926 results about "Drug target" patented technology

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 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 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 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 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 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 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.

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.

The invention discloses a drug target binding affinity prediction method based on multi-scale feature fusion, and the method comprises the steps: introducing the multi-scale structural features of atoms, atomic groups and molecular levels in the expression of drug molecules, and combining a graph neural network and a sequence modeling module as a feature extractor; and deep interaction and fusion between different scale features of the protein and the drug are realized by using a multi-head bilinear cross attention mechanism, so that potential binding site information is effectively captured, and the accuracy and interpretation capability of affinity prediction are improved. The method not only overcomes the problems that a traditional machine learning method depends on manpower and is low in efficiency in feature construction, but also solves the technical bottlenecks that an existing deep learning method is only limited to local neighborhood information and cannot model global structural features, and the interaction relationship modeling capability is insufficient due to direct splicing of drugs and protein representation.

The invention discloses a graph neural network assisted drug-target interaction prediction method, which comprises the following steps: acquiring drug molecules and target protein information in a data set, and constructing a bipartite graph of a drug-target relationship; aggregating local neighborhood information of the bipartite graph by adopting a graph neural network to respectively obtain local embedding expression matrixes of the drug molecules and the target protein; according to the local embedding expression matrix of the drug molecules and the target protein, acquiring a global embedding expression matrix of the drug molecules and the target protein by adopting a dynamic hypergraph; performing comparative learning and fusion on the local embedding expression matrix and the global embedding expression matrix of the drug molecules and the target protein to obtain embedding expression of the drug molecules and embedding expression of the target protein; and splicing the embedding representation of the drug molecules and the embedding representation of the target proteins by adopting a full connection layer, and then inputting a prediction model to obtain the probability of the drug-target interaction relationship type.

The invention discloses a drug-target interaction prediction method based on double-flow collaborative attention and sparse feature fusion, and belongs to the technical field of computational biology. The method solves the problem that the existing method cannot capture the sub-structure discrimination features and the key binding region features of the drug-target interaction pair. According to the method, a double-flow collaborative attention strategy combining a multi-scale space attention mechanism and a channel enhanced attention mechanism is adopted to cooperatively capture discriminative features of substructures, the multi-scale space attention mechanism utilizes a multi-branch convolutional layer to adaptively integrate space substructure features, and molecular representation of each substructure is enhanced; the channel-enhanced attention mechanism mitigates the inconsistency of substructure features. The sparse attention mechanism can highlight the key features while suppressing the noise, and the cross attention mechanism improves the extraction capability of the features of the key combination region through feature interaction between the modeling drug and the target. The method can be applied to drug-target interaction prediction.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.

The invention relates to a protein implicit binding site prediction method based on deep learning. The method comprises four stages of data acquisition, feature engineering, model training and verification application. The method comprises the following steps: firstly, acquiring initial data from a protein database, performing data screening and sequence redundancy elimination, performing standardization processing on a PDB structure file, and generating a sample; then, constructing a composite feature space containing basic protein features and evolutionary conservative features; thirdly, feature dimension reduction is achieved through a hierarchical feature conversion and integrated feature selection method, and a model is constructed; finally, verification and application show that the method obtains 99.44% prediction accuracy on 954 non-redundant protein structures, ROC-AUC and PR-AUC both reach 0.9998, and the overlapping rate with a BioLiP database is 44.4%. The method can accurately identify potential drug binding sites, especially shows good generalization ability for drug targets difficult to prepare, and has important application prospects.

The invention discloses a multi-modal data fusion drug-target affinity prediction system based on Graph Transform. The system comprises a data preprocessing module, a graph representation module, a text representation module and an affinity prediction module. The data preprocessing module is responsible for analyzing and processing the SMILES character string of the compound and the protein ID, and extracting the structural information of the compound and the three-dimensional structural data of the protein. And the affinity prediction module performs multi-modal fusion on the graph features and the text features, processes fusion feature vectors through a feedforward neural network comprising three full-connection layers, and outputs a prediction result of drug-target affinity. According to the method, multi-modal information of a graph structure and a sequence text is combined, the accuracy of cross-domain drug-target affinity prediction can be effectively improved, and the method has a wide application prospect.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention belongs to the field of medical technology analysis, and discloses an analysis method and system for revealing a hidden binding pocket of a drug target, and the method comprises the steps: firstly obtaining a representative conformation metastable state of a target protein through conventional molecular dynamics simulation and clustering analysis; secondly, constructing a Markov state model to analyze a dynamic transformation rule between conformations; carrying out enhanced sampling by adopting meta-dynamics, and deeply exploring a rare conformation space; and finally, constructing a free energy landscape to quantitatively evaluate the relative stability of the conformation, and identifying a hidden binding pocket in the stable rare conformation. According to the method, the limitation of a single calculation means is overcome, a full-chain calculation system of dynamic conformation analysis-hidden cavity feature mining-novel ligand rational design is constructed, and the formation mechanism and potential druggability of the hidden pocket can be comprehensively revealed from the two dimensions of dynamics and thermodynamics; and an efficient and accurate calculation framework is provided for research and development of innovative drugs targeting difficult drug targets.

The invention discloses a deep learning prediction method fused with multi-modal features, which can accurately identify protein hidden binding sites in a ligand-free (apoo) state. The method comprises the following steps of: firstly, constructing a protein graph by taking residues as nodes and taking C alpha distance less than or equal to 14 as edges, wherein node feature sets comprise amino acid one-hot, secondary structures, atomic attributes, protein language model embedding and BLOSUM62 evolutionary information, and edge features comprise distance and angle similarity; then capturing three-dimensional geometric equivariant features by adopting an equivariant graph neural network (EGNN), and modeling a chemical topological relation by using a graph isomorphic network (GINE) with edge features; eGNN and GINE double-branch feature fusion and global dependence integration are realized through gating cross attention and gating multi-head attention; and finally, inputting the fusion features into a Kolmogorov-Arnold network (KAN) classifier, and predicting whether each residue belongs to a hidden binding site or not. The method can adapt to large-scale conformation change without coordinate alignment, AUC and F1 on a standard data set are remarkably superior to those of an existing method, high robustness and generalization are kept for multi-chain protein and complex conformation, and the method can be widely applied to drug target discovery and structure-driven drug design.

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 targeted delivery device and method, and belongs to the technical field of intelligent medical treatment. The device comprises the following modules: an intelligent targeted recognition module for accurately recognizing a gastric cancer specific molecular marker and improving the selectivity and specificity of targeted recognition in combination with a dynamic surface modification strategy; the microenvironment real-time monitoring module is used for monitoring key physiological parameters of the gastric cancer microenvironment in real time; the intelligent path planning module is used for dynamically calculating and optimizing a drug delivery path and realizing accurate drug positioning; the personalized drug administration regulation and control module is used for accurately predicting the drug dosage demand of an individual and dynamically and finely adjusting the drug administration amount in real time; the drug resistance risk management module is used for early warning a drug resistance risk in advance and automatically generating a personalized alternative drug administration strategy through multi-dimensional drug resistance analysis and real-time early warning; and the intelligent drug release module adopts a temperature-sensitive / pH-sensitive polymer shell layer and a nano valve to control drug release, so that precise gradient release of drug concentration is realized.

The invention relates to the technical field of biomedical engineering, in particular to a separated type organ-like chip model and a use method thereof.The model comprises a chip, an organ-like unit module and a fluid control system, and the use method comprises the steps that firstly, the chip is assembled and prepared; step 2, cell inoculation and culture; step 3, establishing and operating a fluid circulation system; step 4, organ-like unit function detection and data acquisition; 5, analyzing an experimental result and adjusting the model; the model is connected with a micro-fluidic channel through an independent cavity, and the characteristics, cell types and culture conditions of a cell culture bracket can be regulated and controlled according to the characteristics of simulated organs. Meanwhile, the micro-fluidic channel ensures stable and ordered material exchange among the organoid units, and is beneficial to more accurately screening out effective treatment schemes and drug targets.

The invention relates to the field of biomedicine, in particular to application of PXDC1 in preparation of a product for treating myocardial hypertrophy. The invention discloses the effectiveness of inhibiting PXDC1 expression in treating cardiac hypertrophy and controlling the occurrence and development process of the cardiac hypertrophy for the first time. The invention provides a novel drug target and strategy for treating myocardial hypertrophy, and the novel drug target and strategy can reduce the expression of a myocardial hypertrophy biomarker, reduce the size of myocardial cells, improve the ejection fraction and short axis shortening rate of the heart, reduce the weight ratio of the heart and relieve the degree of myocardial fibrosis. Besides, the invention clarifies the action mechanism of PXDC1 in myocardial hypertrophy, deepens the understanding of the pathogenesis of the disease, and lays an important foundation for further exploration of myocardial hypertrophy-related cytobiology and molecular biology processes.

According to the screening method for the potential protein biomarkers and the drug targets of the gastric cancer, Mendel randomization analysis in a proteome range is adopted, the genetic causal relationship between circulating plasma protein and the risk of the gastric cancer is evaluated, and finally the remarkably related protein is identified. According to the screening method of the potential protein biomarker and the drug target of the gastric cancer, provided by the invention, the potential association between circulating plasma protein and the gastric cancer is systematically revealed by integrating Mendel randomization, single-cell RNA sequencing analysis, space transcriptome analysis, virtual drug screening, molecular docking, molecular dynamics simulation and other methods.

The invention discloses a drug target prediction method based on fragment-level local and global feature fusion, and belongs to the technical field of computational biology and artificial intelligence drug design. Comprising the following steps: acquiring a medicine SMILES character string and a protein amino acid sequence; respectively segmenting the drug SMILES character string and the protein amino acid sequence to obtain a drug structure fragment sequence and a protein functional fragment sequence; and inputting the drug structure fragment sequence and the protein function fragment sequence into a pre-trained drug-target interaction prediction model to obtain a prediction probability of drug-target pair interaction. Compared with the prior art, the method has the advantages that convolution feature extraction, a multi-head attention mechanism and a gating fusion strategy are combined, an end-to-end DTI prediction framework is constructed, and the interaction between drugs and targets can be comprehensively mined.

The invention provides a TF and Her2 targeting bispecific antibody coupling drug as well as a preparation method and application thereof, and belongs to the technical field of biological drug preparation. The bispecific antibody provided by the invention can target TF and / or Her2 antigens in tumor cells, and has the advantages of high stability, easiness in expression, purification and coupling and the like. The bispecific antibody can be specifically combined with a tumor surface antigen and internalized into tumor cells, so that the tumor cells can be specifically killed. The bispecific antibody coupling drug prepared on the basis of the bispecific antibody has a good tumor inhibition effect in a cell model and an animal model, is nontoxic and harmless to animals, and has an excellent cancer treatment potential.

The invention relates to a drug target determination method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a protein heat map corresponding to candidate proteins; based on a feature extraction model, extracting features of the protein heat map to obtain heat map features corresponding to the candidate proteins; based on the heat map features corresponding to the candidate proteins, clustering the candidate proteins to obtain a plurality of candidate protein sets; based on the temperature data and the protein concentration data of each candidate protein in a candidate protein set, determining the protein concentration difference significance corresponding to the candidate protein set, and based on the protein concentration difference significance and the protein concentration data of the candidate proteins, determining the protein concentration difference significance corresponding to the candidate protein set; and determining standard reference data of the candidate proteins, and determining a drug target based on the standard reference data of each candidate protein in the plurality of candidate protein sets. According to the embodiment of the invention, the drug target can be accurately determined.