789 results about "Drug candidate" patented technology

The invention relates to the technical field of electronic prescriptions, in particular to a dynamic electronic prescription generation method and system based on artificial intelligence. The method comprises the following steps: collecting the latest physiological state parameter flow of a patient, carrying out real-time health state evaluation, and generating a personalized patient state map; historical medical records of a patient are extracted, time sequence pathological evolution tracking is carried out, and a pathological evolution trajectory is generated; performing multi-parameter time sequence difference comparison calculation and drug curative effect quantitative evaluation based on the personalized patient state map and the pathological evolution trajectory to obtain a curative effect evaluation report; performing allergic drug identification on the patient based on the historical medical record of the patient, and performing secondary drug screening to obtain a drug candidate set; and performing intelligent matching calculation on the drug candidate set according to the curative effect evaluation report, and performing combinatorial optimization analysis to generate a final effective electronic prescription. The electronic prescription is automatically updated and adjusted based on the state change of the patient, the risk of allergic prescriptions is reduced, and the safety of the prescriptions is improved.

The invention discloses a medication assisting system and device based on symptom-medicine two-way verification, and belongs to the technical field of intelligent medical treatment. The system comprises a data preprocessing module, an entity extraction and alignment module, an atlas retrieval module, a case retrieval module and a bidirectional verification and auxiliary decision module. The system extracts a medical entity through an AC automaton rule engine and a knowledge enhanced BERT model, and realizes mapping with a knowledge graph entity by adopting a two-stage term alignment strategy; performing knowledge graph query by adopting an operation chain generation mechanism driven by cue words to obtain a candidate drug set; meanwhile, similar cases are retrieved based on vector similarity, and an associated drug set is generated; and finally, fusing external medicine knowledge base data, executing a bidirectional verification and completion strategy, and outputting medicine recommendation with clinical safety and coverage range. The invention further provides a device structure for realizing the functions of the system, and the accuracy and interpretability of clinical medication aid decision making are effectively improved.

The invention discloses a tumor personalized drug recommendation method and system based on fusion of multiple clinical guidelines, and belongs to the technical field of bioinformatics and precision medicine. The method comprises the following steps: acquiring gene variation data and clinical feature information of a patient; generating a preliminary drug candidate list based on drug recommendation rules of a plurality of clinical guidelines; calculating the weight of each guide by adopting a dynamic weight distribution algorithm; obtaining drug-gene-disease associated information through multi-hop reasoning of the knowledge graph; calculating a drug evidence score by adopting a multi-dimensional scoring algorithm; carrying out personalized score adjustment in combination with individual features of the patient; and outputting a personalized drug recommendation result. According to the method, through multi-guide dynamic fusion, multi-dimensional evidence scoring, knowledge graph reasoning and personalized adjustment, the problems of incomplete guide coverage, lack of personalization, low response speed and the like in the prior art are solved, the method has the advantages of high accuracy, high clinical applicability, quick response and the like, and the clinical real-time decision-making requirement can be met.

A method is disclosed for using an artificial intelligence engine to generate candidate drug compounds, wherein the method comprises: generating candidate drug compounds comprising sequences via a creator module of the artificial intelligence engine. The method includes generating, via a descriptor module, a respective description for each of the candidate drug compounds at nodes in a knowledge graph, wherein the knowledge graph comprises a multi-dimensional representation of the candidate drug compounds and the respective description comprises drug compound structural information, drug compound activity information, and drug compound semantic information. The method includes determining a shape of the multi-dimensional representation of the candidate drug compounds; determining, based on the shape, a slice configured to be obtained from the representation; determining, using a decoder, which dimensions are included in the slice; and based on the dimensions, determining an effectiveness of a biomedical feature of the slice.

The invention relates to the technical field of drug research and development, in particular to a target AKT1 drug molecule discovery and delivery integrated system and method based on deep learning. Aiming at the problems of molecular design, optimization and delivery link separation and low research and development efficiency of drugs in the prior art, the system constructs a multi-module collaborative framework, and comprises a target analysis module for analyzing a target structure and formulating a generation strategy; the molecule generation and optimization module is used for generating and optimizing candidate molecules in combination with the generation model and reinforcement learning; the delivery scheme design module is used for matching a delivery carrier based on molecular physicochemical properties; and a verification module that predicts and evaluates the molecule-deliverer combination using molecular docking and ADMET. An evaluation result of the verification module is fed back to the molecule generation and optimization module to form a closed-loop optimization mechanism, so that an automatic process from target analysis to output of candidate drug molecules and matched delivery schemes thereof is realized. Compared with the prior art, the efficiency and success rate of early drug discovery can be improved.

The invention discloses an application of cryptotanshinone in preparation of an anti-mycobacterium tuberculosis drug, and relates to the technical field of bioengineering. The molecular formula of the valerianin disclosed by the invention is C19H20O3. The mycobacterium tuberculosis is a mycobacterium tuberculosis H37Rv strain. Cryptotanshinone is disclosed as a natural-source compound for the first time, has the dual advantages of high efficiency and low toxicity, has an obvious effect of resisting mycobacterium tuberculosis, provides a brand new candidate drug scheme with development potential for solving the difficulty of tuberculosis treatment, and has great scientific value and application prospect.

The invention discloses an ultrashort-sequence antibacterial peptide rich in lysine and tryptophan, an antibacterial peptide hydrogel and application of the ultrashort-sequence antibacterial peptide and the antibacterial peptide hydrogel. The preparation method comprises the following steps: combining lysine and tryptophan to obtain polypeptide containing five or six amino acids; then amidation is carried out on the C terminal of the polypeptide, fatty acid chain modification is carried out on the N terminal and side chain amino groups, the ultra-short sequence antibacterial peptide rich in lysine and tryptophan is obtained, and the antibacterial peptide has broad-spectrum antibacterial activity for resisting gram-positive and gram-negative bacteria. An antibacterial peptide micelle self-assembly experiment shows that C12-2KW can be self-assembled into stable hydrogel in a 7.17 mu M PBS (Phosphate Buffer Solution), the hydrogel internally has a compact net-shaped structure, can completely cover the peritoneal injury surface and physically isolate the peritoneal injury surface from surrounding tissues, and has a good effect on treating wound infection caused by MRSA (Methicillin-Resistant Staphylococcus Aureus); meanwhile, the compound has the effect of resisting intestinal wall and abdominal wall adhesion after an operation, is low in toxicity, has a very good application prospect in the aspect of preparing clinical antibacterial drugs, and is expected to become a candidate drug of a novel antibiotic.

The invention relates to the field of biomedical application of inorganic materials, in particular to application of lanthanum carbonate in preparation of drugs for resisting liver cirrhosis and inhibiting liver inflammations, the drugs comprise lanthanum carbonate and pharmaceutically acceptable auxiliary materials, the drugs are ground and then mixed with food to prepare a drug mixture, and the drug mixture is prepared into the drug for resisting liver cirrhosis and inhibiting liver inflammations. The mass ratio of the lanthanum carbonate in the medicine mixture is 1-10%, and the mechanism of the lanthanum carbonate for treating the liver cirrhosis is as follows: interference or blocking of activation and proliferation of hepatic stellate cells, inhibition of extracellular matrix generation and promotion of extracellular matrix degradation. According to the invention, a rat liver cirrhosis model is constructed through chemical induction, and the effects of reversing liver cirrhosis and inhibiting liver inflammation are found when a rat is treated by taking lanthanum carbonate orally, so that a new candidate drug is provided for clinical treatment of liver cirrhosis.

The invention relates to the technical field of traditional Chinese medicine carbon dots, in particular to ligusticum wallichii carbon dots, a medicine and application. The ligusticum wallichii carbon dots disclosed by the invention are obtained by carbonizing ligusticum wallichii by a hydrothermal method, wherein the hydrothermal method carbonization is a hydrothermal reaction at 175-195 DEG C for 8-12 hours; research finds that the ligusticum wallichii carbon dots have the characteristic of treating myocardial apoptosis caused by myocardial ischemia reperfusion injury, and compared with ligusticum wallichii carbon dots synthesized by ligustrazine and a pyrolysis method, the ligusticum wallichii carbon dots have the advantage that the heart function can be better recovered. The invention provides a new candidate drug for treating myocardial ischemia reperfusion injury.

The invention provides application of Fe3O4 nanoparticles in preparation of a medicine for treating benign prostatic hyperplasia, and relates to the technical field of biological medicines. The invention further discloses application of the Fe3O4 nano-particles in preparation of a medicine for treating benign prostatic hyperplasia. The research result of the invention emphasizes that the ferroferric oxide nanoparticles are a promising candidate drug for targeted therapy of BPH and related prostate diseases, and new possibility is provided for treatment intervention of urinary surgery. The inventor studies the effect of the Fe3O4 nanoparticles in inhibiting benign prostatic hyperplasia. Results of the inventor show that the nanoparticles not only effectively reduce the proliferation rate of prostate cells, but also improve related biochemical indexes. Therefore, the polypeptide has potential application in treatment of prostatic hyperplasia.

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 belongs to the field of new application of medicines, and particularly relates to application of SIRT4-IN-1 in preparation of medicines for treating autoimmune diseases. It is found for the first time that SIRT4-IN-1 reduces the activation degree of immune response induced by HSV-1 virus infection by up-regulating acetylation modification of cGAS, inhibits abnormal activation of immune response in Trex1 deficient mouse cells and peripheral blood mononuclear cells of systemic lupus erythematosus patients, and improves abnormal activation of autoimmune response. The invention provides a new candidate drug for clinical treatment of systemic lupus erythematosus.

The invention discloses a candidate polypeptide drug generation method and system based on artificial intelligence, and relates to the technical field of drug discovery, the method comprises the following steps: collecting sequence data, structural data and related property tags of polypeptide drug molecules and corresponding target proteins thereof, and utilizing interaction site information of polypeptides and targets thereof to generate candidate polypeptide drugs; a target protein structure is fixed, a polypeptide skeleton is generated by using a diffusion model, the generated polypeptide skeleton is inversely folded so as to generate new candidate polypeptide molecules, and a prediction result is analyzed. The method realizes accurate, efficient and low-cost rapid identification and generation of potential candidate drug molecules, effectively solves the problem of structural information of proteins and polypeptides, solves the problem that the polypeptides are difficult to model in a free state, and adjusts the diffusion process in combination with the physical characteristics of the proteins, so that the method conforms to the constraint of an actual chemical environment.

The invention provides a diversity maintenance and deduplication screening method and system for drug candidate molecules of a drug-loaded corneal contact lens, and relates to the technical field of artificial intelligence and candidate molecule generation and screening. The method comprises the steps that after density perception screening, evolutionary memory comparison, gradient sensitivity evaluation and non-dominated sorting screening are sequentially carried out on a pre-obtained original candidate molecular solution set, candidate molecular solutions are reserved as an optimized candidate subset of a current iteration period and used for participating in subsequent fitness reevaluation and updating of next-generation generator parameters; and repeating the above process until a preset number of loop iterations is reached, and then stopping. According to the method, a quadruple cooperation mechanism of density perception, evolutionary memory, gradient sensitivity and non-dominated sorting is constructed, so that adaptive screening and diversity maintenance of a candidate molecular solution set in an iteration process are realized, and the problems of insufficient exploration and local optimum caused by candidate convergence in the prior art are effectively solved.

A device may include a data acquisition layer comprising a patient information collector and a drug information collector, each operable to ingest patient-specific clinical data and drug-specific data, respectively. A device may include an analysis engine comprising a multi-criteria ranking engine configured to compute composite suitability score for candidate medications by weighting one or more of medication efficacy, medication safety, medication resistance, medication cost, or patient-specific coverage factors. A device may include a presentation layer comprising a summary dashboard configured to display a ranked medication recommendation set produced by the analysis engine.

The invention belongs to the technical field of biological medicines, and particularly discloses an alkynyl pyrimidine derivative as well as a preparation method and application thereof. The structure of the alkynyl pyrimidine derivative is shown as a formula I. The novel alkynyl pyrimidine derivative capable of efficiently inhibiting the LSD1 activity is provided, the preparation process is simple and easy to implement, the good effect of resisting colorectal cancer cell proliferation is shown on the animal level, and safe and effective candidate drug molecules are provided for targeted therapy of colorectal cancer.

The invention discloses a drug effect prediction method for drug research and development, and relates to the technical field of drug effect analysis, and the method comprises the steps: collecting drug and target data from a plurality of data sources, and carrying out the preprocessing of the drug and target data; and carrying out feature analysis on the basis of the preprocessed drug and target data to obtain a comprehensive feature sequence, carrying out gene and pathway analysis on a disease applied by the drug, carrying out disease feature coding, and then modeling a disease background. According to the method, drug-target pairs with potential drug effects can be rapidly screened in the early stage of drug research and development through drug effect prediction, further research on a large number of invalid compounds is avoided, time and resources are saved, and then, the interaction strength between the drug and the target is predicted through the model, so that the drug effect is improved. And comprehensive analysis is carried out in combination with a disease background, so that potential drug candidate molecules can be identified more accurately, and the research and development cycle is further shortened.

The invention discloses an application of an RUNX2 inhibitor in preparation of a medicine for treating vascular calcification related diseases, and the RUNX2 inhibitor comprises an ester compound which is selected from at least one of Corilagin, 1, 2, 3, 6-tetragalloylglucose, NADP (Nicotinamide Adenine Dinucleotide Phosphate), Stafib-2 and uridine triphosphate sodium salt. The ester compound provided by the invention is used as a drug for inhibiting vascular calcification and progression of the RUNX2 inhibitor, and provides a new thought, a target spot and a small molecule drug for treatment of vascular calcification. The five small molecule compounds have good drug safety and a strong vascular calcification inhibition effect, and can be used as candidate drugs for inhibiting vascular calcification.

The invention relates to a molecular property prediction method and system based on multi-task pre-training and multi-modal fusion. The method comprises the following steps: collecting a data set and performing molecular conversion; performing multi-task pre-training, which comprises the following steps: generating a heterogeneous enhanced view; constructing a pseudo label; performing comparative learning according to the structure enhanced view and the heterogeneous enhanced view, and constructing a maximum similar task; capturing semantic differences among molecules according to the pseudo labels; carrying out multi-modal fusion, namely introducing functional group structure information, and extracting molecular sequence characteristics based on Transform and Mamba2 to obtain fused molecular multi-modal representation; and analyzing and predicting the classification or regression task. A heterogeneous enhanced view is established in a multi-task pre-training stage, multi-task self-supervision is performed in combination with multi-granularity features of molecular fingerprints, and functional group structure information is introduced in a multi-modal fusion stage, so that deep cross-modal interaction is realized, downstream prediction performance is improved, and candidate drug screening and molecular property evaluation processes are accelerated.

The invention relates to the technical field of anti-enterovirus medicines, in particular to application of Enractevir in preparation of an anti-enterovirus medicine. According to the invention, efficient screening of candidate drugs is realized through a multi-channel virus reporting system based on a TAT trans-activation effect, and a small molecule compound drug Enractevir with a specific antiviral effect on enterovirus group A 71 and enterovirus group D 68 is obtained. Enractevir improves the interferon secretion level of host cells by inhibiting the activity of enterovirus 2A / 3C protease, thereby inhibiting virus replication and proliferation. Enractevir plays a role through double-target 2A / 3C protease, and the risk of virus drug resistance can be reduced. According to the technical scheme, the technical problem that in the prior art, specific drugs for the two kinds of enteroviruses are lacked can be solved, a standardized treatment scheme is provided for prevention and control of enterovirus related diseases in the global range, and the medicine has remarkable public health significance and wide application and popularization value.

The invention discloses a preparation method of a cochlear organ chip integrated with a blood labyrinth barrier, and belongs to the field of biomedical engineering. The preparation method of the simulated blood labyrinth barrier comprises the following steps: constructing an endothelial cell culture cavity and a pericyte culture cavity, and separating the endothelial cell culture cavity and the pericyte culture cavity through a simulated basement membrane; and respectively inoculating percutaneous cells and endothelial cells into a percutaneous cell culture cavity and an endothelial cell culture cavity, and culturing to obtain the simulated blood lost barrier. The chip can effectively simulate the structure and the function of the BLB, and the barrier integrity of the BLB is evaluated through TEER (trans-epithelial electrical resistance) measurement and an apparent permeability coefficient. The invention also relates to application of the chip in NIHL drug screening, in particular to drug evaluation for oxidative stress induced inner ear organ injury. Through the platform, the protection effect of the candidate drugs on the TBHP-induced oxidative stress injury of the inner ear organs can be evaluated, and a new tool and thought are provided for developing novel NIHL treatment drugs.

The invention discloses a fusion polypeptide TGNL and application of neural stem cells modified by the fusion polypeptide TGNL in spinal cord injury repair. The fusion polypeptide TGNL is composed of a TAT penetrating domain, a GSTP targeting domain, an NICDi inhibition domain, an MMP-2 activation domain and a flexible Linker connecting the three domains, and the full-length amino acid sequence is as shown in SEQ ID NO. 1. The invention also provides a preparation method of the polypeptide, a neural stem cell (hNSCs-TGNL) for expressing the polypeptide and a construction method of the neural stem cell (hNSCs-TGNL). Experiments show that the fusion polypeptide TGNL can efficiently penetrate nerve cells, specifically target glial scars, is selectively activated by MMP-2, and promotes neural stem cells to differentiate into neurons by inhibiting Notch signals; the hNSCs-TGNL has high survival rate and strong penetrability in an in-vitro glial scar model, and can significantly promote motor function recovery, reduce injury volume, increase axon density and reduce glial scars in a rat spinal cord contusion model. The invention provides a novel candidate drug and a cell treatment strategy for clinical treatment of spinal cord injury, and has remarkable clinical application value.

The invention belongs to the technical field of biological medicines, and particularly discloses a quinazoline derivative as well as a preparation method and application thereof. The structure of the quinazoline derivative is shown as a formula I. The invention provides a novel quinazoline derivative capable of efficiently inhibiting LSD1 activity, the preparation process is simple and easy to implement, a good effect of resisting colorectal cancer cell proliferation is shown on the animal level, and safe and effective candidate drug molecules are provided for targeted therapy of colorectal cancer.

The invention relates to inhibitors of JAK proteins for use in the treatment of cancer. In vitro methods for screening of candidate agents as JAK inhibitors, and methods for the selection of a subject suffering from cancer as candidate for a cancer therapy comprising in particular JAK inhibitors are also disclosed.

The invention discloses a drug response prediction model for drug combination based on multi-omics data and transfer learning and application, belongs to the technical field of drug response prediction, and solves the problems that a traditional method is lack of a drug response prediction system based on dosage, is mostly based on single drug response prediction, is lack of modeling ability for drug combination, and cannot predict drug response. Meanwhile, the cost is high, the period is long, and individual differences cannot be reflected. According to the prediction model, digital characteristic data, drug dosage and cell line multi-omics data of drugs are integrated, the prediction model adopts a combined transfer learning algorithm, parameters of a single drug response model are migrated into a drug combination model, a gene function module is introduced to enhance the interpretability of the model, and the prediction model can be applied to drug combination. Four cross validation strategies are adopted for evaluation of the prediction model, so that the drug response of drug combination of different candidate drugs is predicted, the method can be used for predicting the response of the drug combination, and the optimal drug combination and dosage are recommended.

The invention belongs to the technical field of biological medicines, and particularly discloses a pyrimidine derivative as well as a preparation method and application thereof. The structure of the pyrimidine derivative is as shown in formula I in the specification. The novel pyrimidine derivative capable of efficiently inhibiting the LSD1 activity is provided, the preparation process is simple and easy to implement, the good effect of resisting colorectal cancer cell proliferation is shown on the animal level, and safe and effective candidate drug molecules are provided for targeted therapy of colorectal cancer.

The invention provides a fluorescent probe compound for simultaneously detecting HClO and ONOO <-> at two sites as well as preparation and application of the fluorescent probe compound. According to the probe compound, N, N-dimethylaminothioformyl ester is used as a response group of HClO, and the fluorescent probe RhB-ClO for simultaneously detecting HClO and ONOO <-> is constructed by expanding a conjugated structure of rhodamine. The probe shows excellent selectivity, high sensitivity (the detection limits are 9.9 nM and 13.1 nM respectively), good light stability and real-time responsiveness (the response time is 30 s and 150 s respectively) on HClO and ONOO <->. The probe not only can realize dual-channel imaging of HClO and ONOO <-> under oxidative stress conditions, but also can visualize the ferroptosis process of cells, a Parkinson's disease model and changes of HClO and ONOO <-> levels in an APAP-induced liver injury model. In addition, RhB-ClO can also be used for screening potential candidate drugs for nerve injury and liver injury, and it is proved that luteolin and piplongumine can be used as candidate antioxidant drugs.

The invention relates to the technical field of medical artificial intelligence, and discloses a drug recommendation system and method based on multi-agent cooperation. The system comprises the steps that a retrieval agent conducts structured processing and retrieval enhancement generation on extracted multi-source heterogeneous data, and a first drug candidate set related to a target disease is obtained; the pharmacological agent performs hierarchical quantitative scoring and reflection-driven rearrangement on the first drug candidate set to obtain a second drug candidate set; the clinical intelligent agent performs clinical quantitative scoring and diagnosis and treatment adaptive weight ranking on the first drug candidate set to obtain a third drug candidate set; and the coordination agent carries out dynamic weighted fusion on the first drug candidate set, the second drug candidate set and the third drug candidate set to obtain a drug comprehensive evaluation score matrix, and carries out conflict resolution and fine arrangement output on the drug comprehensive score matrix to obtain a drug recommendation result related to the target disease. The accuracy, interpretability and reliability of drug recommendation are remarkably improved.

The invention relates to the technical field of structural analysis and learning of pharmaceutical activity and pharmaceutical molecules, in particular to a prediction method of pharmaceutical activity. According to the technical scheme, the method comprises the steps of extracting and preprocessing data features, conducting joint modeling on drug molecular features through a Gaussian correlation embedding mechanism to form a prediction model so as to process the complex relation between different modals and heterogeneous data features, and obtaining a drug function activity prediction network through the prediction model. The method can effectively capture and model multi-dimensional dependency in drug molecule features, and can map a multi-dimensional nonlinear dependency structure to a Gaussian distribution space by introducing a Gaussian correlation function, so as to generate uniform and consistent feature embedding representation among the multi-dimensional features. According to the method, heterogeneity data such as chemical structures, biological activity and drug interaction of compounds can be effectively integrated, and the deep dependency relationship of the heterogeneity data is captured, so that the precision and robustness of drug characteristic prediction and candidate drug screening are remarkably improved.

The invention discloses application of a compound with a benzo-aza structure in preparation of a medicine for inhibiting osteoclast formation, the structure of the compound is shown as a formula (1), and R substituent is one of H, OCH3, Br, F and i-Pr. Cell experiments prove that the compound with the benzo-aza structure has a remarkable inhibiting effect on RANKL-induced osteoclast differentiation and marker gene expression, can be used as a candidate medicine which is novel in structure and is used for inhibiting osteoclast formation, and is used for targeting osteoclast to treat osteoporosis related diseases.