35 results about "Binding drugs" patented technology

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The invention discloses a drug-drug interaction prediction method fusing multi-modal drug information and a static subgraph. The method mainly comprises the following steps: extracting biochemical similarity characteristics of a drug chemical substructure, a target spot and an enzyme; obtaining pharmacological features of the medicine based on a natural language processing model; using graph diffusion convolution to obtain learned node representation from each static sparse sub-graph; embedding of multi-modal feature fusion different drug features; a multi-layer perceptron is used to predict the category of action. According to the drug interaction event prediction method based on the diffusion static subgraph of the multi-modal graph, DDI multi-category prediction is achieved, and compared with an existing algorithm, the method has the following advantages that richer features are provided in combination with multi-modal features such as pharmacology and the like; (2) dividing into a plurality of static sparse sub-graphs by using random edges, learning node representation from each sub-graph, and providing global structure features and local structure features of the DDI network; and (3) richer potential representation is obtained by using graph diffusion convolution.

The invention discloses a spectrogram data management method based on laboratory equipment, and relates to the technical field of medical data management, and the method comprises the steps: constructing a topological graph through a graph theory method, describing the incidence relation between different target drugs, collecting the drug component data of a target drug sample through the laboratory equipment, in combination with a traditional Chinese medicine compatibility theory and a formula standard, edges and weights between different nodes are initialized, the relation between attributes of the nodes and the edges in the topological graph is adjusted by comparing with a medicine component database in an existing pharmaceutical standard, and finally the verified and optimized topological graph is stored as standardized spectrogram data. According to the method, the relationship among the medicine components is visualized through a graph theory method, and the edges and the weights of the edges among the nodes in the topological graph are generated in combination with the attributes and interaction of the medicine components, so that researchers can visually check the action relationship among the components, and the research efficiency is improved. And the problems of information isolation and difficult query in traditional experimental data management are reduced.

The invention relates to the technical field of bioinformatics and artificial intelligence, and provides a drug-target interaction prediction method and system based on multi-modal feature fusion, and the method comprises the steps: carrying out the word segmentation coding of an obtained to-be-recognized drug sequence and a target sequence, and respectively extracting the subsequence features of a drug and a target; constructing a two-dimensional molecular diagram of the drug and a three-dimensional structure diagram of the target, and respectively extracting diagram structure characteristics of the drug and the target; fusing the subsequence features and graph structure features of the drug and the target through a cross attention mechanism; and carrying out interactive fusion by adopting a bidirectional collaborative attention mechanism to obtain a prediction result of the binding affinity of the drug and the target. According to the invention, by combining the multi-modal complementary information of the drug and the target, deep interaction between different modal features is deeply mined; meanwhile, a two-way collaborative attention mechanism is introduced into interaction modeling of the drug and the target, and the accuracy of drug-target binding affinity prediction is effectively improved.

The present invention relates to modular linker compounds for target-binding drug conjugates (TBDCs) and in particular to modular linker compounds for antibody-drug-conjugates (ADCs). The modular linker compounds comprise solubility enhancing groups and self-immolative spacers. The invention further relates to methods of synthesizing the modular linker compound, to target-binding drug conjugates comprising the modular linker compound of the invention as well as to pharmaceutical compositions comprising target-binding drug conjugates compound of the invention. Embodiments of the invention have been particularly developed as target-binding drug conjugates for use in the treatment of cancer and will be described hereinafter with reference to this application. However, it will be appreciated that the invention is not limited to this particular field of use.

The invention relates to a DTA prediction method and model based on multi-scale features. A DTA prediction method based on multi-scale features comprises the following steps: modeling a drug and a target into an undirected graph, and learning through a graph neural network to obtain molecular scale features of the drug and the target; learning the bipartite network of the medicine and the target from a network scale to obtain network scale characteristics of the medicine and the target; and dynamically fusing the molecular scale characteristics and the network scale characteristics of the drugs and the molecular scale characteristics and the network scale characteristics of the targets, and outputting a prediction result. According to the DTA prediction method and model based on the multi-scale features, feature information is captured through a multi-scale method combining molecular structure scales of drugs and targets and interaction network scales, then composite features are obtained through feature adaptive fusion, and the DTA prediction effect is improved.

The invention provides a tolerance evaluation system and method for medicines in an intelligent medicine box, and the method comprises the steps: extracting a medication behavior feature vector and a physiological feature vector from the weight change data of the medicines in the intelligent medicine box and a physiological signal after a target patient takes medicines, and determining the fusion features of the tolerance evaluation of the medicines; recognizing the dependency relationship between the medication event of the target patient and the physiological signal according to the fusion feature and the metabolic half-life period of the drug; combining the drug metabolism data of the target patient with a knowledge graph of drug action in the intelligent drug box to determine drug tolerance feature nodes of the target patient in the drug use process; and determining a drug tolerance tensor when the target patient takes drugs according to the tolerance feature node in combination with the dependency relationship, and generating a drug tolerance evaluation map according to the drug tolerance tensor and the current metabolic data of the target patient. According to the technical scheme provided by the invention, the dynamic association between the medication event of the target patient and the continuous physiological metabolism response in the drug tolerance evaluation can be identified.

The invention relates to the field of vancomycin detection, in particular to a vancomycin sample pretreatment method and a vancomycin sample detection kit preparation method. The invention relates to a vancomycin sample pretreatment method and a detection method of the detection kit. The sample pretreatment step comprises three steps of S1, sample diluent preparation, S2, sample preparation and S3, pretreatment; the preparation method of the vancomycin sample determination kit comprises the following four steps: (1) preparation of a magnetic bead antibody working solution; (2) preparation of an enzyme-labeled antigen working solution; (3) pretreatment of a vancomycin sample; and (4) detection. According to the invention, double-pH interference removal glycopeptide structure protection binding state drug release is integrated into a single reagent for the first time, and high accuracy, specificity and simplicity of serum vancomycin concentration detection are realized.

This invention discloses a method for drug molecule scaffold replacement and screening based on a deep transfer learning model, belonging to the field of computer-aided drug development. The specific steps include: acquiring source and target domain datasets; extracting the molecular scaffolds of corresponding compounds in the source and target domains, as well as the compounds to be screened; pre-training a graph neural network model based on the source domain dataset; inputting the target domain dataset into the pre-trained network model, fine-tuning the network model parameters to obtain new parameters and the model; obtaining small molecule scaffolds based on comprehensive scoring; and replacing the molecular scaffolds to obtain a series of novel molecules. Based on D-MPNN and FNN, this invention introduces a transfer learning approach, combining a dual upstream and downstream strategy of drug target upstream and downstream and transfer learning task upstream and downstream. It extracts and fine-tunes the feature values ​​of the pre-trained network, achieving better training results on small sample datasets, and helping to establish novel molecular scaffold screening and replacement models for upstream and downstream targets with small sample sizes.

The application provides a drug recommendation method and system with continuous training and drug mutual constraint, relates to the technical field of medical health and artificial intelligence, and the method comprises the following steps: acquiring medical data to construct single-disease and multi-disease samples; a first model distribution of diseases and drugs in a single-disease scenario is obtained through self-attention mechanism pre-training; multi-disease samples are continuously iterated as units, a cross-attention mechanism is used to learn the joint influence of multi-disease and surgery on drugs, a drug interaction graph is introduced to calculate a drug conflict rate as a constraint term to construct a loss function to update the model, and a second model distribution is obtained; multi-disease samples are input into the trained model to output drug combination recommendation, and explanatory content is generated in combination with the drug interaction graph; the application learns single-disease correlation through pre-training, and introduces cross-attention and drug conflict rate constraint in continuous training, so that more accurate and safe drug combination recommendation in a multi-disease coexistence scenario is realized.

To provide a combination of reagents that is low in toxicity and safe, and a method of detecting targeted immunosuppressants in whole blood.SOLUTION: The present invention relates to the field of in vitro detection technology, specifically to a method of detecting targeted immunosuppressants in whole blood without centrifugation by a combination of reagents. The present invention is to provide a safe combination of reagents with low toxicity and a method of detecting targeted immunosuppressants in whole blood; the method according to the present invention can be used for performing immunodetection, and the detection results can be highly consistent with mass spectrometry analytical detection results, which shows that the whole blood treatment agent of the present invention can be used for treating a specimen, and bound medicines can be fully released, and the obtained medicines in free state can meet the requirements of immunodetection. The detection method provided by the present invention truly realizes detection without centrifugation and greatly improves detection efficiency.SELECTED DRAWING: None

The present disclosure relates to modular linker compounds for target-binding drug conjugates (TBDCs) and in particular to modular linker compounds for antibody-drug-conjugates (ADCs). The present disclosure further relates to methods of synthesizing the modular linker compound, to target-binding drug conjugates comprising the modular linker compound of the present disclosure as well as to pharmaceutical compositions comprising target-binding drug conjugates compound of the present disclosure. Embodiments of the present disclosure have been particularly developed as target-binding drug conjugates for use in the treatment of cancer and will be described hereinafter with reference to this application. However, it will be appreciated that the present disclosure is not limited to this particular field of use.

The present invention provides a fungal drug carrier comprising an avirulent fungal pathogen cell, for example a Cryptococcus neoformans cell, linked to one or more surface-bound drug-loaded nanoparticles, and methods of making the fungal drug carrier. The invention provides methods of using the fungal drug carrier to deliver a drug to the central nervous system of a patient, wherein the fungal drug carrier is phagocytosed by an immune cell of a patient, transported across the blood-brain barrier, and vomocytosed to allow drug release from the surface-bound nanoparticle into the central nervous system of the patient. The methods involve treating a central nervous system in a patient comprising administering to the patient a pharmaceutical composition comprising a fungal drug carrier.

A conjugate drug formulation in the biopharmaceutical field and its manufacturing method and use. The formulation contains a ligand-drug conjugate, a pH adjuster and a freeze-drying excipient. The pH screening is performed on the above-mentioned formulation containing the ligand-drug conjugate, and the pH adjuster and freeze-drying excipient used are limited. At the same time, the administration order and the liquid distribution environment during drug production are considered, and the freeze-drying process parameters are screened, thereby finally achieving the effect of keeping the properties, water content, impurity content, pH value, etc. of the formulation stable during the storage period.

This invention relates to the fields of bioinformatics and artificial intelligence, and proposes a method and system for predicting drug-target interactions based on multimodal feature fusion. The method includes: segmenting and encoding the acquired drug and target sequences to be identified; extracting sub-sequence features of the drug and target respectively; constructing a two-dimensional molecular graph of the drug and a three-dimensional structural graph of the target, and extracting graph structural features of the drug and target respectively; fusing the sub-sequence features and graph structural features of the drug and target through a cross-attention mechanism; and using a bidirectional collaborative attention mechanism for interactive fusion to obtain the predicted binding affinity of the drug and target. This disclosure, by combining multimodal complementary information of the drug and target, deeply explores the deep interactions between different modal features; simultaneously, by introducing a bidirectional collaborative attention mechanism in the interaction modeling of the drug and target, it effectively improves the accuracy of drug-target binding affinity prediction.

The application discloses a DNA binding small molecule high-throughput screening and characterization method, adopts a small molecule binding agent to program a toehold-mediated strand displacement (TMSD); performs one-stop comprehensive characterization of the interaction between the small molecule binding agent and double-stranded DNA, including dissociation constant, binding site size, thermodynamic parameters and sequence selectivity; performs high-throughput screening (HTS) identification of the small molecule binding agent by using a series of induced nucleic acid displacement BIND reaction system; and combines experimental measurement and computer simulation, and it is shown that the small molecule double-stranded DNA binding agent can be used in the reaction path of programming toehold-mediated strand displacement (TMSD), so that the molecular tool of comprehensive thermodynamic characterization can be performed by using the binding agent induced nucleic acid displacement (BIND) technology, and the high-throughput screening (HTS) of the small molecule double-stranded DNA binding agent can be performed without any special equipment, the application is widely used as a one-stop HTS and comprehensive characterization platform, so as to accelerate the pace of discovering novel small molecule double-stranded DNA binding drugs / tracers.

The present invention relates to the field of pharmaceutical knowledge graph technology, specifically a method and system for constructing a pharmaceutical knowledge graph based on artificial intelligence, comprising the following steps: collecting the molecular structure of a drug and its corresponding target protein sequence through database query, numerically encoding the molecular structure data of the drug, extracting molecular fingerprints and protein domain features, and combining the drug's chemical properties with protein sequence characteristics to form a drug and protein feature set. In the present invention, through precise analysis of the drug's molecular structure and its target protein sequence, the innovative solution significantly enhances the understanding of the interaction between drugs and proteins, allowing researchers to directly extract key features from the data and monitor the dynamic changes in drug effects. This not only accelerates the drug development process and optimizes treatment plans, but also provides strong data support for personalized medicine by dynamically tracking the interaction between drug side effects and pathological characteristics.

The invention discloses an iron-based nano composite material and a preparation method and application thereof. The invention relates to an iron-based nano composite material which is obtained by taking an iron-based metal organic framework material as a carrier, loading a medicine 1, wrapping the surface of the iron-based metal organic framework material with polydopamine and combining with a medicine 2 through a borate bond, the iron-based metal organic framework material is MIL-88, the medicine 1 is beta-lapamoquinone, and the medicine 2 is chlorambucil. The preparation method comprises the following steps: S1, preparing MIL-88; s2, preparing MIL-88-beta-lap (MIL-88-beta-lap) S3, MIL-88-beta-lap (PDA) is prepared, and MIL-88-beta- S4, cb-BA is prepared; s5, MIL-88-beta-lap (PDA)-cb is prepared, and the PDA-cb is prepared; the iron-based nano composite material is used as a drug carrier to be applied to tumor treatment. According to the invention, combined treatment of ferroptosis, photo-thermal treatment and chemotherapy can be realized.

The invention discloses screening and application of an esophageal squamous carcinoma cell aptamer, and belongs to the technical field of nucleic acid. The technical problem to be solved in the invention is how to obtain the aptamer specifically targeting esophageal squamous carcinoma cells and to prepare a reagent for identifying and diagnosing esophageal squamous carcinoma or esophageal squamous carcinoma cells. In order to solve the technical problem, the invention provides a nucleic acid aptamer ESCwl-7a, ESCwl-68a or ESCwl-75a and a composition composed of the nucleic acid aptamer ESCwl-75a, ESCwl-68a or ESCwl-75a. The nucleic acid aptamer obtained through screening has the advantages of being low in immunogenicity, easy to modify and mark, stable in property and good in reproducibility; compared with an antibody, the stability is good, and the synthesis cost is low. And further characterization and target identification can be carried out on the properties of the nucleic acid aptamer subsequently, so that the exploration and discovery of tumor markers are facilitated. Meanwhile, the aptamer can be used as a high-efficiency molecular probe tool with strong penetrating power for early diagnosis of tumors, and also can be combined with drugs for tumor targeted therapy.

The present invention relates to modular linker compounds for target-binding drug conjugates (TBDCs) and in particular to modular linker compounds for antibody-drug-conjugates (ADCs). The modular linker compounds comprise solubility enhancing groups and self-immolative spacers. The invention further relates to methods of synthesizing the modular linker compound, to target-binding drug conjugates comprising the modular linker compound of the invention as well as to pharmaceutical compositions comprising target-binding drug conjugates compound of the invention. Embodiments of the invention have been particularly developed as target-binding drug conjugates for use in the treatment of cancer and will be described hereinafter with reference to this application. However, it will be appreciated that the invention is not limited to this particular field of use.

The application provides an individualized medication method, device, computing equipment and storage medium, wherein the method comprises: collecting personal information of a patient and collecting gene detection data of the patient; prioritizing gene sites contained in the gene detection data, and generating primary medication information based on a prioritization result and a pre-constructed gene-drug guideline database; constructing a blood drug concentration-time curve based on the personal information and the primary medication information; obtaining precise medication information based on the blood drug concentration-time curve and the primary medication information; collecting actual medication information of the patient, adjusting the precise medication information based on the actual medication information, and obtaining target medication information. The application realizes a precision medicine mode of customizing an exclusive medication scheme for a patient based on individual characteristics of the patient, combining a drug-gene correlation rule and a clinical guideline, and achieving one person, one drug and one dose, so that the efficacy is improved and the risk of adverse reactions is reduced.

The present disclosure relates to the field of pharmacy and medicine, in particular to a method for constructing a drug hepatotoxicity prediction model and its application in predicting drug hepatotoxicity, further relates to a method, system and device for predicting drug hepatotoxicity, and further relates to the use of a combination of cell phenotype parameters in predicting drug hepatotoxicity. The method for predicting drug hepatotoxicity provided by the present disclosure can identify the toxicity of a drug to be tested on hepatocytes by performing high-content analysis test experiments on hepatocytes in at least 3 groups (7 parameters) and at most 6 groups (13 parameters), combined with the drug human body exposure C max value, i.e. the toxicity of the drug to be tested on hepatocytes can be identified, and the accuracy of the method can be up to 87%, and the highest sensitivity and specificity are 84% and 94% respectively, which are significantly higher than the reported prediction methods based on HCA or other technologies.

A lipid-binding drug for binding to a membrane-based or membrane lipid comprises at least one aptamer having a sequence selected from the group consisting of GGCGGCAAAAAAAAAA (SEQ ID NO: 1), GGCGGCAAAAAAAAAAA (SEQ ID NO: 2), GGCGGCAAAAAAAAAAAA (SEQ ID NO: 3), GGCGGCAAAAAAAAAAAAA (SEQ ID NO: 4), GGCGGCGGGGGGGGGG (SEQ ID NO: 5), GGCGGCGGGGGGGGGGG (SEQ ID NO: 6), GGCGGCGGGGGGGGGGG (SEQ ID NO: 7), GGCGGCGGGGGGGGGGG (SEQ ID NO: 8), CAAAAGGAGCAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO: 9) CAAAAGGAGCAAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO: 10), CAAAAGGAGCAAAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO: 11), CAAAAGGAGCAAAAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO: 12), CAAAAGGAGCGGGGGGGGGGGGGGGGGGGGGGG (SEQ ID NO: 13), CAAAAGGAGCGGGGGGGGGGGGGGGGGGGGGGGG (SEQ ID NO: 14), CAAAAGGAGCGGGGGGGGGGGGGGGGGGGGGGGGG (SEQ ID NO: 15), and CAAAAGGAGCGGGGGGGGGGGGGGGGGGGGGGGGGG (SEQ ID NO: 16).

The invention relates to the field of albumin binding type drugs, in particular to a cyclosporin A entrapped albumin binding type drug as well as a preparation method and application thereof. The albumin binding type medicine comprises cyclosporin A or a derivative thereof and albumin, the mass ratio of the cyclosporin A or the derivative thereof to the albumin is 1: (1-10), and albumin molecules are connected through disulfide bonds formed by self-crosslinking of free sulfydryl of the albumin. The albumin combined medicine disclosed by the invention shows a remarkable curative effect in the aspect of treating xerophthalmia, and can be used for remarkably improving tear secretion and quality, effectively repairing corneal injury, relieving eye inflammation and promoting overall recovery of eye tissues. According to the albumin binding type medicine, the retention time of the medicine on the ocular surface can be remarkably prolonged, the local medicine concentration is improved, the eye tissue recovery is comprehensively promoted while symptoms are rapidly relieved, and good safety and potential clinical application value are shown.

Disclosed herein are systems and methods for releasing controlled drug doses to cells within fluidic channels. In certain aspects, stimuli such are selectively provided to drug-containing substrates or surface-bound drug libraries to release one or more drugs in time, location, and dose-specific manners.

The present invention relates to the field of biomedicine technology, and in particular to an emulsion gel microbead, and a preparation method and application thereof. Specifically, the process comprises the following steps: (1) mixing a sodium alginate solution modified with dodecylsuccinic anhydride and fish oil to obtain a DSA emulsion; (2) mixing the DSA emulsion, the sodium alginate solution and glycolithocholic acid to obtain a DSA / SA emulsion; (3) mixing the DSA / SA emulsion, a surfactant solution and an emulsifier solution to obtain an O / W / O emulsion; (4) dripping a calcium chloride solution into the O / W / O emulsion to obtain an emulsion gel; (5) mixing the emulsion gel with water, stirring, and standing, and removing impurities to obtain emulsion gel microbeads. The emulsion gel microbeads prepared by the present invention can effectively bind drug ingredients, have good biocompatibility after injection, and can achieve a drug release process in which the drug is slowly released and uniformly released.

The present invention relates to modular linker compounds for target binding drug conjugates (TBDCs), and in particular to modular linker compounds for antibody-drug conjugates (ADCs). The modular linker compounds comprise a solubilizing group and a self-immolative spacer. The present invention further relates to methods of synthesizing the modular linker compounds, to target binding drug conjugates comprising the modular linker compounds of the present invention, and to pharmaceutical compositions comprising the target binding drug conjugate compounds of the present invention. Embodiments of the present invention are specifically developed for target binding drug conjugates for use in cancer therapy, and will be described hereinafter with reference to the present application. However, it will be appreciated that the present invention is not limited to this particular field of use.

The present invention relates to a medical implant comprising in its interior a device for electrically controllable ad- and desorption of molecules, said device comprising (a) two electrodes comprising a conductive surface, (b) a stationary phase between said two electrodes, wherein said stationary phase is not in direct contact with said electrodes, (c) a controller functionally associated with a power supply configured for applying and changing a voltage between said electrodes, wherein changing the voltage between said electrodes changes the affinity of molecules so as to adsorb said molecules to said stationary phase or desorb molecules from said stationary phase when said molecules are adsorbed to said stationary phase, and the use of said medical implant in the treatment of a medical condition.