364 results about "Drug discovery" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

A computer-implemented method of training a machine learning model to identify biological entities for drug discovery is disclosed. The method comprises providing a training data set comprising a plurality of entity-linked text sequences, each text sequence including a mention of a biological entity, where the biological entity is linked to a corresponding biological entity identifier from a set of possible biological entity identifiers; masking the mention of the biological entity within each text sequence; encoding each masked text sequence into an input representation for a machine learning model; and training a machine learning model to predict the unique entity identifier of the masked biological entity based on the input representation. The described method is able to utilise the full breadth of the rich contextual information available in the biomedical text corpus to predict new biological targets for drug discovery and avoids the restrictions intrinsic to relationship prediction using knowledge graphs. The ability to identify more promising, biologically relevant targets in an automated manner, significantly reduces the requirement of human input and reduces the failure rate in targets that are progressed in the drug delivery pipeline.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention provides a drug-disease association prediction method and system based on heterogeneous node sequence representation, and belongs to the technical field of drug discovery in bioinformatics. Constructing a drug similarity network and a disease similarity network; constructing a heterogeneous biological interaction network by using the drug similarity network and the disease similarity network, and obtaining a first-order neighbor subgraph of the target node; splitting the first-order neighbor subgraph of the target node into a plurality of meta-relation bigraphs according to the meta-relation type, performing intra-domain message propagation and aggregation in each meta-relation bigraph, and updating the semantic features of the target node; fusing the semantic features of the target nodes in a cross-domain manner, updating the sequence features of the target nodes, and aggregating the features; obtaining vector representation of a target node through a multi-head self-attention mechanism by using the updated sequence features; and inputting the vector representations of the drug nodes and the disease nodes into a multi-layer perceptron to predict the drug-disease association probability. Semantic information confusion is effectively avoided, and then the accuracy of model prediction is improved.

The invention provides a drug target affinity prediction algorithm based on protein three-mode and multi-feature fusion, and the algorithm makes full use of sequence information and two-dimensional and three-dimensional structural features of drugs and proteins, and realizes deep integration of data. The technical innovation point lies in that a multi-modal data fusion mechanism and an advanced graph neural network technology are introduced, and the sensitivity and recognition capability of the model to key biomarkers are enhanced through a dynamic attention mechanism. According to the technical advantage, the method has higher accuracy and stability when processing complex biological data, and compared with the prior art, the generalization ability and the interpretability are remarkably improved. In the aspect of technical maturity, the algorithm has passed multiple rounds of verification and shows good prediction performance and adaptability. The method is simple in operation and wide in market application prospect, can assist in new drug discovery and accelerate the drug design process, can also promote the development of the precision medical field, and particularly has important value in targeted therapy and disease prevention strategy formulation.

The invention provides a drug-disease association prediction method and system based on cross-view comparative learning, and belongs to the technical field of drug discovery in bioinformatics. Constructing a semantic view and an interactive view; constructing and training a drug-disease association probability prediction model: integrating the semantic view and the interaction view to obtain fused drug vector representation and disease vector representation as a fusion view; using the fused drug and disease vector representation to output a drug-disease association probability; performing cross-view comparison learning on the fusion view and the semantic view; performing cross-view comparison learning on the fusion view and the interactive view; implicit knowledge is migrated from comparative learning to drug-disease association probability prediction through bottom-layer shared parameters in a multi-task learning mode. And the problem that necessary association information between the medicine and the disease is discarded due to comparison view enhancement by adopting random disturbance is avoided, so that the accuracy of medicine-disease association prediction is improved.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

Embodiments are directed to a method for determining relationships between genes, phenotypes, and diseases that includes extracting a first set of biological data containing gene data, phenotype data, and disease data from a database, extracting a second set of biological data from a database, creating nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents, training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results, using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes, creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes, and displaying at least one of the association scores to a user.

A query-based generic end-to-end molecular optimization (“QMO”) system framework, method and computer program product for optimizing molecules, such as for accelerating drug discovery. The QMO framework decouples representation learning and guided search and applies to any plug-in encoder-decoder with continuous latent representations. QMO framework directly incorporates evaluations based on chemical modeling, analysis packages, and pre-trained machine-learned prediction models for efficient molecule optimization using a query-based guided search method based on zeroth order optimization. The QMO features efficient guided search with molecular property evaluations and constraints obtained using the predictive models and chemical modeling and analysis packages. QMO tasks include optimizing drug-likeness and penalized log P scores with similarity constraints and improving the target binding affinity of existing drugs to pathogens such as the SARS-CoV-2 main protease protein while preserving the desired drug properties. QMO tasks further improves optimizing antimicrobial peptides toward lower toxicity.

An artificial intelligence system for drug discovery and development, consisting of: a data integration module configured to collect and process pharmaceutical data from multiple sources, including molecular structures, protein-protein interactions, drug-protein interactions, and clinical trial data, and to standardize the collected data into a uniform format; a machine learning engine configured to: analyze the standardized data using deep learning algorithms, generate predictive models for drug-protein interactions, and identify potential drug candidates based on molecular features; a drug development module configured for the following purposes: generation of novel molecular structures using generative contradictory networks, evaluation of the synthetic feasibility of generated molecules, and prediction of the biological activities of developed molecules; A clinical trial optimization module configured to: analyze patient data to select optimal study participants; predict drug efficacy based on molecular and patient-related characteristics; and create study protocols based on the predicted outcomes; and a validation module configured for evaluating the accuracy of generated predictions, validating synthetic routes for drug candidates, and assessing the safety profiles of developed molecules.

In a small molecule drug discovery method, a transition state for a specific enzyme is modelled using quantum mechanics and molecular dynamics based simulation of the enzyme and substrate reaction; data defining the transition state (a ‘quantum pharmacophore’) is fed to a machine learning engine configured to generate transition state analogues, such as enzyme inhibitors.

A method for training a deep learning model configured for virtual screening of molecules for micro ribonucleic acid (miRNA) drug discovery includes creating a training dataset including a plurality of assay datasets. The method also includes training the deep learning model to learn a plurality of tasks; performing, using the deep learning model, inference for the respective task associated with at least one miRNA assay dataset; and evaluating respective performance metrics associated with the respective predictions for each of the plurality of tasks. The method further includes selecting a set of the plurality of assay datasets for training; and training the deep learning model to learn a set of the plurality of tasks using the set of the plurality of assay datasets. The trained deep learning model is configured to predict molecules capable of affecting a target miRNA.

The invention relates to the field of artificial intelligence assisted drug discovery, in particular to a protein large language model-based molecule generation and optimization method, which comprises the following steps of: acquiring amino acid sequence information and three-dimensional structure information of a target protein pocket; encoding the amino acid sequence of the protein pocket by using a protein encoder constructed based on a protein large language model to obtain a protein pocket feature vector; using a context encoder module to encode the context information according to a preset molecule generation mode (de novo generation or optimization based on a seed compound) to obtain a latent vector; and fusing the protein pocket feature vector with the latent vector. According to the method, accurate representation of the protein pocket is realized by utilizing the protein large language model, and a generation-screening-optimization iterative drug design strategy is developed by supporting a unified framework of two generation modes, so that the targeting specificity of generated molecules and the overall efficiency of drug design are improved.

The invention discloses a drug-target interaction prediction method based on graph neural network blocks. Accurate discrimination of the interaction between the drug and the target is a key stage of drug discovery, and the core of prediction of the interaction between the drug and the target is reasonable molecular characterization learning. The previous research has proved that GNN is effective in the aspect of drug feature coding. However, the GNN-based methods cannot effectively give consideration to local substructure characteristics of drug molecules and overall structure attributes of the molecules. For the current difficulty, a framework named GNNBlockDTI is provided. According to the GNNBlock DTI, multiple layers of GNNs are combined into a GNNBlock unit, and a feature enhancement strategy is introduced, so that a substructure mode of a drug map in a local range is captured. And filtering redundant information among the GNNBlocks by using a gating unit mechanism so as to obtain effective overall structure information of the medicine. According to the method, the global and local structure attributes of the drug molecules can be effectively coded, and the accuracy of drug-target interaction prediction is improved.

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 a drug molecule virtual screening method based on combination mode analysis, and belongs to the field of artificial intelligence drug discovery. Using PLIP to calculate binding modes between the to-be-screened target and the small molecules, performing coding, using a DBSCAN algorithm to cluster the binding modes, and identifying compound populations of similar binding modes; in the screening stage, two key indexes are introduced: an energy index is formed according to a hydrogen bond compensation number and an AutoDock Vina score, the correlation between related residues and key site alpha carbon atoms in a binding mode is used as an activity index for evaluating the binding stability and characteristic activity between a target and a small molecule, and multi-target frontier analysis is performed on candidate molecules; according to the method, the efficiency and precision of virtual screening are remarkably improved, detailed analysis on intermolecular interaction is enhanced, the method is suitable for small-scale and large-scale molecular screening, the drug research and development cost is reduced, the drug research and development time is shortened, the diversity of virtual screening results is enriched, and potential candidate molecules are provided for subsequent experiments.

The present invention provides a method and system for fabricating complex human tissues by integrating bio-electrospraying, cell electrospinning merged with 3D multi material microfluidic bioprinting, and future additions of magnetic and acoustic levitation technologies. The fabricated tissues serve as an alternative to animal testing in drug discovery processes, aiding in the prediction of human physiological responses to various drug compounds.

The invention discloses a lung squamous cell carcinoma drug discovery method and system based on disturbance response scanning analysis, and belongs to the field of biological medicines.The method comprises the steps that lung squamous cell carcinoma protein expression profile data is downloaded and preprocessed, a robust network module is constructed and multi-scale analysis and evaluation are conducted, and drug genomic data are obtained based on drug genomic data. Predicting the drug sensitivity of the patient by using machine learning, and generating a DRN through difference analysis of the drug sensitivity; predicting drug-target protein binding affinity in combination with deep learning, quantifying node sensitivity through a PRS technology, calculating a DPI disturbance score and ranking drug priorities; high-ranking drugs are screened through comprehensive sensitivity analysis and literature investigation; a drug for a lung squamous carcinoma cell line is screened out through a pre-experiment and is relocated. According to the framework developed by the invention, by integrating proteomics, pharmacogenomics and dynamic network analysis, systematic analysis is provided for relocation drug identification of lung squamous cell carcinoma, and good news is brought to treatment of lung squamous cell carcinoma.

Described herein are platforms, systems, media, and methods for a machine learning-driven iterative drug discovery process. Some aspects comprise: receiving a first input data set comprising first binding interaction information between a target molecule and a library of compounds; processing the first input data set using a machine learning module to generate a model representation of binding interactions, wherein the representation is configured to predict binding between the target molecule and an input compound; determining an updated library of compounds using the model representation of binding interactions, wherein the updated library of compounds comprises one or more new compounds predicted to bind the target molecule; receiving a second input data set comprising second binding interaction information between the target molecule and the updated library of compounds; and processing the second input data set using the machine learning module to update the model representation of binding interactions, wherein the predictive accuracy of the updated model representation is improved.

The invention belongs to the field of bioinformatics, and relates to a parallel full-connection network-based drug and target affinity prediction method, which comprises the technologies of deep learning, a parallel full-connection network, an attention mechanism and the like. The method comprises the following steps: firstly, preprocessing data of a drug molecule sequence and a protein sequence, performing feature extraction on the molecule sequence by using a Morgan fingerprint model, and performing feature extraction on the protein sequence by using a pre-trained protein language model ESM-2; secondly, respectively inputting the processed molecular sequence and protein sequence into a parallel full-connection network for training, and storing a trained model; and finally, inputting a drug and a target spot which need to be predicted into the model, and calculating a cosine distance as a prediction result. According to the method provided by the invention, the accuracy of predicting the combination of the drug on the target protein is effectively improved, and the discovery process of the drug is promoted.

A molecular structure prediction method based on a multi-granularity graph neural network comprises the steps of data set preprocessing, multi-granularity level data feature construction, multi-granularity graph neural network construction, multi-granularity graph neural network training, multi-granularity graph neural network verification and multi-granularity graph neural network testing. The key connection relation between atoms, the incidence relation of substructure keys and graph-level global feature representation are determined in the molecular graph, and the problem of local information loss in graph representation learning is effectively relieved; a message passing mechanism of graph neural sub-networks with different granularities is improved, a multi-granularity molecular graph data structure is trained, unique information of each hierarchical molecular structure is fully utilized, and the modeling capability of a model for a complex chemical structure is enhanced. The method has the advantages of being high in prediction accuracy, reducing errors, relieving local information loss existing in the prediction method, being good in prediction interpretability and the like, and can be applied to the technical fields of drug discovery, molecular property prediction and the like.

The invention discloses a lead compound optimization method and system based on skeleton constraint and training enhancement, and belongs to the field of artificial intelligence drug discovery. The method comprises the following steps: performing structural treatment on an initial lead compound, extracting a growth skeleton structure, and pairing a protein pocket structure; inputting information of the skeleton structure and the protein pocket structure into a coding model based on a three-dimensional graph neural network for joint coding to obtain structure coding representation; on the basis of a Delete model, an atomic distance-based resampling mechanism and a hydrophobic group mask are introduced, and a molecular generation model is constructed; training and tuning a molecule generation model through staged pre-training and a knowledge enhancement fine tuning strategy so as to improve the structural accuracy, the binding activity and the druggability of generated molecules; and inputting the structure coding expression into a trained and optimized molecular generation model PocketGrow to generate optimized molecules, screening the generated optimized molecules, and outputting a lead compound with development potential.

The invention relates to the technical field of biology, in particular to a composition for preparing a brain organ, the brain organ and application of the brain organ. The invention further discloses a construction method of the brain organ, and on the basis of the method, different stem cells such as ESCs and iPSCs can be utilized to stably construct the three-dimensional brain organ model capable of simulating the medulla oblongata trigeminal nerve. The invention further discloses an organoid assembly which has important application potential in the fields of research on development, functions and related diseases of brain nuclei, drug discovery and the like.

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.

Methods for facilitating drug discovery in various models and proposing single cell-type-specific drug candidates, and predicting cellular drug sensitivities. Predictions comprise obtaining bulk RNA sequence data, wherein bulk RNA seq data is pan-cancer cell line transcriptomic data from one or more sources; obtaining single cell RNA sequence data (scRNA-seq) from one or more sources; integrating the bulk RNA sequence data and scRNA-seq data; capturing similarities between the bulk data and the scRNA-seq data based on a shared drug response relative gene and using canonical correlation analysis; extracting one or more drug-response relevant features and selecting a pharmacogenomic subspace for accurately predicting a single-cell drug response.

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 discloses a high-sensitivity visual detection kit for the activity of human structure-specific nuclease FEN1 and application of the high-sensitivity visual detection kit. A turnup flap structure in FDP is specifically cut by FEN1 to generate a dumbbell-shaped DNA probe with a notch, the notch of the probe is connected under enzyme catalysis to generate a closed annular dumbbell-shaped DNA probe, rolling circle amplification is carried out in the presence of an amplification primer and enzyme, a urease modified signal probe is added into an amplification product, and the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows that the detection result shows. An amplification product and a signal probe complement are separated by virtue of magnetic microspheres and are added into a urease substrate solution, urea in the solution is efficiently catalyzed by urease in the complement to cause change of a pH value, so that the color of a phenol red indicator is changed, and the FEN1 activity is detected. The kit is high in sensitivity, simple and convenient to operate, free of additional instruments and equipment, low in cost and large in application potential, results can be observed by naked eyes, and the kit is used for screening FEN1 inhibitor candidate drugs, and the drug discovery efficiency is remarkably improved.

The invention relates to the technical field of biology, in particular to a composition for preparing organoids and application of the composition. The invention discloses a construction method of a multi-tissue co-differentiation organoid. Based on the method, different stem cells (such as ESCs and iPSCs) can be utilized to stably construct a three-dimensional model for simulating interaction of nerves, muscles and bones. According to the method, the complex operation of separately differentiating multiple tissues and integrating the multiple tissues is avoided, particularly the problem that bone tissues and skeletal muscle tissues are difficult to fuse with other tissues is solved, a co-development regulation mechanism of the multiple tissues can be displayed in vitro, and a convenient supporting platform is provided for researches such as drug discovery.

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.

The present disclosure relates to organoids and particularly to brain organoid models. The brain organoids include neurons, microglia, astrocytes, and blood vessels. The brain organoid models can be used to create models for Alzheimer's Disease. Methods of using the brain organoids for drug discovery are also described.