867 results about "Target drug" patented technology

The invention relates to a dialkyl imidazole bionic lipid compound and a preparation method and application thereof.The structure of the dialkyl imidazole bionic lipid compound imitates natural phospholipid design, alkyl with no less than 10 carbon atoms is modified on the fourth site and the fifth site of imidazole, and primary amines with different alkyl chain lengths are modified on the second site; the dialkyl imidazole bionic lipid compound is mixed with a therapeutic drug and other auxiliary materials to prepare lipid nanoparticles loaded with the therapeutic drug, and the lipid nanoparticles can be used as a drug delivery system for preparation of brain-targeted drugs. The dialkyl imidazole bionic lipid has the function of dynamically regulating and controlling the blood-brain barrier, and can realize reversible opening of the blood-brain barrier; the formed drug-loaded lipid composition can pass through a blood brain barrier and well realize brain-targeted delivery of loaded therapeutic drugs; the drug-loaded lipid composition composed of the dialkyl imidazole bionic lipid belongs to a new generation of bionic nano-drug carriers, and provides a new strategy for realizing brain-targeted delivery of different types of drugs.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

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.

The present application relates to a kind of dihydrocarbylimidazole biomimetic lipid compound and its preparation method and application, dihydrocarbylimidazole biomimetic lipid compound structure imitates natural phospholipid design, modification is not less than 10 carbon atoms alkyl on the 4th and 5th of imidazole, and modification is different alkyl chain length primary amine on 2nd position;Dihydrocarbylimidazole biomimetic lipid compound is mixed with therapeutic drug and other adjuvant, can be prepared to be loaded in the lipid nanoparticle of therapeutic drug, can be used as drug delivery system for the preparation of brain-targeted drug.Dihydrocarbylimidazole biomimetic lipid has the function of dynamic control blood-brain barrier, can realize the reversible opening of blood-brain barrier;Formed drug-loaded lipid composition can cross blood-brain barrier and better realize the brain-targeted delivery of loaded therapeutic drug;Drug-loaded lipid composition consisting of dihydrocarbylimidazole biomimetic lipid belongs to a new generation of biomimetic nanomedicine carrier, provides new strategy for realizing the brain-targeted delivery of different kinds of drugs.

The invention discloses a tumor cholesterol metabolism regulation microneedle patch and a preparation method, the microneedle patch comprises a needle tip and a backing which are connected, the needle tip comprises a needle tip body and nanoparticles loaded on the needle tip body, the nanoparticle is a manganese ion-doped organic metal framework, the outer layer of the manganese ion-doped organic metal framework is modified with a targeting agent, cholesterol oxidase and superoxide dismutase are carried on the manganese ion-doped organic metal framework, the targeting agent can target a CD44 receptor, and the organic metal framework can carry drugs. The targeted drug can enter tumor cells in a targeted mode, superoxide dismutase catalyzes superoxide anions overexpressed in the tumor cells to generate H2O2 and O2, O2 needed by catalysis is provided for cholesterol oxidase, cholesterol at the tumor site is consumed by the cholesterol oxidase, accumulation of 7-DHC is reduced, meanwhile H2O2 can be generated, and the cholesterol oxidase can be used for catalyzing the tumor site. H2O2 is catalyzed by manganese ions through a Fenton-like reaction to generate OH to induce tumor cells to generate ferroptosis, so that ferroptosis-immune synergistic treatment is realized.

According to the GPX4 protein degradation agent and the application, the degradation agent serves as molecular glue to induce tumor cell ferroptosis and is different from an existing PROTAC technology, and the molecular glue can induce interaction between E3 ubiquitin ligase and target protein GPX4 and promote ubiquitination of the E3 ubiquitin ligase and the target protein GPX4. Compared with the existing GPX4 degradation agent, the molecular glue has the advantages of small molecular weight, high cell permeability and better druggability. The GPX4 molecular glue disclosed by the invention can be used for effectively degrading GPX4 and has a killing effect on various tumor cell lines. The preparation method is simple in synthesis route and mild in reaction condition, can be used for being developed into a new generation of GPX4 targeting drugs, and has great clinical application value and considerable market potential.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

The invention relates to the field of molecular targeted therapy, and discloses an application of an SLC16A5 (MCT6) small-molecule inhibitor MCT6-Ai7-2 in preparation of a medicine for treating acute myeloid leukemia (AML). The inhibitor takes an SLC16A5 protein structure predicted by Alphafold as a target spot, and is obtained through compound database screening, molecular docking and druggability optimization. An in-vitro experiment proves that MCT6-Ai7-2 can remarkably inhibit proliferation of AML cell lines such as U937 and MOLM-13, induce cell apoptosis and retard a cell cycle, has an inhibiting effect on a primary AML patient specimen and is relatively low in toxicity to normal cells; in-vivo experiments show that the compound is effective and has good safety in AML model mice. In addition, the MCT6-Ai7-2 and the vinca can be combined to synergistically enhance the inhibition effect on vinca drug-resistant cells, and by reducing the expression of anti-apoptotic protein MCL-1, the activation of pro-apoptotic factors BIM and tBID is promoted to play a role. The invention provides a novel targeting drug and strategy for treatment of AML (especially drug-resistant or recurrent patients).

The invention discloses a data management system and method based on a large model, and relates to the technical field of big data analysis, and the method comprises the steps: setting a data monitoring management period, enabling the system to collect the stock change information of various types of medicines in a hospital, analyzing the historical warehouse-out data of a department in combination with a time sequence model, and precisely measuring and calculating a first warehouse-out predicted value; and comparing the daily average dose with a standard limited daily dose, deeply mining compatible drug data associated data in an auxiliary drug use scene, analyzing a compatible drug data ex-warehouse trend, and accurately mastering a collaborative consumption rule. By tracking ex-warehouse proportion changes of target drug data and compatible drug data in a historical period, predicting a proportion trend, predicting ex-warehouse quantity in combination with the compatible drug data, dynamically calculating an auxiliary drug use demand, and deeply deconstructing inventory change data, prospective pre-judgment of the inventory change data is realized, hospitals are assisted to optimize an inventory structure, and the workload of the hospitals is reduced. And the accuracy and response efficiency of inventory data management are improved.

The invention relates to the technical field of targeted cargo delivery, in particular to a drug loading and delivery method and a cell carrying method of a core-shell mesoporous micro-nano robot. The drug loading and delivery method is realized based on the drug loading capacity of the mesoporous structure, the magnetism of the iron oxide core and the photothermal effect of the polydopamine shell. The core-shell mesoporous micro-nano robot can adsorb and load drugs through capillary force and electrostatic force generated by the mesoporous structure of the core-shell mesoporous micro-nano robot; then, an external magnetic field is used for driving the device to move; near-infrared light is utilized to irradiate when approaching a targeted area, the temperature of a pore structure rises, and along with thermal vibration, drugs in mesopores are effectively released to the targeted area, so that the drug delivery effect is achieved. When the drug is loaded and delivered, the core-shell mesoporous micro-nano robot has carrying capacity after being clustered under a magnetic field, and cells are carried through an eddy current carrying method and a contact adhesion method. Targeted goods delivery of goods such as targeted drugs and cells can be achieved through the micro-nano robot.

The invention provides a pathogen interpretation method and device and a medium, and the method comprises the steps: carrying out the data preprocessing of received initial sequencing data, and obtaining first sequence data; comparing the first sequence data with a reference genome sequence in a preset pathogen database to obtain a comparison result; according to a comparison result, obtaining a read parameter set of the initial sequencing data; performing pathogen interpretation processing on the read parameter set to obtain a target pathogen type corresponding to the initial sequencing data; performing drug-resistant gene detection on the target pathogen type, and judging whether a target drug-resistant gene corresponding to the target pathogen exists or not; if the target drug-resistant gene exists, analyzing the target drug-resistant gene to obtain drug-resistant data; and generating a target report according to the target pathogen type, the target drug-resistant gene and the drug-resistant data. According to the invention, the type of the infected pathogen can be rapidly and accurately interpreted, and the drug resistance condition of the infected pathogen can be accurately analyzed, so as to assist clinicians in formulating a reasonable target report.

The invention relates to an application of an intervention SNRK-MTA1 signal channel axis in preparation of a non-small cell lung cancer targeted therapy drug. The nucleotide sequences of the mRNA of the SNRK gene and the mRNA of the MTA1 gene are respectively as shown in SEQ ID NO.1-2. The invention innovatively provides a strategy for treating the non-small cell lung cancer through double-target combined intervention. According to the strategy, SNRK gene expression is improved through exogenous gene overexpression plasmids, and meanwhile MTA1 gene expression is silenced through the siRNA technology. In a non-small cell lung cancer model, the strategy of combined application of the SNRK-OE plasmid and siMTA1 can specifically up-regulate the SNRK mRNA level and knock down the MTA1 mRNA level at the same time, and the combined strategy shows a better anti-tumor effect than single intervention, and can more effectively inhibit the growth and migration of tumor cells. Based on the discovery, the SNRK-MTA1 signal pathway axis can be developed into a novel therapeutic target for non-small cell lung cancer, and is used for designing a drug combination scheme or a composite targeted drug.

The invention belongs to the technical field of biological medicines, and particularly relates to an iron-based nano material, a preparation method and application of the iron-based nano material in preparation of medicines for treating breast cancer. The iron-based nano material is formed by loading a ferroptosis inducer on a nano carrier; the nano carrier is an iron-based metal organic framework material Fe-MOF (Metal Organic Framework). A product FEH for treating breast cancer is obtained based on coupling of the iron-based nano-material provided by the invention with a targeted drug such as trastuzumab. The FEH provides a new method for developing a nano platform with an antibody function, and realizes a treatment strategy of trastuzumab and ferroptosis combined anti-tumor. As a safe and effective tumor treatment strategy, the EFH has huge clinical transformation potential.

The invention discloses a multi-dimensional efficacy evaluation system for colon cancer targeted drug screening, and relates to the technical field of medical assistance. Comprising a data acquisition module, an inference engine module and an output module. The data acquisition module acquires an organ sample response data set (including apoptosis rate A1, activity decline degree A2 and image structure change score B1) and a patient background data set (including gene mutation discrete variable set G and metabolic pathway continuous variable set M). The inference engine module executes dominant condition rules: generating a negative weight factor alpha according to a gene mutation state and a metabolic activation threshold value; generating a deduction item gamma based on a preset drug-resistant mutation subset; performing weighted calculation on the apoptosis rate and the activity decline degree to generate a basic drug effect score beta, and increasing beta through a critical value; and finally generating a comprehensive response score S. And the output module outputs a drug effect judgment result according to the S, and multi-dimensional accurate evaluation is realized.

The invention relates to the technical field of biology, and particularly provides a targeting carrier, a targeting drug, a preparation method and application. The targeting carrier comprises (a) a metal-polyphenol compound particle and (b) a targeting structure, and the targeting structure is connected with the outer surface of the metal-polyphenol compound particle. Under the condition that the effectiveness of the LNP based on the cationic lipid and / or the ionizable lipid is not lower than that of the LNP based on the cationic lipid and / or the ionizable lipid, the targeting carrier provided by the invention does not use the cationic lipid and the ionizable lipid, so that the toxicity is greatly reduced, the biological safety is remarkably improved, negative charge drugs are more favorably carried in a living body, the application range is wide, and the targeting carrier can be used for drugs with different sizes. The targeted drug can achieve high expression quantity of nucleic acid drugs, the biological safety is remarkably improved, the targeting property is good, and efficient treatment of various diseases can be achieved.

Theranostic targeted ultrasonic blasting nano-bubbles, a preparation method therefor, and use thereof, wherein the nano-bubbles are the targeted drug-gene nano-bubbles miRNA / FTY720 / PFP@PEI-T7 NBs. The nano-bubbles can be used for preparing a thyroid ultrasound diagnostic agent or a drug for treating thyroid cancer.

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 current application reveals a method, apparatus, device, and storage medium for detecting micro residual lesions, falling within the domain of medical detection technology. This method is based on differentiated deep whole-exome / targeted drug sequencing and tissue-blood cell-plasma co-capture technology, and 100,000× ultra-high depth personalized / high evidence hotspot combination panel sequencing to evaluate tiny residual lesions and tumor evolution / second primary in plasma samples. It resolves the challenges of existing techniques, such as elevated tissue detection thresholds, restricted tracking locations, inadequate detection sensitivity and precision, or elevated costs when ctDNA concentrations in the bloodstream are minimal. Furthermore, it surmounts the challenge of simultaneously achieving personalized tracking detection and monitoring tumor evolution or second / primary detection. It markedly boosts the precision of forecasting the likelihood of recurrence following patient therapy within a restricted budget.

The invention discloses a multi-target drug molecule generation model construction method and a multi-target drug design method, and relates to computer drug design and bioinformatics. Determining a corresponding two-dimensional molecular map based on the SMILES sequence; the fully-connected pharmacophore diagram and the two-dimensional molecular diagram are used as input of a GatedGCN module, each atomic node in the two-dimensional molecular diagram is connected with all pharmacophore nodes in the fully-connected pharmacophore diagram in message transmission, and information exchange between different nodes is achieved; the masked SMILES sequence serves as the input of an encoder, and the decoder generates an SMILES sequence conforming to pharmacophore characteristics in an autoregression mode; freezing the network parameters of the GatedGCN module and the encoder; and training the multi-target drug molecule generation model through the elite multi-target molecule set, and reversely optimizing and updating decoder network parameters according to an output result. According to the method, the model fully learns the multi-target molecular structure characteristics, and the multi-target drug molecule generation accuracy is improved.

The invention relates to the technical field of preparation of biological materials and pharmaceutical preparations, in particular to organic composite hydrogel as well as a preparation method and application thereof. The specific preparation method comprises the following steps: dissolving a targeted drug in orthoester to obtain a targeted drug-orthoester solution; the preparation method comprises the following steps: mixing an oxidized dextran solution, a targeted drug-orthoester solution and a carboxymethyl chitosan solution, carrying out vortex treatment, and standing to prepare the organic composite hydrogel. The solubility of the targeted drug in the hydrogel is improved by utilizing orthoester, the orthoester can carry the targeted drug to efficiently escape from the hydrogel, and the targeted drug can be self-assembled to form nanoparticles along with degradation of the orthoester in the escape process, so that the retention capacity of the targeted drug at a tumor site is enhanced, and the targeting drug can be used for treating tumor tumors. The drug enrichment capacity is improved, meanwhile, the toxic and side effects of the whole body are reduced, and the problems that nano-drugs are poor in solubility, still prone to being removed after intravenous administration and poor in enrichment effect at tumor sites are solved.

The invention belongs to the technical field of biomedicine, and particularly relates to application of GPR161 as an acute distress syndrome diagnostic marker and a therapeutic target. Experiments prove that the expression level of GPR161 in peripheral blood mononuclear cells of a patient with the acute respiratory distress syndrome is remarkably increased, and the expression quantity of the GPR161 is positively correlated with the severity of the disease, so that the GPR161 gene or GPR161 protein can be used as a molecular marker for screening or diagnosis or prognosis evaluation of the acute respiratory distress syndrome; a reagent for inhibiting GPR161 gene expression or protein activity can be used for preparing a medicine for treating the acute respiratory distress syndrome. The invention provides a new strategy for diagnosis, monitoring and targeted drug development of ARDS, and has important clinical application value.

The invention belongs to the technical field of biological medicines, and discloses application of vitamin K3 in preparation of a medicine for treating psoriasis. The invention finds that the compound vitamin K3 can obviously inhibit the activation of the STING downstream immune pathway and has an inhibition effect on the activation of the STING pathway. A mouse psoriasis model is formulated according to the absorption and metabolism characteristics of the inhibitor VK3, and it is proved that the VK3 can significantly inhibit the process of psoriasis and up-regulation of an STING pathway at an affected part in a transdermal drug delivery mode. The STING pathway does not belong to a main pathway affecting the course of disease in most non-immunodeficient inflammatory diseases, so that a better treatment effect may be generated by using the STING pathway in combination with a direct treatment drug (such as an IL-17A antibody for psoriasis). The vitamin K3 is simple in chemical structure, mature in production process and low in preparation cost, has more remarkable price advantage compared with biological agents and small-molecule targeting drugs, and has high popularization value and application prospects.

The present invention discloses drug-loaded nanoparticles targeting neuraminidase, as well as a preparation method and application thereof, belonging to the technical field of pharmaceutical preparations. The present invention connects polysialic acid and polylactic acid glycolic acid via an ester bond to obtain drug-loaded nanoparticles with stable structure and performance. The drug-loaded nanoparticles have the function of targeting neuraminidase and have relatively high efficiency in entering cells. Furthermore, antibiotics are dispersed in the drug-loaded nanoparticles, and an inflammatory modulator is adsorbed on the surface of the drug-loaded nanoparticles. The drug-loaded nanoparticles can deliver the antibiotics and the inflammatory modulator to Grasseria suis in pigs, releasing the antibiotics and the inflammatory modulator in the slightly acidic environment of the infected site in the pig, thereby efficiently killing Grasseria suis and reducing inflammatory damage caused by bacterial infection and toxic damage of the antibiotics. Therefore, the present invention has good application prospects in the preparation of drugs for preventing and / or treating Grasseria suis infection.

The invention is suitable for the field of medicine management, and provides an information tracing method and system for undivided medicines and electronic equipment, and the method comprises the steps: generating a sub-sequence code corresponding to each undivided medicine based on a medicine package tracing code of a target medicine according to a first number value of the undivided medicines in the target medicine; acquiring patient information and medicine sales information; and based on the medicine sales information, determining a second quantity value of the to-be-sold medicine, cancelling the second quantity value of the sub-sequence codes, and associatively storing the cancelled sub-sequence codes and the patient information. According to the scheme, on the basis of the generated sub-sequence code related to the drug package traceability code, association binding of the patient information and the undivided drug is achieved, and the problem that in the prior art, an information traceability method for the undivided drug is lacked is solved.

The invention belongs to the technical field of model optimization, and provides a large language model optimization method and system based on a clinical test. The method comprises the following steps: determining a first attribute of a target drug aiming at a clinical test, and matching based on the first attribute to obtain field data; generating a small sample data set based on the domain data, and performing fine tuning training on the large language model by using the small sample data set; a plurality of second attributes are obtained through matching based on the first attributes, clinical test data are obtained based on the first attributes and the second attributes, multi-modal data fusion is conducted on the clinical test data, and a strengthened training data set is obtained; and performing reinforcement learning optimization on the fine-tuned large language model by using the reinforcement training data set, and performing model evaluation and iteration on the large language model until an optimization ending condition is met. According to the method, the field data and the clinical test data are integrated, the optimization of the large language model is realized by using reinforcement learning, and the model generalization ability of the large language model can be remarkably improved.

The invention discloses a combined treatment medicine for acute myelogenous leukemia. The combined treatment medicine is prepared from vinaclaria, azacitidine and aclarithromycin. The medicine is applied to preparation of an acute myelogenous leukemia cell proliferation inhibitor; the medicine is applied to preparation of an acute myelogenous leukemia cell apoptosis inducer. The three medicines are combined to achieve a synergistic effect: the three medicines have a synergistic anti-tumor effect instead of a simple superposition effect, and a new combined medication mechanism is a result obtained through laboratory research and an initial effect of clinical application. The subsequent consolidation treatment scheme may also be used for part of initially treated patients with acute myelogenous leukemia, which are not suitable for enhanced chemotherapy or intense intense treatment by using targeted drugs, and relapse / refractory patients with acute myelogenous leukemia, and a better treatment scheme can also bring new hope and dawn to more patients.

The invention discloses a lung cancer targeted drug treatment result prediction method based on multi-modal characteristics, and belongs to the technical field of medical image analysis, deep learning and precision medical treatment. According to the method, effective feature extraction is performed on pathological images, CT images and clinical text data by using different encoders, and deep fusion and information complementation of different modal data are realized by designing a multi-modal collaborative modeling module and a cross-modal feature sensing module. Meanwhile, a multi-instance learning method is introduced to better process heterogeneity among different modal data, contribution weights of different instances to an overall prediction result are automatically learned through an attention mechanism, and the capture and expression ability of the model to important features is enhanced. According to the method disclosed by the invention, the accuracy and the stability of prediction of the targeted drug treatment effect of the lung cancer patient are remarkably improved, meanwhile, the calculation complexity of multi-modal data processing is effectively reduced, and the method has important clinical application value and technical innovation.