102 results about "Drug structure" patented technology

The invention relates to the field of drug-target affinity prediction, in particular to a drug-target affinity prediction method based on a multi-scale mixed attention network. According to the method, global residue characteristics can be learned by protein pocket residues by utilizing a self-attention mechanism, so that the global characteristics of the protein are learned. In addition, a cross-modal feature fusion mechanism is utilized, protein and drug structural features are enhanced in sequence features, cross-modal feature fusion in entities is enhanced, meanwhile, a cross-entity interaction module is utilized to recognize key atoms of drug molecules or key residues of protein, and therefore the overall performance of the model is improved. Finally, multi-level protein feature extraction is further introduced to further extract protein features.

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

The invention discloses a structural analysis system and a modeling method for a protein degradation targeting chimera compound, and belongs to the field of computer-aided drug molecule design. The structural analysis system comprises a chimera component analysis module, a chimera induced complex component analysis module and a chimera linker length distribution analysis module, so that automatic structural splitting and analysis of the protein degradation targeted chimera drug are realized; through protein-protein docking, conformation search, conformation fusion and optimization, conformation clustering and conformation scoring and sorting, efficient and accurate structure modeling is realized. According to the method, automatic component splitting and structure analysis can be carried out, so that a potential drug molecule binding mode is comprehensively explored, the trial and error cost and time of traditional drug structure analysis are remarkably reduced, the design efficiency and success rate of a protein degradation targeting chimera are remarkably improved, and through automatic parameter optimization, the design efficiency of the protein degradation targeting chimera is greatly improved. And the high efficiency and reliability of the structure modeling process are ensured.

The present application belongs to the technical field of biomedical functional materials, and particularly relates to a drug structural coating having a super-hydrophobic property and a preparation method therefor. The preparation method in the present application uses a phenol amine compound to undergo oxidation, crosslinking and polymerization reactions under the action of an oxidant to generate a polyphenol nanoparticle film layer outside drug-loaded nanoparticles. The polyphenol nano film layer not only serves as a "protective layer" for the drug-loaded nanoparticles and a cross-linking substance between the drug-loaded nanoparticles, but also can immobilize the drug-loaded nanoparticles onto the surface of a substrate material, so that after reaction and deposition, a drug nanometer multi-level structural coating having a micro-nano topological structure can be formed on the surface of the substrate material. During an irradiation process, an ultraviolet cross-linking device can generate a large number of active oxygen free radicals, so that by means of ultraviolet irradiation on a sample, the active oxygen free radicals fully react with a micelle protective layer in the drug coating, so as to convert hydrophilic phenolic hydroxyl in the phenol amine into hydrophobic quinonyl. Therefore, the micro-nano topological structure and the hydrophobic group cooperatively act to endow the surface of the material with the super-hydrophobic property.

The embodiment of the invention provides a drug structure-activity relationship analysis method and device based on a multi-level structure, and the method comprises the steps: carrying out the multi-level molecular structure characterization of a compound, the multi-level molecular structure comprises a clustering structure, an original structure and a fragment structure; calculating the weights of the clustering structure, the original structure and the fragment structure based on the research and development state of the compound; and determining an activity relationship between the compound and the target, and calculating a target association credibility score based on the activity relationship, the evidence supporting the activity relationship and the research and development state weight of the compound.

The invention relates to the technical field of image analysis, in particular to a targeted drug curative effect prediction method based on image recognition, which comprises the following steps: acquiring tissue images and nuclear morphological parameters by a microscope, establishing a database in combination with transcripts, extracting an injury area, recognizing image features through a convolutional neural network, and constructing a prediction model; and inputting candidate drug molecular structures for molecular docking, calculating a repair progress by combining animal verification to establish a curative effect model, predicting drug scores and response time based on the curative effect model to generate a ranking list, screening high-score drug cells, verifying monitored survival, comparing, predicting and outputting a result. The method comprises the following steps: extracting a cell nucleus form, revealing a relation between damage and molecular abnormality in combination with a transcriptome, identifying a target spot corresponding to an abnormal mode and pathological change through deep learning, performing affinity prediction and animal verification on a drug structure, quantifying the repair progress by adopting image difference, and evaluating the curative effect with two dimensions of structure and function. And curative effect scores and response prediction are output to realize system sequencing, so that drug screening is more accurate and practical.

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

The invention provides a coupling drug and a preparation method thereof, and relates to the field of antibody drugs. The structure of the coupling drug provided by the invention is as shown in the following formula I, X is an antibody or fusion protein containing an Fc fragment, GlcNAc is N-acetylglucosamine, Man is mannose, Fuc is fucose, Gal is galactose, Fucc * is a fucose derivative and contains Dxd; and m is 1-4. Compared with DS-8201, the antibody coupling drug prepared by the invention has better hydrophilicity, stability and plasma stability. # imgabs0 #

The present invention provides a method for predicting drug-target interactions by integrating multi-layer drug structure information. First, the drug and target information in the pharmacomics database is preprocessed to extract the drug and target information with interactions; secondly, the drug SMILES molecular fingerprint is represented as a molecular graph structure, and the molecular completion graph convolutional neural network and the Transformer network are used to extract drug feature information; then, the target sequence information is processed using a convolutional neural network, and the target feature information is extracted; finally, the extracted drug feature information and target feature information are sent to a classification model for training, the model is saved, and the relationship between the drug and the target is predicted. The present invention effectively extracts the feature information in the drug molecular structure, has a higher accuracy rate when predicting the drug-target relationship, improves the efficiency and accuracy of drug-target relationship verification, effectively shortens the drug research and development cycle, and greatly reduces the cost of new drug research and development.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for computationally docking a small molecule or ligand to a prion-protein In one aspect, a system comprises receiving data comprising a structure of a candidate drug, a structure of a prion-protein filament comprising one or more prions, and a location of a binding site in the prion-protein filament structure, generating a plurality of stacked pose configurations of the candidate drug using at least the candidate drug structure, determining a corresponding docking score for each stacked pose configuration in the plurality of stacked pose configurations in accordance with a measure of a likelihood of binding for the stacked pose configuration in the location of the binding site in the prion-protein filament, and taking an action based on the corresponding docking scores.

Embodiments of the present disclosure provide a multi-level structure-based drug structure-activity relationship analysis method and device, the method comprising: performing multi-level molecular structure characterization on a compound, the multi-level molecular structure comprising cluster structure, original structure and fragment structure; calculating the weights of the cluster structure, the original structure and the fragment structure based on the research and development state of the compound; and determining the activity relationship between the compound and a target point, and calculating a target point association credibility score based on the activity relationship, evidence supporting the activity relationship and the research and development state weight of the compound.

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

A method for predicting drug-target interaction includes: determining a first drug association matrix according to drug attribute information, the drug attribute information including at least one of a drug structure similarity, a pharmacophore similarity, a side effect similarity, and a GO pathway-based similarity of drugs, and the first drug association matrix being used to characterize feature information of each drug on at least one drug attribute; determining a first target association matrix according to target attribute information, the target attribute information including at least one of a target structure similarity and a target interaction relationship of targets, and the first target association matrix being used to characterize feature information of each target on at least one target attribute; and predicting a probability of interaction between a drug and a target according to the first drug association matrix and the first target association matrix.

The invention belongs to the field of liposome drug structure detection, and particularly relates to a liposome drug structure detection device and method.The liposome drug structure detection device comprises a detection table body and a mounting mechanism arranged on the detection table body, and the mounting mechanism comprises a sliding assembly and a protection assembly; the sliding assembly comprises a sliding groove, a threaded rod, a sliding rail support, a threaded shaft, a connecting block, a supporting shaft, a mounting shell and a detection lens, the sliding groove is formed in the rear side wall of the detection table body, the threaded rod is mounted in the sliding groove, and the sliding rail support is mounted on the outer surface of the threaded rod; the reciprocating screw rod is driven by the motor to rotate, the scraping plate and the cleaning shell slide on the sliding rod to a certain extent through the reciprocating screw rod, then the scraping plate and the cleaning shell form an elastic structure through the connecting spring, the elastic structure is tightly attached to the detection table body, and the scraping plate slides in the detection table body. Scraping of the to-be-detected material is effectively promoted, and cleaning and using are convenient.

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 invention discloses a single cell chemical perturbation transcription response prediction method based on a generative neural network and application thereof, the generative neural network of a prediction model constructs a learnable conditional mapping function, and multi-modal embedding is constructed during prediction model training. A training result is optimized by adopting a zero-expansion Gaussian negative logarithm likelihood loss function; the multi-modal embedding is the key input of a conditional mapping function and comprises gene expression semantic features, multi-source biological priori knowledge and drug molecular structure semantic features, so that the learning ability of the conditional function on the true disturbance law of the known drug structure on the gene expression of the known cell type can be remarkably improved; the trained prediction model can break through the limitation that traditional drug reaction research is highly dependent on experimental conditions, low in flux and high in cost, and prediction single cell perturbation transcription response spectrums without drugs are obtained according to structural semantics of new drugs or expression semantics of new cells in a zero sample scene.

This invention discloses a structure and preparation method for loading targeted therapeutic drugs, relating to the field of targeted drug preparation. The invention includes a base, a battery, a preparation chamber, a conveyor belt, a drug delivery device, an electronic control panel, and a drug structure. The preparation chamber includes heating plates, with several heating plates disposed on one surface inside the preparation chamber body. The conveyor belt includes second nozzles, with one end of several second nozzles connected to the inside of a storage tank. The drug delivery device includes drug storage cylinders, with several drug storage cylinders disposed on the upper surface of the preparation chamber body. The electronic control panel includes a display screen, disposed on another surface of the panel body. The drug structure includes a first drug tube, with one end of the first drug tube connected to the inside of a lower outer shell. This invention provides a structure and preparation method for loading targeted therapeutic drugs, allowing for in vivo drug delivery through the first and second drug tubes; the heating plates accelerate material curing and molding.

A system for predicting the binding affinity of a radiopharmaceutical, according to the present invention, comprises a feature extraction model for extracting a feature related to a radiopharmaceutical connected to a linker-drug structure with respect to a nuclide present in a radioactive decay chain by using chemical properties of an atom and physical properties of radiation related to the radiopharmaceutical as inputs, wherein the physical properties of the radiation include at least any one among the charge amount, mass, energy, and radioisotope decay constant of the radiation.

The invention discloses a drug synergistic effect prediction method based on a biomedical knowledge graph and Transform, and relates to a drug synergistic effect prediction method. The invention aims to solve the problems that the traditional drug combination discovery process mainly depends on clinical tests, time and labor are wasted, the cost is high, and the patient also has risks. The method comprises the steps of extracting a drug data sample, generating a data set, dividing a training set and a test set, and performing network training and testing. Constructing a biomedical knowledge graph; converting the sequence representation of the drug structure into a graph by using Rdkit; mining sub-graphs of the knowledge graph by using the multi-hop sub-graph mining network; using a relation perception Transform to learn the feature representation of the knowledge network and the drug molecular map, and fusing the feature representation of the knowledge network and the feature representation of the drug molecular map; a multi-layer perceptron is used for predicting the synergistic effect between the drug pairs; inputting the drug pairs in the training set into the model; and inputting the drug pairs in the test set into the prediction model to obtain a prediction result. The invention belongs to the technical field of drug synergistic effect prediction.

The application discloses a drug-drug adverse reaction prediction method based on deep learning, which comprises four steps: step one, using a message passing network with an attention mechanism to encode drug structure information; step two, using a similarity function and an automatic encoder to encode drug binding proteins; step three, splicing the different characteristics of the above-mentioned drugs into an adaptive learning module inspired by a meta-path to adaptively learn a meta-path subgraph and perform graph convolution operation to obtain drug characteristics; step four, sending the drug characteristics into a multilayer perceptron to predict drug-drug adverse reactions. The application proposes a method that can effectively capture drug-drug adverse reaction characteristics, solves the problem that common models directly use graph neural networks on interaction networks, and easily mixes noise, and through adaptive learning of effective feature information, the drug-drug adverse reaction prediction effect is improved.

The invention provides a Tunnel and Latch allosteric site specific recognition molecular probe of tyrosine phosphatase SHP2, a preparation method of the Tunnel and Latch allosteric site specific recognition molecular probe and application of the Tunnel and Latch allosteric site specific recognition molecular probe in drug screening. The probe provided by the invention can simply, conveniently, efficiently and accurately determine binding sites of SHP099 type and SHP244 type allosteric inhibitors; understanding of the action mechanism of the medicine is facilitated, the medicine structure is optimized, the medicine effect is improved, and side effects are reduced. Experimental data of the invention show that the developed AlphaScreen activity screening method is stable, reliable and high in sensitivity, and can realize high-throughput screening.

The invention belongs to the technical field of pharmacy, and particularly relates to minoxidil liniment and a preparation method thereof. According to the minoxidil liniment disclosed by the invention, the nano-liposome prepared from the lichen extract-phospholipid complex is used for wrapping minoxidil, the common allergic dermatitis phenomenon in the using process of the minoxidil liniment is avoided by utilizing a synergistic drug loading mechanism of the lichen extract and the phospholipid complex and through anti-inflammatory and anti-oxidation effects, and meanwhile, the drug structure is protected; the action time of the minoxidil liniment is prolonged; the gel substance formed by the polyvinyl alcohol-sodium carboxymethyl cellulose composite coating liquid reduces the surface exposure of minoxidil, avoids the precipitation of minoxidil crystals, and ensures the delivery and absorption efficiency of minoxidil drugs.

The present invention discloses a structural analysis system and a modeling method for proteolysis-targeting chimera complexes, belonging to the field of computer-aided drug molecule design. The structural analysis system includes: a chimera component analysis module, a chimera-induced complex component analysis module, and a linker length distribution analysis module of the chimera to achieve automatic splitting and analysis of the structure of proteolysis-targeting chimera drugs. Based on the component analysis results, through protein-protein docking, conformation search, conformation merging and optimization, conformation clustering, and conformation scoring and ranking, efficient and accurate structure modeling is achieved. The present invention can perform automatic component splitting and structural analysis, so as to comprehensively explore potential drug molecule binding modes, significantly reduce the trial-and-error cost and time of traditional drug structure analysis, significantly improve the design efficiency and success rate of proteolysis-targeting chimeras, and ensure the efficiency and reliability of the structure modeling process through automatic parameter optimization.

The present invention discloses a method and system for predicting drug-target interaction relationships. The method comprises: obtaining three-dimensional drug structure data and three-dimensional target protein data as training sets; constructing a drug-target interaction prediction model; using the training set as input to the drug-target interaction prediction model, establishing a loss function based on the error between the predicted value output by the drug-target interaction prediction model and the true value; and training the drug-target interaction prediction model using a gradient descent method until the model accuracy meets the requirements, thereby obtaining an optimal drug-target interaction prediction model; and predicting the interaction relationship between the drug and the target protein using the optimal drug-target interaction prediction model. The drug-target interaction prediction method and system disclosed by the present invention can effectively extract and utilize spatial features, thereby capturing complex patterns in the data.

Two-layer drug structure (1) comprising the following: an inner core (11) which is spherical in shape, wherein the inner core (11) contains a pharmaceutically active component; an enveloping polymer layer (12) having a thickness wherein the enveloping polymer layer (12) envelops the outer surface of the inner core (11), wherein the enveloping polymer layer (12) shrinks inwards in a gastric acid environment and swells outwards in an intestinal environment; and a multitude of hydrophilic pore-forming particles (13) embedded in the enclosing polymer layer (12), wherein the hydrophilic pore-forming particles (13) are exposed to the outside when the enclosing polymer layer (12) swells and dissolve in the intestinal fluid to form channels.

The present invention relates to the field of drug-target affinity prediction, and in particular to a drug-target affinity prediction method based on a multi-scale hybrid attention network. The present invention utilizes a self-attention mechanism to enable protein pocket residues to learn global residue features, thereby achieving learning of global protein features. Furthermore, a cross-modal feature fusion mechanism is utilized to enhance sequence features of protein and drug structural features. While enhancing intra-entity cross-modal feature fusion, the present invention also utilizes a cross-entity interaction module to identify key atoms in drug molecules or key residues in proteins, thereby improving the overall performance of the model. Finally, the present invention further introduces multi-level protein feature extraction to further extract protein features.