1155 results about "Drug target" patented technology

The invention relates to the field of drug target interaction, and discloses an application of a multi-modal feature fusion model in drug target binding affinity prediction, which comprises the following steps: S1, obtaining an original data set of drug target binding affinity, and taking a binding affinity index in the data set as a label of a training set and a test set; s2, acquiring representation information of other modals by using the drug sequence and target sequence representation provided in the original data set, preprocessing the acquired representation information of drugs and targets in different modals, and processing the representation information into a model acceptable feature vector form; and S3, respectively storing the multi-modal feature vectors of the drug and the target as well as the binding affinity tags in a form of pytorchsensor (. Pt). According to the method, shallow characterization of the drug and protein sequences is learned by applying the pre-training model, and the problem that a drug target binding affinity data set is insufficient is solved.

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a drug and target interaction prediction method based on multi-scale convolution feature fusion, which comprises the following steps: acquiring drug molecule data, target protein data and drug and target interaction data, constructing a drug molecule map according to the drug molecule data, and coding a target protein sequence according to the target protein data; inputting the drug molecular map into a model, and obtaining drug features through a multi-scale map convolutional network and a dynamic gating attention mechanism; inputting a target protein sequence into the model, and obtaining target features through hierarchical cavity convolution and a bidirectional gating cycle unit; through multi-head cross attention, the drug features are aligned with the target features, local and global cross-modal fusion is carried out, and drug target fusion features are obtained; based on the drug target fusion features, outputting a drug and target interaction prediction probability; and training the model according to the drug and target interaction data and the prediction probability, and applying the trained model to drug and target interaction prediction.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention is applicable to the technical field of biological information, and provides a drug target prediction method based on a graph neural network, which comprises the following steps of: respectively taking a drug and a target node as a core, and automatically extracting local sub-graphs of the drug and the target node to capture local context information of the nodes in a complex biological network; taking different types of edges as feature information and fusing the feature information into a feature learning process, dynamically adjusting contribution degrees of the different types of edges to node representation through an attention mechanism, introducing residual connection and a normalization processing strategy, and fusing node features; and performing deep mining on the fused node features by using a two-layer graph convolutional network, and calculating a potential interaction prediction score between the drug and the target. The invention further provides a drug target prediction model based on the graph neural network. According to the method, independent feature extraction and training are carried out on predicted drugs and target spots, and the accuracy and richness of node representation are effectively improved while the flexibility of the model is kept.

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

The invention discloses a drug target interaction prediction method based on a protein sequence. The method comprises the following steps: constructing a drug target database; constructing a target feature extractor based on a pre-trained protein language model; constructing a drug feature extractor based on a pre-trained molecular model; constructing a feature learning method by adopting a self-attention mechanism, constructing a drug target interaction learning method by adopting a cross attention mechanism, constructing a predictor in combination with a linear layer, and constructing a prediction drug target interaction model; training and optimizing parameters of the drug target interaction prediction model to obtain an optimized drug target prediction model; inputting a drug target into the optimized prediction model, outputting a prediction result, and finally marking potential active sites and important amino acid residues of the drug and the target through attention score visualization. According to the method, the precision of predicting the interaction between the drug and the target spot based on the protein sequence is improved, and the interpretability of the deep learning model is realized.

The invention provides a drug target affinity prediction algorithm based on protein three-mode and multi-feature fusion, and the algorithm makes full use of sequence information and two-dimensional and three-dimensional structural features of drugs and proteins, and realizes deep integration of data. The technical innovation point lies in that a multi-modal data fusion mechanism and an advanced graph neural network technology are introduced, and the sensitivity and recognition capability of the model to key biomarkers are enhanced through a dynamic attention mechanism. According to the technical advantage, the method has higher accuracy and stability when processing complex biological data, and compared with the prior art, the generalization ability and the interpretability are remarkably improved. In the aspect of technical maturity, the algorithm has passed multiple rounds of verification and shows good prediction performance and adaptability. The method is simple in operation and wide in market application prospect, can assist in new drug discovery and accelerate the drug design process, can also promote the development of the precision medical field, and particularly has important value in targeted therapy and disease prevention strategy formulation.

The invention discloses a traditional Chinese medicine multi-target interaction prediction method based on a Transform architecture. The method comprises the following steps: creating an interaction label of a medicine and a target; respectively carrying out feature extraction on the traditional Chinese medicine component data and the target protein data; fusing the traditional Chinese medicine component feature vector and the target feature vector through a cross attention mechanism; creating a data set by using the feature vector F and an interaction label of the drug and the target; the method comprises the following steps of: constructing and training a Transform encoder; inputting a to-be-detected medicine-target data set into the trained Transformer encoder, outputting a probability value of interaction between each target and the traditional Chinese medicine component, and when the probability value of interaction between the traditional Chinese medicine component and the target is greater than a preset threshold value, determining that interaction exists; the invention provides an efficient and accurate traditional Chinese medicine multi-target interaction prediction means, and is beneficial to acceleration of traditional Chinese medicine research and new medicine development processes.

The invention provides a drug target prediction method based on a 3D structure and a multilevel attention mechanism, and belongs to the technical field of drug target prediction. The technical problem to be solved is to provide a drug target prediction method based on a 3D structure and a multi-level attention mechanism. According to the technical scheme, the method comprises the following steps: collecting data information of an interaction tag among a related drug, a target sequence and a drug target protein, and processing and extracting SMILES character string characteristics of the drug; learning the numeralized protein amino acid sequence by using a convolutional neural network, completing feature coding of a target spot on the sequence through multi-layer convolution and pooling operation, and extracting target spot sequence feature data; a PDB file is read and modeled into a pocket graph, feature calculation is carried out on the pocket graph, a final protein structure representation code is generated, and structural feature data of target protein is extracted by modeling the PDB file; the method is applied to drug target prediction.

The invention relates to the technical field of biological medicines, and provides application of an ADRM1 inhibitor in preparation of a medicine for treating osteoporosis. Through the scheme of the invention, the ADRM1 affects bone metabolism, bone tissue morphological structure and osteoporosis progress, and can provide a new drug target for clinical prevention and treatment of osteoporosis. The ADRM1 inhibitor is applied to preparation of the osteoporosis medicine, and bone mass loss can be obviously reduced by down-regulating ADRM1 expression or inhibiting ADRM1 activity and inhibiting differentiation and functions of osteoclasts, so that the effect of treating osteoporosis is achieved.

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 a drug target prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing dynamic gating fusion on graph structure features and sequence features of a target drug to obtain drug features; performing dynamic feature enhancement on the digitized sequence of the target protein, and further obtaining protein features through context sensing optimization; obtaining a target affinity score of the target drug and the target protein by using a prediction model based on the drug characteristics and the protein characteristics; wherein the prediction model is obtained through feature representation training marked with actual affinity scores on the basis of a neural network. According to the method, the prediction precision of drug target interaction is improved through deep fusion of drug multi-modal features and protein sequence context perception optimization. The method can realize high-precision prediction of the drug target, and can be widely applied to the technical field of drug target prediction.

According to the pre-training language model-based drug-target interaction prediction method designed by the invention, natural language processing and graph neural network technologies are fused, context semantic features are automatically extracted from drug molecule SMILES character strings and protein sequences, and by constructing a graph structure taking drug-target pairs as nodes, the drug-target interaction is predicted. The weight of an edge is defined according to the similarity between embedded vectors, and a simplified graph convolutional network is adopted to carry out graph structure modeling to realize complex relation learning, so that the accuracy, generalization and interpretability of prediction are improved, the limitation of a traditional method on the problems of sparse feature expression, mutual information loss and'words outside a vocabulary 'is overcome, and the prediction accuracy, generalization and interpretability are improved. And finally, the accuracy of predicting the drug-target interaction relationship is 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 traditional Chinese medicine curative effect intelligent analysis and prediction platform, and relates to the technical field of traditional Chinese medicine analysis. The intelligent management platform is in communication connection with a data preprocessing and standardization module, a symptom description standardization processing module, a network relation analysis module, a network target positioning module, a network target navigation module, a drug effect prediction and evaluation module and a report generation and output module. According to the traditional Chinese medicine curative effect intelligent analysis and prediction platform, by integrating a machine learning technology, the accuracy of traditional Chinese medicine curative effect evaluation is remarkably improved, a large number of traditional Chinese medicine prescriptions, patient syndromes and curative effect data information are collected and analyzed, and the treatment effects of the traditional Chinese medicine prescriptions on different syndromes are evaluated by combining a network target and an established network relationship; and analysis results of the drug target intervention effect and the synergistic effect are provided, so that the action mechanism of the traditional Chinese medicine prescription can be deeply understood, and the traditional Chinese medicine prescription with a potential curative effect can be quickly screened out.

The invention belongs to the field of functional proteomics research, and particularly relates to a combined analysis method for analyzing interaction between a small molecule substance and a targeted protein, which is used for analyzing the small molecule substance and the protein by adopting LiP-MS (Limited Protein-Mass Spectrometry) technology and phosphorylated proteome combined analysis. According to the method, the optimized LiP-MS technology is adopted, the target protein can be directly recognized under the complex proteome background without enriching specific protein, and the problem that low-abundance protein is difficult to detect is solved; phosphorylated proteome abundance transformation protein is analyzed by combining high-resolution mass spectrometry, so that the recognition capability on a drug target and a shear variant thereof is enhanced, and the detection sensitivity is improved.

The invention discloses a construction method and application of a multi-scale and multi-level regulation and control network, and relates to the field of biological information.The construction method comprises the following steps that firstly, 20 databases about interaction among protein, transcription factors, enhancer super enhancers, ncRNA and metabolites are collected; 2, performing quality control on each database, and annotating a specific disease physiological state to each regulation and control relationship; 3, integrating molecular regulation and control relationships among different levels according to biological logic; 4, a bidirectional random walk algorithm is adopted to cooperate with a multi-level biological network to predict and mine potential drug treatment targets; the regulation and control network constructed by the method has a multi-level structure and a specific disease physiological state, the dynamic regulation and control network conforming to the real disease physiological state can be constructed, and the method can be applied to mining potential drug targets and exploring pharmacological mechanisms.

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 belongs to the field of computational biology, and particularly relates to a drug target matching recommendation method based on a multi-view neural network, and the method comprises the steps: obtaining to-be-predicted drug target information, inputting the to-be-predicted drug target information into a trained drug target recommendation model, selecting a drug target whose prediction probability is greater than an interaction threshold, and recommending the drug target to a user; according to the method, the feature vectors of the drug molecules are extracted by using the graph neural network for carrying out message passing based on points and edges, modeling and feature extraction are carried out on the drug molecules from two angles of atoms and chemical bonds, and the globality of drug sequence information is considered at the same time; the atomic characteristics, chemical bond characteristics and sequence characteristics of the drug molecules are interacted by using a cross attention mechanism, the complex relationship between the drug molecules and the target protein is captured more accurately, and the accuracy of drug-target interaction prediction is improved.

The invention discloses a drug target affinity prediction system based on cross-modal feature fusion, and relates to the technical field of biological information. The invention aims to solve the problem of low prediction precision of the existing DTA prediction method. The method comprises the following steps: preprocessing a drug SMILES character string and a protein amino acid sequence to obtain a drug molecular map, a drug SMILES sequence embedding characteristic, a protein contact map and a protein amino acid sequence embedding characteristic; according to the drug molecular diagram and the protein contact diagram, drug diagram modal characteristics and protein diagram modal characteristics are obtained; obtaining drug sequence modal characteristics and protein sequence modal characteristics by using drug SMILES sequence embedding characteristics and protein amino acid sequence embedding characteristics; fusing the drug pattern modal features and the drug sequence modal features to obtain drug fusion features, and fusing the protein pattern modal features and the protein sequence modal features to obtain protein fusion features; and acquiring the drug target affinity by using the drug fusion feature and the protein fusion feature. The method is used for predicting the affinity of the drug target.

The invention belongs to the technical field of biological medicines, and particularly relates to application of oxibenzophenone in preparation of a medicine for treating pathological cardiac hypertrophy and / or heart failure. Research results in myocardial cells of primary newborn rats show that Exibenzophenone can inhibit pathological hypertrophy of myocardial cells induced by phenylephrine (PE). Animal experiments show that the epoxybenzophenone can improve cardiac dysfunction induced by aortic arch constriction (TAC) and reduce cardiac hypertrophy and cardiac tissue fibrosis. The invention provides a new drug research and development approach and a drug action target for treating pathological cardiac hypertrophy and heart failure, and has very important medicinal value.

The invention discloses a nanometer delivery device for a gastric cancer targeted drug and a use method of the nanometer delivery device, and belongs to the technical field of drug targeted delivery. The device comprises a molecular recognition and optimization module which is responsible for accurately recognizing a tumor cell specific molecular marker and providing personalized treatment strategy recommendation; the nano-carrier design module is used for designing multifunctional lipid-polymer hybrid nano-particles and carrying out surface targeting ligand modification; the intelligent drug loading module is used for improving the stability and controllability of drug loading based on a pH / temperature sensitive release mechanism and effectively reducing the toxic influence of non-targeted tissues; the molecular imaging and monitoring module integrates a nanoprobe imaging technology and a quantum dot tracing technology, constructs a treatment-imaging integrated system, realizes real-time dynamic monitoring of a treatment process, and provides instant feedback of a molecular level; and the risk assessment module is used for establishing a safety database and providing dynamic risk assessment of the treatment process through a biological safety assessment system and long-term tracking management.

The invention belongs to the technical field of biological medicines, and particularly relates to application of a macrophage Angulin-1 gene and / or an Angulin-1 protein in preparation of medicines and diagnostic products for preventing and / or treating atherosclerosis and related diseases. Research finds that plaque and necrotic core areas of an atherosclerosis animal model are remarkably increased due to macrophage Angulin-1 gene knockout, and disease development is promoted; and the recovery of the expression level of Angulin-1 significantly inhibits the development of lesion. Cell experiments show that Angulin-1 reduces intake of oxidized low-density lipoprotein by down-regulating expression of macrophage LOX-1, so that formation of foam cells is inhibited. Therefore, the macrophage Angulin-1 can be used as an atherosclerosis drug target, a gene therapy target gene and an auxiliary diagnosis marker, and a new theoretical basis and an intervention strategy are provided for prevention and treatment of the disease.

The invention discloses a drug-target interaction prediction method based on graph neural network blocks. Accurate discrimination of the interaction between the drug and the target is a key stage of drug discovery, and the core of prediction of the interaction between the drug and the target is reasonable molecular characterization learning. The previous research has proved that GNN is effective in the aspect of drug feature coding. However, the GNN-based methods cannot effectively give consideration to local substructure characteristics of drug molecules and overall structure attributes of the molecules. For the current difficulty, a framework named GNNBlockDTI is provided. According to the GNNBlock DTI, multiple layers of GNNs are combined into a GNNBlock unit, and a feature enhancement strategy is introduced, so that a substructure mode of a drug map in a local range is captured. And filtering redundant information among the GNNBlocks by using a gating unit mechanism so as to obtain effective overall structure information of the medicine. According to the method, the global and local structure attributes of the drug molecules can be effectively coded, and the accuracy of drug-target interaction prediction is improved.

The invention relates to the technical field of computer biological information, and discloses a drug target identification method based on a deep learning framework, and the method comprises the following steps: S1, obtaining a Chemogenetic-Model data set of drug-target interaction; s2, obtaining a structure file of the target, and obtaining a structure feature representation and an edge feature representation of the target through the structure file of the target; s3, obtaining sequence tag coding feature representation, graph structure feature representation and physical and chemical feature representation of the drug and the target; s4, the sequence tag coding feature representation of the medicine is sent into a three-layer multi-scale convolutional neural network, the step length of each layer is 1, the filling is 0, and the sizes of convolution kernels are 3 * 3, 5 * 5 and 7 * 7 respectively. The reference data set used by the method is only one third of the reference data set used by a comparative advanced method, but on the same target identification test data set, the MM-IDTarget shows the performance close to or superior to that of the comparative advanced method on all Top-K evaluation indexes.

The invention provides a medicine composition for treating cancer. The medicine composition comprises an inhibitor for inhibiting FOXRED2 protein expression and application of Paraprevir in preparation of a medicine for treating cancer. According to the application disclosed by the invention, the new indication of the hepatitis C medicine paritaprevir for inhibiting the malignant process of the liver cancer HCC is found for the first time, the treatment field of the medicine in the liver cancer is expanded, and a theoretical basis is provided for developing a new target spot for treating the liver cancer.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying therapeutic targets for treating a disease. In one aspect, a method comprises: obtaining data defining a disease graph, wherein the graph comprises: (i) a plurality of nodes that each represent a respective biological entity, and (ii) a plurality of edges; processing the data defining the disease graph using a graph neural network, in accordance with values of a set of graph neural network parameters, to generate a respective node embedding associated with each node in the disease graph; and processing, for each of a collection of biological entities represented by respective nodes in the disease graph, the node embedding associated with the node to generate a target score defining a likelihood that the biological entity is a therapeutic target for treating the disease.

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 belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.