70 results about "Drug structure" patented technology

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.

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 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.

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 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.

This invention discloses a high-boron-loading manganese-based boron drug for BNCT diagnosis and treatment, its preparation, and its application, belonging to the fields of biomedicine and nuclear medicine. The boron drug structure is shown in general formula 7, with manganese porphyrin as the core skeleton. A benzene ring side chain at the meso position of the porphyrin ring is connected to a boron-10-containing catechol borate ester group via an amide bond. All boron atoms are boron-10 isotopes. The preparation method includes: first constructing the manganese porphyrin skeleton through condensation, cyclization, and metal coordination reactions; then independently preparing boron source units; and finally obtaining the target product through amide bond coupling. This boron drug possesses high boron-10 loading, efficient blood-brain barrier penetration, and excellent MRI imaging performance, enabling real-time and precise monitoring of boron drug distribution in vivo. The quantitative correlation between imaging signals and boron concentration guides the timing of neutron irradiation, integrating MRI tracing and BNCT treatment, effectively solving the problems of low boron loading, inability to penetrate the blood-brain barrier, and difficulty in real-time monitoring of in vivo distribution in existing boron drugs.

The structural general formula of the aaptamine derivative is selected from one of the following structures: an efficient chemical synthesis route of the aaptamine derivative is established, diversified structural modification is realized on the basis of the chemical synthesis route, and a large number of aaptamine derivatives which are not reported before are obtained. The compounds not only provide a rich molecular basis for systematic research on the drug structure-function relationship of aaptamine, but also find that the compound Ap-a48 can induce cell apoptosis by inhibiting highly dependent signal channels of two tumor cells, namely PI3K-AKT and ATR-CHK1, and show remarkable treatment potential for acute myelogenous leukemia.

The application discloses a kind of nano-drugs with low oxygen response and photoisomerization function and preparation and application, belong to the field of biomedicine technology.The present application uses 4,4'-dichloro azobenzene as low oxygen response unit, which is covalently linked with 10-hydroxy camptothecin and drug carrier mPEG to form size-controllable nano-drugs, which have suitable particle size, more conducive to the enrichment of drug circulation in tumor site and play curative effect, when the nano-drugs reach the low-oxygen tumor site, the azo reductase unique to low-oxygen environment will reduce the azo group inside the nano-drugs, resulting in the disintegration of the nano-drug structure, thereby releasing 10-hydroxy camptothecin to play the efficacy of chemotherapy, and reduce the toxic and side effects of drug on normal tissues;In addition, the nano-drug has high utilization rate and good biocompatibility, so it has good application prospect in the preparation of antitumor drugs.

The present invention discloses an amino acid derivative containing a non-steroidal anti-inflammatory drug structure and a preparation method and application thereof. The structural formula of the derivative is as represented by formula I. The novel compound has a broad-spectrum anti-tumor effect, can prolong the survival period of tumor patients, and improve the quality of life of tumor patients.

The present application belongs to the technical field of biomedical functional materials, and particularly relates to a multifunctional drug structure coating with regulation of lesion microenvironment homeostasis and a preparation method thereof. The nano drug carrier can load drugs with anti-smooth muscle cell growth or regulation of inflammation, the drugs can resist the growth of smooth muscle cells or regulate microenvironment inflammation, and provide a stable and safe microenvironment for the adhesion and growth of endothelial cells. Under the action of an oxidizing agent, polyphenol and polyamine compounds undergo oxidation, crosslinking and polymerization reactions, and a polyphenol nanoparticle (10 nanometer level) film layer is generated outside the nano drug carrier (100 nanometer level). The polyphenol nanoparticle film layer not only serves as a protective layer for the nano drug carrier and crosslinking substances therebetween, but also can fix the nano drug carrier on the surface of a base material, and together with the drug carrier, a drug structure coating with a topological structure is constructed. The hydrophilic groups contained in the coating make the surface super-hydrophilic and negatively charged.

AI based medical equipment for docking and Structure analysis of drug

The invention discloses a drug target affinity prediction method and system based on three-dimensional molecule fragmentation. The method comprises the following steps: obtaining a drug molecular map structure to be predicted and a protein molecular map structure to be predicted; encoding the drug molecular map structure to obtain a drug structure feature vector; encoding the protein molecular map structure to obtain a protein structure feature vector; performing fragment feature extraction on the drug molecular map structure to obtain a drug fragmentation structure feature vector; and splicing the drug structure feature vector, the protein structure feature vector and the drug fragmentation structure feature vector into a fusion feature vector, and inputting the fusion feature vector into a pre-trained drug target affinity prediction model to obtain a drug target affinity prediction result.

The application relates to the technical field of deep learning, and is an application of deep learning technology in drug research and development, in particular to a single-cell drug sensitivity prediction method fusing cross attention and domain self-adaption, which comprises the following steps: data preprocessing, a self-encoder, cross attention, domain self-adaption and single-cell drug sensitivity prediction.The application first applies domain self-adaption based on cross attention to the problem of predicting single-cell drug sensitivity, and combines gene expression data and drug structure data to construct a model, and has achieved a performance of 0.85 AUC in predicting single-cell drug sensitivity.

The application discloses an antibody conjugated drug binding prediction method based on a virtual graph and multi-scale features, proposes a drug carrier, a linker, an antibody heavy chain and a light chain, and an antigen target protein initial feature construction method, proposes a 1DCNN for extracting drug sequence features, designs a drug feature extraction method based on a graph virtual node, introduces a virtual node and a virtual edge into a molecular structure graph, takes a Graph Transformer as a graph feature extractor, takes a virtual node feature as a drug representation, then inputs protein and drug features into a feature fusion module, adds an attention mechanism and a gated skip connection mechanism in feature fusion, captures potential interactions while fusing feature information of different hidden layers, and realizes higher precision affinity prediction. The application can solve the technical problems that existing methods are difficult to extract structural features of antibody conjugated drugs and features of different components are difficult to fuse.

The application belongs to the technical field of pharmacy, and particularly relates to a minoxidil liniment and a preparation method thereof. The application wraps minoxidil by using a lichen extract-phospholipid complex to prepare nano-liposomes, utilizes the synergistic drug loading mechanism of the lichen extract and the phospholipid complex, avoids the common allergic dermatitis phenomenon in the use process of the minoxidil liniment through anti-inflammatory and antioxidant effects, protects the drug structure, and prolongs the action time of the minoxidil liniment; the gel-like substance formed by using a polyvinyl alcohol-carboxymethylcellulose sodium complex coating solution reduces the surface exposure of minoxidil, avoids the precipitation of minoxidil crystals, and guarantees the delivery and absorption efficiency of minoxidil drugs.

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. Systems and methods described herein relate to DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states on cellular subsystems which are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to design of synergistic drug combinations, which can be validate systematically. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

The application discloses a drug target affinity prediction method and system based on three-dimensional molecular fragmentation, comprising the following steps: obtaining a drug molecule graph structure to be predicted and a protein molecule graph structure to be predicted; encoding the drug molecule graph structure to obtain a drug structure feature vector; encoding the protein molecule graph structure to obtain a protein structure feature vector; performing fragment feature extraction on the drug molecule graph structure to obtain a drug fragmented structure feature vector; splicing the drug structure feature vector, the protein structure feature vector and the drug fragmented structure feature vector into a fusion feature vector, inputting the fusion feature vector into a pre-trained drug target affinity prediction model, and obtaining a drug target affinity prediction result.

The application discloses a method for predicting a G protein-coupled receptor protein three-dimensional folding and a drug molecule binding model. The method comprises the following steps: selecting a plurality of target GPCR initial models by using three-dimensional sequence alignment and template three-dimensional structure selection, and optimizing a flexible area of the GPCR initial model and a drug structure site to obtain a target GPCR model for molecular docking and drug design; and selecting an optimal GPCR and drug molecule binding mode by cross-verification of prediction performances of a plurality of types of GPCR-drug molecule artificial intelligence models. The application improves the prediction accuracy of the G protein-coupled receptor protein three-dimensional folding and the GPCR drug molecule binding mode of a drug target, and can also accurately capture the interaction mode of the related drug molecule and the GPCR target.