacy of the drug on the target disease. The drug relocation method provided by the invention can be used for giving disease specific gene characteristics for drug library screening.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention discloses a drug relocation model construction method for simultaneously predicting drug-target interaction and drug-disease incidence relation, and belongs to the field of drug research and development, and the method comprises the following steps: integrating heterogeneous networks and attribute characteristics of drugs, targets and diseases, learning multi-relation node embedding by using RGCN, and constructing a drug relocation model for simultaneously predicting drug-target interaction and drug-disease incidence relation; a Gelato algorithm is combined to enhance a network structure, an auto-covariance is introduced to calculate a potential association score, and drug-target interaction and drug-disease association are synchronously predicted; the weighted cross entropy and N-pair loss joint optimization is adopted, unbiased training is realized, the problems of class imbalance and network sparseness are solved, and the model generalization ability and prediction precision are improved.

The invention relates to the technical field of medicine data analysis, in particular to a method, a system and equipment for generating a medicine relocation research report and a medium. According to the method for generating the drug relocation research report, a drug relocation request submitted by a pharmaceutical enterprise is decomposed into three sub-tasks of indication extension analysis, target matching analysis and drug effect simulation verification, and an indication extension analysis agent, a target matching analysis agent and a drug effect simulation verification agent are called respectively; the contribution value of each link is calculated through multi-agent cooperation, and finally a relocation research report containing potential rating and confidence evaluation is generated based on the drug-disease knowledge graph. By implementing the technical scheme provided by the invention, the efficiency and reliability of drug relocation analysis are improved, and scientific and credible decision support is provided for pharmaceutical enterprises.

The invention discloses a drug relocation method based on multi-source information fusion and multi-level hypergraph learning, and belongs to the technical field of drug relocation. On a model architecture, protein biological entity information is introduced for multi-source biological information fusion, and a global hypergraph network and a local hypergraph network are constructed; in the local hypergraph network, constructing a drug-disease local sub-network, a drug-protein local sub-network and a disease-protein local sub-network, and mining local feature information by adopting a targeted graph neural network; in the global hypergraph network, constructing a drug-disease-protein complex association network and directly mining global feature information; besides, the multi-level features and the original features are fused by adopting an attention weight strategy and a residual jumping strategy, so that a finally predicted drug-disease correlation matrix is obtained, and accurate prediction of drug-disease correlation can be realized.

The invention discloses a drug relocation method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical domain knowledge heterogeneous graph; generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair based on the biomedical domain knowledge heterogeneous graph; generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network; and based on disease knowledge embedding, drug knowledge embedding, disease structure embedding and drug structure embedding, obtaining a drug relocation result. According to the method, complex biological network characteristics are accurately captured and complex entity information is finely modeled through an innovative drug relocation model, so that accurate drug relocation is realized.

The invention discloses a drug relocation method based on multi-modal diagram learning, and belongs to the technical field of drug research and development. The implementation method of the invention comprises the following steps: 1, constructing a multi-modal drug relocation network consisting of sub-networks with the same number of input modals, and enabling the sub-networks to independently process single-modal data; 2, performing characterization learning and characterization fusion on the sub-networks in the multi-modal drug relocation network; 3, carrying out adaptive graph structure learning on the multi-modal drug relocation network representing fusion; 4, loss optimization is carried out on the multi-mode drug relocation network of adaptive learning, and then a drug relocation prediction result is obtained; compared with the prior art, the method has the advantages that representation learning and self-adaptive learning are carried out through similarity and incidence matrix calculation and by using an attention mechanism and graph neural network learning high-level features on the basis of the sub-network constructed on the basis of multi-modal data, and the generalization ability of drug relocation downstream tasks of multi-modal graph learning is improved.

The invention discloses a drug-disease association prediction method, system, device and medium, and relates to the technical field of drug relocalization, and the method comprises the steps: constructing a drug similarity network and a disease similarity network; obtaining a binary adjacency matrix through k-neighbor graphs of different nodes in the two similarity networks, carrying out weighted fusion on the binary adjacency matrix through an attention coefficient to obtain a soft adjacency matrix, and carrying out fine-grained graph convolution updating to obtain drug features and disease features; extracting heterogeneous node representations of drugs and diseases in the biochemical heterogeneous network, and carrying out dynamic weight distribution on different representations to obtain final embedding of drug nodes and disease nodes; and splicing the final embedding of the drug nodes and the final embedding of the disease nodes, and performing prediction according to the spliced features to obtain the drug-disease association probability. According to the method, the potential complementary relationship between the two is fully mined, and the heterogeneous feature fusion effect is improved, so that the performance and robustness of drug-disease association prediction are integrally enhanced.

The application discloses a candidate drug ranking method and system based on topological coding supervision reconstruction, and belongs to the field of bioinformatics. The method comprises the following steps: step S1, obtaining drug design data; step S2, constructing a drug-biological entity association matrix; step S3, calculating a drug side topological matrix for describing the connection relationship between drugs and a biological entity side topological matrix for describing the connection relationship between biological entities according to the association matrix; step S4, constructing a finite-order topological filter and a spectral stable topological filter to form a bidirectional topological evidence field; step S5, obtaining a reconstruction supervision matrix through a supervised reconstruction objective function; and step S6, obtaining candidate drug ranking based on the reconstruction supervision matrix. The application can improve the high-order mechanism recognition ability, robustness and interpretability in drug-target prediction, drug repositioning, candidate molecule screening and molecular generation result ranking.

The invention discloses a drug-miRNA interaction prediction method based on a meta-path expert network, belongs to the field of biological information, and relates to a drug-miRNA interaction prediction method. Prediction of the interaction between the drug and the miRNA not only contributes to revealing the drug action mechanism, but also can provide theoretical support for drug target discovery, drug relocation and individualized treatment. A traditional method mainly verifies drug-miRNA association based on biological experiments, but has the problems of high cost and long consumed time. However, the existing calculation method does not fully excavate the relationship between the drug and the miRNA. The invention provides a drug-miRNA interaction prediction method based on a meta-path expert network, and the method comprises the steps: firstly fusing various similarities, and constructing a heterogeneous graph in combination with the known drug-miRNA interaction; a heterogeneous graph is converted into a plurality of isomorphic graphs through meta-paths, semantic information of different meta-path graphs is learned by using a graph convolutional network, and node features with rich semantics are obtained. And finally, fusing node features of different meta path diagrams by using a hybrid expert model to obtain final drug node features and miRNA node features, and performing interaction prediction through MLP.

The invention discloses a drug relocation prediction method based on causal inference. The method comprises the following steps: 1) constructing a multi-modal heterograph; 2) obtaining a medicine functional embedding expression; 3) calculating a causal intensity weight of a drug-disease edge based on Do-calculation, and constructing a causal perception heterograph; 4) constructing a structural causal model, and calculating the average treatment effect of the drug on the disease; 5) designing a causal heterogeneous graph convolutional network, and learning node deep causal characterization; 6) identifying and correcting bias and errors; (7) What-if analysis is conducted through an anti-fact reasoning module, and the difference between an anti-fact result and the effect is calculated; and 8) fusing node deep causal characterization, a causal intervention result and an anti-fact reasoning conclusion, outputting a drug-disease causal association probability, and tracing a key causal path and an action mechanism. According to the method, the problem that the causal effect and the false correlation are difficult to distinguish in a traditional drug relocation method is solved, and the reliability, the interpretability and the generalization ability of a prediction result are improved.

The present invention relates to a pharmaceutical composition for preventing or treating coronavirus disease 2019, and more specifically to a pharmaceutical composition for preventing or treating coronavirus disease 2019 found by drug repositioning technology using drug virtual screening technology. The pharmaceutical composition for preventing or treating coronavirus disease 2019 according to the present invention is a composition obtained by finding new uses for drugs, which have already been proven effective, for preventing or treating coronavirus disease 2019 by drug repositioning technology. The pharmaceutical composition is useful because it has significantly lower side effects than new drugs and can be rapidly applied to clinical practice.

This invention relates to the field of bioinformatics, and particularly to an artificial intelligence-based drug relocation method, apparatus, device, and storage medium. The AI-based drug relocation method of this invention first constructs a heterogeneous network, and then builds a relocation model including a multi-relational heterogeneous graph encoder, a multi-head attention mechanism module, and an interactive decoder. Based on the heterogeneous network, it predicts the treatment score of a drug for a disease, achieving better test metrics compared to other methods. Furthermore, this invention interprets the prediction results, identifying key functional nodes and explaining the pharmacological mechanism of drug treatment for diseases.

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 discloses a drug repositioning method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical field knowledge heterogeneous graph; based on the biomedical field knowledge heterogeneous graph, generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair; by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network, generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair; and based on the disease knowledge embedding, the drug knowledge embedding, the disease structure embedding and the drug structure embedding, obtaining a drug repositioning result. The application accurately captures complex biological network characteristics and finely models complex entity information through an innovative drug repositioning model, so that accurate drug repositioning is realized.

The present invention belongs to the field of pharmaceutical technology, and discloses a febuxostat tetravalent platinum prodrug, a preparation method and a preparation. The present invention synthesizes two small molecule tetravalent platinum prodrugs of tetravalent platinum, febuxostat cisplatin and febuxostat oxaliplatin, for the first time, and prepares a febuxostat cisplatin nanoformulation. The nanoformulation solves the shortcomings of cisplatin such as poor water solubility, congenital or acquired drug resistance, and obvious toxic and side effects, and has many advantages such as passive targeting of tumor tissue. It is not only economical and practical, but also provides possibilities for industrial production. By using the idea of ​​"drug repositioning", the anti-tumor effect of febuxostat is further studied, and a combined anti-tumor effect with platinum drugs is achieved. At the same time, a carrier-free preparation method is adopted to avoid the unknown toxicity of the carrier, the preparation has good safety, and the tumor targeting of platinum drugs is achieved by nanoformulation, while enhancing the accumulation of tumor sites and reducing systemic toxic and side effects.

The invention belongs to the technical field of biological medicines, and particularly relates to an application of a Xiaoxiao pill extract in preparation of a medicine for treating breast cancer, an extraction method of the Xiaoxiao pill extract comprises the following steps: taking Xiaoxiao pill pills, adding the Xiaoxiao pill pills into a 1640 culture medium, soaking for the first time, performing ultrasonic treatment, soaking for the second time, taking supernatant, and filtering to obtain filtrate which is the Xiaoxiao pill extract; the mass volume ratio of the Xiaoxiao pills to the 1640 culture medium is 3g: (30mL-40mL). The obvious effect of the Xinxiao pill extract in treating the breast cancer is found for the first time. The discovery not only breaks through the traditional treatment limitation of Xiaoxiao pills for ulcers and pyogenic infections, expands the clinical application range of the Xiaoxiao pills, but also combines the traditional Chinese medicines with the modern bioinformatics technology through the modern medicine relocation strategy, verifies the anti-breast cancer activity of the Xiaoxiao pills, can be prepared into various dosage forms such as tablets, capsules and the like with various pharmaceutic adjuvants, and has wide application prospects. The clinical applicability is good.

The present application relates to the field of bioinformatics, and proposes a drug target correlation identification method based on spatial atlas and isometric network. Firstly, the three-dimensional spatial geometric atlas of target protein and the microcosmic chemical topological graph of drug molecule are deduced and constructed respectively, so as to losslessly reserve the physical conformation of receptor and the local pharmacophore of small molecule. Secondly, the three-dimensional isometric graph neural network and the graph message passing mechanism are jointly used for deep feature representation. Finally, the affinity measurement strategy based on hyperbolic space is introduced, the features are losslessly projected into the Poincare ball model with constant negative curvature, and the correlation probability between drugs and targets is predicted by using the non-Euclidean geometric distance. The present application can effectively overcome the feature congestion problem of traditional space, fully excavate the implicit hierarchical information inside complex biological molecules, significantly improve the accuracy and generalization robustness of drug target correlation analysis, and provide effective technical support for new drug research and development, drug repositioning and precise medication.

The application relates to the technical field of bioinformatics, in particular to a drug repositioning method based on a graph generative adversarial network and a variational autoencoder, which comprises the following steps: a drug similarity matrix, a target similarity matrix and a drug-target interaction matrix are utilized to construct a drug-target heterogeneous network, an adjacency matrix and an initial feature matrix; the variational autoencoder is adopted to utilize the feature extraction and fusion functions of a convolutional neural network to encode initial features in the adjacency matrix and the initial feature matrix of the drug-target into hidden variables; based on the drug-target heterogeneous network, an adversarial model is utilized to strengthen the feature representation of drugs and targets in the hidden variables; after multiple iterations of the adversarial model, the hidden variables in the generator are extracted to construct a prediction matrix; and the unknown drug-target interaction is predicted. The application has good performance in drug-target interaction prediction and good effect in drug repositioning.

The present application relates to the technical field of computational biology, and discloses a drug repositioning method and system based on multi-task learning and deep cross-domain, which comprises the following steps: S1. Collecting data with a "target node-drug node-disease node" ternary relationship; S2. Inputting target domain and auxiliary domain data into a feature extraction network for feature extraction; S3. Building a two-layer graph attention neural network for the target domain and the auxiliary domain respectively; S4. Fusing the enhanced deep target domain feature vector and the auxiliary domain feature vector; S5. Setting a loss function of the graph attention neural network of the target domain and the auxiliary domain, and performing multi-task learning on the graph attention neural network; and S6. Outputting a final predicted drug-disease association matrix to complete drug repositioning. The present application solves the problem that the prior art does not unify the prediction of drug-target interaction and the prediction of drug-disease association, and has the characteristics of accuracy and strong robustness.

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 present invention provides a drug repositioning method that automatically selects fused meta-paths, which relates to the technical field of drug repositioning. The method includes: taking the view embeddings learned by the first meta-path from drug entities and disease entities as inputs, and performing meta-path feature aggregation based on the self-attention mechanism; calculating the cosine similarity between each view embedding and other view embeddings, sorting the importance scores of the views, and taking the top k views to update the disease tensor and the drug tensor; fusing the multi-view embeddings of drugs and diseases based on the Transformer cross-attention multi-view fusion module; dynamically transmitting the feature information of proteins to drug and disease nodes through a heterogeneous graph neural network; and predicting and outputting the association scores of the embeddings obtained for drugs and diseases through an MLP layer. The technical solution of the present invention overcomes the problem in the prior art that the association between drugs and diseases cannot be deeply explored.

The present invention proposes a drug repositioning method and system based on biological knowledge and network topology, comprising a network construction module, a feature learning module, a model training module, a drug repositioning module, and a result presentation module. The network construction module constructs biological network data into a heterogeneous information network. The feature learning module executes server computing instructions to obtain biological feature matrices and network feature matrices for drugs and diseases. The model training module obtains input parameters and trains a drug discovery model on the server. The drug repositioning module executes drug repositioning instructions after obtaining the drug repositioning model. Finally, the drug repositioning results are output and presented through the presentation module. The present invention directly acts on biological network data sets containing biological knowledge, enabling drug repositioning in heterogeneous biological networks with high accuracy and the ability to effectively discover new uses for known drugs.

The invention provides a drug-target interaction prediction method, device and equipment based on comparative learning and Transformer, and relates to the technical field of computer artificial intelligence and biological medicine, a to-be-predicted drug-target sequence is acquired and is represented and encoded as to-be-predicted drug sequence representation and to-be-predicted target sequence representation; inputting the to-be-predicted drug sequence representation and the to-be-predicted target sequence representation into a pre-trained prediction model; and sequentially performing feature extraction and feature mapping through a Transform and a multilayer perceptron (MLP) network in the prediction model, calculating an interaction probability between a to-be-predicted drug feature mapping vector and a to-be-predicted target feature mapping vector, and taking a drug-target pair of which the interaction probability is greater than a preset threshold value as a predicted interaction result. According to the method, the accuracy and generalization ability of drug-target interaction prediction are improved, and the method can be widely applied to new target discovery, drug relocation, drug screening and precision medical treatment.

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 relates to the technical field of bioinformatics, in particular to a drug repositioning method based on reinforced symmetric metric learning and a graph convolution network, which comprises the following steps: a drug-disease heterogeneous network is constructed by integrating the correlation of drugs and diseases and biomedical information. The heterogeneous network comprises a drug-drug similarity network, a disease-disease similarity network and a drug-disease correlation network. A graph convolution network is applied to learn the node features of drugs and diseases, and potential drug-disease correlations are predicted to supplement the missing drug-disease correlation information. A reinforced symmetric metric learning method with adaptive margins is used to learn the potential vector representation of drugs and diseases, and the symmetric learning of drug-centered and disease-centered is considered. Based on the potential vector representation learned in the unified metric vector space, new drug-disease correlations are identified through a metric function. The application is simple and effective, and has good performance in drug repositioning prediction.

The invention discloses a drug relocation prediction method and system based on incomplete multi-view learning, and the method comprises the steps: S1, obtaining drug-disease associated data and various types of biological and pharmacological auxiliary information; s2, on the basis of the auxiliary information, constructing adjacency matrixes of each single view angle of the disease and the medicine, wherein the adjacency matrixes comprise incomplete adjacency matrixes; s3, constructing a prediction model, wherein an objective function of the prediction model comprises an incomplete graph interpolation loss item, a single-view-angle correlation prediction loss item, a multi-view-angle weight constraint item and a multi-view-angle global consistency loss item; and S4, taking the label matrix and the adjacent matrix as input, taking the prediction incidence matrix as output, and performing optimization training on the prediction model through an alternating direction minimization strategy. According to the scheme, the interpolated graph is regarded as an intermediate variable for promoting link prediction optimization, drug-disease association prediction is directly carried out on the incomplete graph, graph interpolation, multi-view fusion and link prediction are organically integrated, and the prediction performance is effectively improved through mutual reinforcement among modules.

The application belongs to the field of proteomics research, and discloses application of a reagent in preparation of a product for detecting or treating IgG4-RD. Six PD-L1 related DEPs (RPS6KB1, NFATC1, CD3E, PTEN, PTPN6 and JAK2 proteins) can be used as biomarkers for patient stratification and identification of individuals with high risk of severe disease progression or fibrosis sequelae, secondly, activation of the PD-1 / PD-L1 pathway suggests that immune checkpoint regulation may be a new treatment method for IgG4-RD, thirdly, significant enrichment of metabolic pathways indicates that IgG4-RD is a metabolic disorder disease, which provides a way for targeted immunometabolism. Finally, drug repositioning research highlights resveratrol and sorafenib as candidate therapeutic drugs, and the potential combination of these drugs provides a new method for rebalancing immune activation and inhibition in IgG4-RD.