299 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.

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.

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.

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 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 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.

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.

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.

The invention provides a compound vector database construction method and a compound similarity search method and device, which are used for cleaning, structurally coding and vectorizing compound structure data of a plurality of data sources, and forming a uniform feature vector for each compound according to molecular basic properties and structural attributes of the compound. And storing the feature vectors through a vector database and establishing an index to obtain a compound vector database. In the application process, structural coding and vectorization are firstly carried out on structural data of compounds needing to be retrieved, then a compound vector database is searched, a plurality of most similar compounds are output to serve as retrieval results, rapid and accurate retrieval of the structures of the compounds in a large-scale compound database can be achieved by means of indexes, and the retrieval efficiency is improved. The method has wide application prospects in the fields of drug discovery, material design and the like, and provides powerful tool support for researchers.

According to the liquid drop sorting instrument based on fluorescence detection, an inverted microscope serves as a whole instrument base and is located at the bottom, an XY precise moving platform is fixed to the microscope, a z-axis moving platform is fixed to a simple supporting frame beside the microscope, a fluorescence channel is installed on the z-axis moving platform, and a microfluidic liquid drop sorting module is fixed to the tip end of the fluorescence channel. A fluorescence detection module is arranged on the side wall of the fluorescence channel, the fluorescence detection module and the microfluidic liquid drop sorting module are both connected with the signal end of the FPGA signal processing module, the fluorescence detection module and the microfluidic liquid drop sorting module are controlled through the FPGA, the condition of generated liquid drops is observed through a microscope, the sorting process of the liquid drops is dynamically adjusted, and the quality of the liquid drops is improved. The closed-loop control of fluorescent liquid drop sorting is realized; according to the invention, high-precision and high-throughput liquid drop sorting can be realized, the liquid drop screening efficiency is greatly improved, the target liquid drop can be accurately identified and selected, and the method can be widely applied to the fields of high-throughput screening, single cell analysis, enzyme evolution, drug discovery and the like.

Embodiments of this disclosure provide a method and apparatus for drug design, a device, a medium, and a program product. The method for drug design includes: obtaining protein data representing a three-dimensional structure of a protein and initial molecule data representing an initial molecule to be bound to the three-dimensional structure of the protein. The method further includes: determining first molecular fragment data representing a first molecular fragment in the initial molecule based on the protein data and the initial molecule data. Generating target molecule data representing a target molecule based on the first molecular fragment data and the initial molecule data. A molecular fragment is automatically determined in the initial molecule, and the initial molecule is optimized based on the determined molecular fragment, such that fragment-based artificial intelligence optimization of a drug molecule can be implemented in a targeted manner, thereby reducing time and labor costs of drug discovery.

In one embodiment, a method includes accessing a biomolecule representation of a first biomolecule and processing the biomolecule representation by a machine-learning model trained using sequencing time-series data. The sequencing time-series data was obtained from directed evolution of a population of biomolecules over multiple enrichment rounds where the population of biomolecules in each enrichment round was a unique set of biomolecules with respect to each other enrichment round. The sequencing time-series data for each enrichment round comprises a biomolecule frequency of each biomolecule of the population of biomolecules in the respective enrichment round. The training comprises learning inferred fitness scores of the population of biomolecules for each enrichment round by predicting biomolecule frequencies of the population of biomolecules in the respective enrichment round given biomolecule frequencies of the population of biomolecules in prior enrichment rounds. The method further includes outputting an inferred fitness score for the first biomolecule.

The application provides a drug virtual screening method and device based on deep learning, wherein the method comprises the following steps: inputting all candidate compound small molecules in a candidate molecule database into a pre-trained molecular encoder respectively to obtain molecular vectorization representation of the candidate compound small molecules; constructing an index structure corresponding to the candidate compound small molecules based on the molecular vectorization representation of the candidate compound small molecules; inputting a protein target to be matched into a pre-trained protein target encoder to obtain protein target vectorization representation corresponding to the protein target; and matching the protein target vectorization representation based on the index structure to obtain a target compound small molecule corresponding to the protein target. The method establishes a full-amount mapping function of the target and the molecule through a vector calculation mode, realizes high-throughput virtual screening in a second-level calculation time, realizes rapid virtual screening of a full-amount candidate molecule library, improves the precision of the virtual screening, and increases the possibility of drug discovery.

Embodiments of the present disclosure provide a molecular property prediction method, related device and medium. The method splits and recombines a first molecule in an unlabeled molecule dataset to obtain a new second molecule, determines a training sample based on the first molecule and the second molecule, and optimizes a molecular encoder using the training sample. Then, the molecular property prediction model based on the optimized molecular encoder is trained using a labeled molecule dataset to achieve accurate prediction of the molecular property. Embodiments of the present disclosure can fully utilize the substructure information inside the molecule to improve the accuracy and generalization ability of the prediction, optimize the molecular representation ability of the molecular encoder, and enable the target molecular encoder to better understand the combination relationship between the molecular fragments, thereby improving the representation quality and prediction accuracy of complex molecular structures. Embodiments of the present disclosure can be applied to drug discovery, material science, molecular virtual screening and other scenarios.

The invention belongs to the technical field of pharmaceutical analysis, and particularly relates to a method for separating active components in tobacco based on liquid-phase segmented separation, SPR, affinity chromatography enrichment and mass spectrometry detection, a product and application. According to the method, liquid chromatography separation, SPR detection screening, affinity chromatography enrichment and mass spectrum identification technologies are fused, and active small molecules are screened and enriched from a complex system by virtue of high sensitivity of SPR and accuracy of mass spectrum. The method comprises the following steps: firstly, carrying out liquid chromatography segmented separation on extract components to improve SPR detection precision and affinity enrichment efficiency; by means of specific binding of Ni Sepharose FF (NTA) resin and a His tag, more target proteins are fixed, and the micromolecule enrichment effect is further enhanced; and finally, identifying the molecular structure by using mass spectrum, and locking the bioactive small molecule combined with the target protein. According to the strategy, efficient enrichment of small molecule ligands is achieved, and a new way is provided for early-stage development of small molecules in drug discovery. 6.

The present invention relates to the field related to artificial intelligence and drug discovery. Specifically, a drug-drug interaction (DDI) prediction method based on RGDA-DDI is invented to solve the problem that the existing DDI prediction methods are not ideal. The method is divided into a data encoding module, a feature fusion module and a prediction module. The data encoding module is composed of multiple Residual-GAT submodules, each of which is composed of a graph attention layer, a Normalize layer and a SAGPooling layer, and is used to extract multi-scale features of the input drugs; the feature fusion module is composed of two dual-attention mechanism submodules, which are used for multi-scale drug feature fusion; finally, the fused features are input into the prediction module for prediction. This method overcomes the shortcomings of the existing DDI prediction methods, which lack modeling of multi-scale drug features and lack of mining of potential drug pairs (DDP) features. Experimental verification shows that the prediction performance of this method is better than the recently disclosed drug-drug interaction prediction method.

The present disclosure relates to the field of G protein coupled receptor (GPCR) structural biology and signaling. In particular, the present disclosure relates to binding domains directed against and / or specifically binding to GPCR:G protein complexes. Also provided are nucleic acid sequences encoding such binding domains and cells expressing or capable of expressing such binding domains. The binding domains of the present disclosure can be used as universal tools for the structural and functional characterization of G-protein coupled receptors in complex with downstream heterotrimeric G proteins and bound to various natural or synthetic ligands, for investigating the dynamic features of G protein activation, as well as for screening and drug discovery efforts that make use of GPCR:G protein complexes.

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.

The present disclosure relates to methods and compositions for generating topographically organized tissues in vitro, for the resulting cultured tissue and components thereof, and for uses of such cultured tissue and its components in drug discovery, toxicology studies, and therapy.