129 results about "Drug discovery" patented technology

The invention relates to the field of drug discovery and delivery collaboration, in particular to an autonomous evolutionary drug discovery and delivery collaboration method and system based on a large language model. The method comprises the following steps: aiming at a given biological target three-dimensional structure, generating a candidate molecular library which is complementary with a target pocket and gives consideration to druggability through an SE (3) isovariant hybrid generation model; according to a two-stage funnel type high-throughput virtual screening process, screening out the molecule with the highest comprehensive potential from the candidate molecule library; generating a customized delivery scheme through a three-stage process; in a digital twinborn model for simulating a real in-vivo tumor microenvironment, a targeted delivery process of a drug and carrier complex is simulated, and efficiency is evaluated; atomic-scale performance confirmation is carried out on a medicine, carrier and target point ternary system through molecular dynamics simulation. The invention is suitable for drug research and development.

The invention relates to the technical field of medical artificial intelligence, in particular to a drug discovery task processing method, device and equipment based on large model reasoning, and the method comprises the steps: obtaining a drug discovery task instruction of a user; the drug discovery task instruction is input to the target large model, the target large model outputs an execution result of a drug discovery task, and the reasoning process of the target large model comprises the steps that the drug discovery task instruction is analyzed to obtain an instruction intention and instruction parameters, a target key matched with the instruction intention is selected from a parameterized memory pool based on the instruction intention, and the target key is input to the target large model; the instruction parameter is mapped into a target key value, a target calling tool format is generated based on the target key and the target key value, and a corresponding target discovery tool is called based on the target calling tool format to execute a drug discovery task; and sending an execution result of the drug discovery task to the user. According to the method, the target large model automatically executes the reasoning process, various tools are automatically called, and intelligent drug discovery is achieved.

Methods and systems are described for a high-level approach to organ generation by self-assembly using bioprinting, overcoming longstanding challenges in replicating the intricate architecture and function of complex organs. In some embodiments, these methods are used to produce lung tissues and microtissues, demonstrating how mini-lung constructs can be produced on a small scale through specialized processes and media. In such embodiments, methods and systems for generating bioprinted lung tissues and microtissues are provided, as are tissues made by such methods. The tissues find use in implantation, drug discovery, personalized medicine, and other applications. Methods for the large-scale manufacturing of lung epithelial stem cells are also provided.

This invention discloses a molecular event visualization method and electronic device, proposing the concept of a molecular film. The method acquires the topological structure file of the drug molecule-target protein and the original trajectory file generated after molecular dynamics simulation of the drug molecule-target protein; based on the original trajectory file and topological structure file, it obtains the keyframe sequence of the molecular dynamics simulation of the drug molecule-target protein; it monitors the keyframe sequence to identify key event sequences of the drug molecule-target protein, and annotates the key event sequences to obtain annotated key event sequences; and it renders the annotated key event sequences to visualize and output the generated rendering data as molecular events, significantly improving the efficiency and depth of understanding molecular mechanisms of action in drug discovery and optimization.

The present application relates to a candidate drug efficacy prediction and selection method based on a graph neural network, a medium and equipment, first, a biological medicine causal chain is obtained, then an efficacy prediction model is constructed and trained according to the causal chain. Next, after the model is constructed, the target differential protein is determined, and the relationship is determined according to the type of the differential protein. Finally, the differential protein is taken as the tail entity, the relationship is taken as the edge, the candidate drug is taken as the head entity, the triple is constructed, the prediction model is input, and the efficacy prediction result of each candidate drug is output. Overall, the present application solves the problem that the prior art cannot efficiently integrate and mine the multi-dimensional association relationship among drugs, target points and diseases, constructs an interpretable, scalable and updatable knowledge network, reduces the difficulty of drug discovery, shortens the research and development cycle and the like.

Provided herein are compositions comprising a particle comprising a library encoded bead (e.g., DEL bead) coated in a polymeric matrix forming a core-shell particle, wherein the core-shell particle is further inside a 3D tissue culture. Also provided herein are methods of making such particles, and methods of using such particles for drug discovery such as identification of bioactive compounds via high-throughput cellular activity-based phenotypic screens of DNA-encoded chemical library beads.

Compounds and methods for combinatorial drug discovery in nanoliter droplets are described herein. More particularly, novel synergistic agents that increase efficacy of antibiotic agents to treat bacterial infections are described.

The application relates to the technical field of computer-aided drug design, and provides a molecule generation method, system, medium and equipment for drug discovery, which comprises the following steps: acquiring a SMILES string of a ligand and an amino acid sequence of a target protein, respectively extracting a ligand feature vector and a protein feature vector, splicing and fusing, and then predicting a binding affinity value through a multilayer perception machine; taking the protein feature vector as a condition, generating a new molecular potential representation through a reverse denoising process of a conditional diffusion model; wherein, at each step of the reverse denoising process, a graph-level readout operation is performed on pure noise, an affinity value is predicted through the multilayer perception machine, a gradient of an affinity guidance loss is returned to a noise prediction network, and the new molecule generation is guided to a high affinity area; and the new molecular potential representation is decoded into a SMILES string through a pre-trained molecular language decoder. Novel molecules with high binding potential can be quickly generated.

The invention discloses a novel equivalence device structure closing and single-step repairing method and system in medical diagnosis, drug discovery and physiological status modeling, belongs to the field of medical diagnosis analysis, biological information processing and model verification, and ensures the reliability and traceability of the process through structural equivalence device detection and receipt track recording. The method monitors sequential equivalents (ABBA rectangles for checking consistency of operational orders) and conservation equivalents (triangles for checking information conservation and energy balance) during operation to find anomalies in diagnostic inference chains, drug action pathways, or physiological closed loops (such as predictor deviations, energy conservation imbalances, or model inconsistencies). When a non-equivalence condition is detected, the system selects a unique repair adapter from a geometric layer, a frame layer and a field layer for correction according to a preset priority, and the deviation metric mu is strictly reduced by one (delta mu = 1) during each repair, and is gradually converged to an equivalence closed state.

The invention relates to the technical field of computational biology and artificial intelligence assisted drug discovery, and discloses a drug-target effect prediction method and system based on multi-mode self-supervised learning. The method comprises the steps of drug side self-supervised pre-training, protein side self-supervised pre-training, multi-feature fusion, drug-protein interaction modeling and multi-task joint prediction. Drug-target effect prediction is carried out based on self-supervised pre-training and multi-modal information fusion, a self-supervised learning method is firstly used to train a deep learning model by using label-free data, and then the deep learning model is used to carry out multi-modal characterization, multi-modal fusion and interaction relationship modeling on drug molecules and protein targets. According to the method, combined prediction of drug-target interaction, affinity and action mechanism can be realized, and the prediction precision and model robustness of the model are effectively improved.

The invention belongs to the technical field of computational chemistry and artificial intelligence crossing, and provides a molecular potential energy surface prediction method based on deep learning. The method comprises the following steps: inputting a structure file of an unoptimized molecule, and reading and predicting energy and force of a current molecular structure by utilizing a machine learning potential model; and then, judging whether the structure is converged or not according to a prediction result by adopting a GeomeTRIC algorithm, and if not, adjusting coordinates to generate a new structure and performing circular prediction until the structure is converged. According to the method, the quantum chemistry and the deep learning technology are combined, the calculation efficiency can be greatly improved, complex inter-atomic interaction in molecules can be fully captured, and the prediction accuracy is ensured; and meanwhile, batch processing and automatic generation of a quantum chemistry software interface are supported, and the method is particularly suitable for high-throughput molecular structure optimization in the fields of drug discovery, material science and chemical product design.

The invention discloses a biomolecular interaction prediction method based on multi-modal attention fusion, and belongs to the technical field of artificial intelligence drug discovery. The method comprises the following steps: acquiring multi-modal characteristics of drugs, targets, diseases and genes: sequence structure characteristics, 3D structure characteristics, similarity network characteristics and biological relation network embedding characteristics; constructing a feature fusion prediction model, and performing training; inputting the multi-modal features of the two biological entities into the trained feature fusion prediction model, and outputting the probability of interaction of the two biological entities; the feature fusion prediction model comprises a Transform encoder and an MLP (Markup Language Protocol) network; the multi-modal features are input into a feature fusion prediction model for stacking and then are input into a Transform encoder, the features are processed by using a multi-head self-attention mechanism, and an output result is flattened and subjected to dimension reduction processing to obtain embedded vector representation; and finally, performing element corresponding multiplication on the embedded vectors of the two biological entities, inputting the embedded vectors into an MLP network, and outputting an interaction probability between the two biological entities.

The present invention relates to systems, compositions and methods for multicellular bioprinting vascularized tissue constructs comprising a hierarchical vascular network. The methods employ a dual bio-ink strategy involving fibrous microgel matrix bio-inks with adjustable hardness, self-healing rheology, and microporosity to support capillary self-assembly, and sacrificial bio-inks that define a large pourable channel after removal. The system integrates top-down printing and bottom-up cell self-organization within a programmable bio-printing platform. The key aspect of the invention is to engineer the physical topography of the matrix to actively guide vascular morphogenesis beyond conventional permissive stents. The integrated platform enables the manufacture of complex bionic vascular networks with enhanced mass delivery, cellular viability, and in vivo integration potential. The invention represents an important progress in the aspect of extendable production of functional human tissues, and has application in regenerative medicine, disease modeling and drug discovery.

The invention discloses a drug collaborative screening system based on artificial intelligence prediction and metabonomics verification, and belongs to the technical field of artificial intelligence and bioinformatics crossing. Comprising a multi-source data acquisition and feature fusion module, an artificial intelligence prediction module, a drug screening and visualization module, a metabonomics verification module and a feedback optimization and self-learning module. According to the invention, through multi-modal data integration and feature coding, unified modeling of a drug-disease-molecule network is realized; drug combination synergistic effect prediction is realized through an artificial intelligence algorithm; a prediction result is verified by using non-targeted metabonomics data, and the mechanism interpretability of the model is enhanced; a technical system capable of realizing closed-loop optimization is constructed, and an algorithm and experiment dual-verification platform is provided for drug discovery and precision medicine.

The invention discloses fluoroalkyl substituted azafluorene and pyridopyrimidino isoindole compounds as well as a synthesis method and application thereof, and belongs to the technical field of organic synthesis and drug discovery. The preparation method comprises the following steps: mixing a 3-alkenyl-1, 2, 4-oxadiazolone compound 1, a fluoroalkyl acetylenic ketone compound 2, a catalyst, an additive and an organic solvent, and carrying out heating reaction to prepare a fluoroalkyl substituted azafluorene compound 3 and / or a fluoroalkyl substituted pyridopyrimidino isoindole compound 4. Experimental research discovers that the compound disclosed by the invention has the activity of obviously inhibiting proliferation of Hela and HCT-116 cancer cells, so that the compound disclosed by the invention has obvious anti-cancer activity on cervical cancer and / or colon cancer, has potential medicinal value, and provides a new structural unit for drug screening.

The application provides a HBV inhibitor screening method based on a molecule-gene interaction constraint graph convolution network. In view of the limitation of a traditional drug discovery method in processing complex biological data, the application is based on a constructed compound library verified by anti-HBV in-vitro activity, a plurality of gene targets associated with the corresponding compound and an interaction network thereof, a graph data processing capacity of a graph convolution network model is used, and a molecule-gene interaction constraint graph convolution network model is constructed. The model combines an interaction matrix of a target protein corresponding to the gene, a gene feature matrix and a compound activity label, and effectively predicts the biological activity category of the compound. The specific steps include data processing, graph data generation, graph convolution network model training, hyperparameter optimization and model evaluation. The model parameter AUC value is 0.97, and the model effect is good. The application provides a new path and idea for virtual screening of anti-HBV drugs, and has potential application value.

The present invention discloses, in one embodiment, a method of using human induced pluripotent stem cells to generate three-dimensional human organ tissue for therapeutic drug toxicity and discovery⋅. In one embodiment, a high throughput microtiter plate is loaded with both wild type and Rett disease 3D spheroids and exposed to a drug library, and activity is measured and analyzed for disease rescue to wild type cell behavior.

Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.

A drug discovery support device includes a processor. The processor is configured to obtain multiple specimen images of a tissue specimen of multiple organs of subjects subjected to an evaluation test of a candidate substance; select, in accordance with degree-of-selection-priority information in which a degree of selection priority is set for each of the multiple organs, a target specimen image of a tissue specimen of a single organ from among the multiple specimen images; make a determination as to whether a morphological abnormality has occurred in the tissue specimen in the target specimen image; and update the degree-of-selection-priority information, based on a determination result as to whether the morphological abnormality has occurred.

To provide a predictive method and a predictive system for predicting the drug discovery target protein of disease for the therapeutic purpose.SOLUTION: In a method for predicting the drug discovery target protein of disease for the therapeutic purpose, the disease for therapeutic purpose is selected, in which the drug target protein is desired to be predicted, for that disease, an analysis step S11 is performed, in which the clinical data analysis is first performed, the drug having the effective possibility is identified, and a prediction step S21 is performed for this drug, in which the binding protein prediction for the compound is performed. Then, the predicted drug discovery target protein is displayed on a monitor or the like as appropriate S31.SELECTED DRAWING: Figure 1

The invention discloses a molecular property prediction-oriented hierarchical guidance collaborative attention method, and relates to the crossing field of artificial intelligence and chemoinformatics. The method comprises the following steps: S1, constructing atomic-scale graph representation and group-scale graph representation of molecules; s2, providing top-down context guidance for atomic-scale attention calculation through the group-level semantic features; s3, dynamically adjusting the weight of cross-scale information fusion by using a context gating mechanism; s4, breaking the independence of each attention head in the traditional multi-head attention by adopting a multi-head collaborative attention mechanism; and S5, generating a molecular representation fusing the coarse-grained functional semantics and the fine-grained local structure for property prediction. According to the method, a hierarchical guidance collaborative fusion mechanism is introduced, the problem of information conflict caused by simple splicing or summation in a traditional multi-scale fusion method is solved, collaborative enhancement of atomic-level details and group-level functional semantics is achieved, the accuracy and robustness of molecular property prediction are remarkably improved, and the method has good application prospects. And an efficient multi-scale molecular representation learning tool is provided for drug discovery and material design.

The present disclosure relates to the field of drug discovery and therapeutics, including systems and methods to predict drug function, to classify or prioritize drugs based on predicted function, and to test known and novel compounds for function in a vertebrate system. The present disclosure also relates to compounds identified using the disclosed systems and methods, the novel mechanisms of action of the compounds, and applications of the compounds as therapeutics, e.g., as cancer therapeutics.

The invention relates to a method for realizing GPCR receptor target drug discovery based on a model consensus mechanism. The method comprises the following steps: constructing composite training data of a general data set and a special data set; establishing a cascade subtask model, and sequentially performing interaction prediction, combination strength prediction and functional activity prediction; capturing interaction characteristics through a double-view-angle coding and interaction module, and outputting a standardized prediction score; generating unified confidence and consistency measurement for interaction prediction, affinity prediction and functional activity prediction tasks, and obtaining a final screening result. According to the method, the device, the processor and the computer readable storage medium for realizing GPCR receptor target drug discovery based on the model consensus mechanism, through cascade multi-task modeling and multi-scale feature fusion, layered inference of protein-small molecule action is realized, single-model deviation is reduced, prediction precision is improved, and the method and the device are suitable for large-scale popularization and application. Unstable prediction is automatically screened out by using a model consensus mechanism, and the robustness and generalization ability of a result are improved.

A mask-based diagnostic (MBD) system for remote patient monitoring that collects chemical biomarker data and non-chemical biometric data from patients in a non-invasive manner. The MBD can be used to monitor various medical conditions, including cardiovascular disease, lung cancer, diabetes, and respiratory diseases. The system consists of a mask having an exhaled breath condensate (EBC) collector that tests for chemical biomarkers in EBC. Non-chemical biometric data, such as temperature, heart rate, and blood oxygen levels can also be obtained using a wearable electronic device. The collected data is transmitted wirelessly to a remote server for aggregation, analysis, and interpretation using artificial intelligence (AI) algorithms. The AI algorithms detect patterns and trends in the patient data, which can be used for drug discovery, to identify health issues, adjust treatment plans, etc. The MBD can improve patient outcomes by providing real-time monitoring, early detection of health issues, and personalized treatment options.

Described herein are systems, methods, and interconnecting porous hydrogel blocks for cell culture. A block may comprise a 3D continuous polymeric matrix with a network of microporous cavities, and may be configured to interconnect with at least one other block. A system may comprise at least one block and be configured to be perfused via at least one cavity by at least one perfusate under one or more controllable flow rates, pressures, volumes, viscosities, and / or osmotic pressures. A method may comprise exposing at least one block to at least one perfusate under one or more controllable flow rates, pressures, volumes, viscosities, and / or osmotic pressures.

This invention relates to a method for disease therapeutic target discovery and drug prediction based on multi-omics networks and deep learning models, belonging to the interdisciplinary field of bioinformatics and artificial intelligence drug discovery. The method includes: integrating genomic expression profiles and common molecular interaction data from disease and control groups to construct a candidate whole-genome network; refining the network based on expression profile data through systematic modeling and the AIC criterion to obtain the real molecular interaction network; extracting the core network using the master network projection method and identifying key targets through pathway analysis; predicting candidate drugs interacting with the targets using a pre-trained deep neural network model; and finally screening potential therapeutic drugs based on multi-dimensional criteria such as regulatory ability, sensitivity, and toxicity. This invention achieves a complete integration from disease mechanism analysis to drug prediction, and is particularly suitable for complex diseases such as atopic dermatitis. It can systematically discover precise targets and efficiently predict repositionable drugs, significantly improving R&D efficiency.

Described are systems and methods for identifying an analyte in drug discovery and development. A method can include directing a solution having the analyte to a surface having a capture moiety coupled thereto, wherein the analyte is coupled to a force transmitter comprising a magnetically-attractable particle; using the capture moiety to capture the analyte; applying a magnetic field to the magnetically-attractable particle, wherein the magnetic field has a magnetic field condition that is sufficient to direct the analyte and the magnetically-attractable particle coupled thereto away from the capture moiety; and using the magnetic field condition to identify at least one event of the analyte, wherein the magnetic field condition comprises a time-varying force, at least one period of time, at least one motion path, or any combination thereof.

The invention belongs to the cross technical field of computer-aided drug discovery and deep learning, and discloses a drug virtual screening method and system based on gene ontology enhanced contrast learning, and the method comprises the steps: collecting a protein-small molecule historical pairing data set; constructing a multi-mode dual-encoder framework which is composed of a protein encoder and a small molecule encoder and supports sequence and structure dual-mode input; introducing gene ontology information, and performing functional semantic enhancement on the protein encoder through protein-gene ontology and gene ontology-gene ontology contrast learning tasks; a model is trained through the combination of comparative learning and affinity sorting; in the reasoning stage, protein representation independent of gene ontology information is used for small molecule screening, multi-modal prediction results are integrated through an uncertainty perception fusion mechanism, and a more stable sequencing result is obtained. The generalization ability of the model to unknown proteins is improved, and the method is suitable for actual drug discovery scenes.

An automatic drug discovery method and system based on multi-agent collaboration and structured memory mechanism solves the problems of context explosion, key constraint loss, task arrangement instability, tool call lack of deterministic control and decision process black box in long-range complex tasks of existing large language model driven drug discovery systems. The whole drug discovery process is divided into two functional levels of cognitive layer and service layer. The cognitive layer serves as the global control center, used for receiving user input tasks, extracting target targets and optimization constraints, determining task types, selecting generation models and outputting standardized task plans. The service layer serves as the execution layer, which calls the corresponding special agent to complete the actual calculation task according to the task plan output by the cognitive layer. The present application is suitable for various automatic drug discovery scenarios.

This invention proposes a drug target interaction prediction method based on graph interaction and multi-granularity fusion, belonging to the field of bioinformatics. Addressing issues such as oversmoothing of graph neural networks in heterogeneous graphs, insufficient utilization of drug-target interactions and multi-scale information, and poor adaptability of multi-branch feature splicing, an improved method is proposed. First, a deep interactive graph neural network branch is constructed on the node adaptive local smoothing features and the heterogeneous graph composed of drug-drug, target-target, and drug-target edges, realizing message passing and fusion of classification edges and introducing residual connections. Second, a dual-tower interactive branch is constructed for multi-order interactions. Third, a multi-granularity fusion branch is constructed to achieve progressive fusion of multi-scale features. Finally, the multi-branch representations are weighted and fused through a gating network, combined with the output results of a perceptron and a sigmoid function, and optimized using binary cross-entropy. This invention improves prediction performance and interpretability, and is applicable to scenarios such as drug discovery and target screening.