5190results about "Molecular structures" patented technology

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention discloses a genomics-based Parkinson's disease drug target prediction model construction method, and relates to the technical field of drug research and development, and the method comprises the following steps: collecting genomics, transcriptomics and proteomics data related to Parkinson's disease patients, and carrying out quality control and standardization processing; through differential expression analysis and function enrichment, key genes and signal pathways related to Parkinson's disease are identified, and a potential drug target range is determined. By integrating genomics, transcriptomics and proteomics data of patients with Parkinson's disease, molecular mechanisms related to Parkinson's disease can be comprehensively analyzed, multi-target combination is optimized in combination with the ant colony algorithm, the limitation that a traditional single-target model is difficult to capture complex disease network comprehensiveness is effectively overcome, and the method is suitable for popularization and application. The accuracy of target spot prediction is remarkably improved, a more reliable action target spot is provided for drug research and development, and the failure rate of clinical tests is reduced.

Methods and systems for performing in situ decoding are described that minimize optical crowding, thereby improving decoding accuracy. The methods may comprise, e.g., receiving images of a biological sample acquired during a cyclical decoding process; detecting a series of detectable signals (ON signals) or absence thereof (OFF signals) at one or more locations in the biological sample corresponding to one or more barcoded target analytes; determining a code word based on the series of ON and OFF signals that corresponds to a barcode for each of the one or more barcoded target analytes, where the one or more code words are assigned to the one or more barcoded target analytes based on a minimax decision rule to minimize a density of ON signals detected in the images of the series of images; and identifying the one or more barcoded target analytes based on the one or more determined code words.

The invention relates to the field of drug-target affinity prediction, in particular to a drug-target affinity prediction method based on a multi-scale mixed attention network. According to the method, global residue characteristics can be learned by protein pocket residues by utilizing a self-attention mechanism, so that the global characteristics of the protein are learned. In addition, a cross-modal feature fusion mechanism is utilized, protein and drug structural features are enhanced in sequence features, cross-modal feature fusion in entities is enhanced, meanwhile, a cross-entity interaction module is utilized to recognize key atoms of drug molecules or key residues of protein, and therefore the overall performance of the model is improved. Finally, multi-level protein feature extraction is further introduced to further extract protein features.

The invention relates to the field of drug target interaction, and discloses an application of a multi-modal feature fusion model in drug target binding affinity prediction, which comprises the following steps: S1, obtaining an original data set of drug target binding affinity, and taking a binding affinity index in the data set as a label of a training set and a test set; s2, acquiring representation information of other modals by using the drug sequence and target sequence representation provided in the original data set, preprocessing the acquired representation information of drugs and targets in different modals, and processing the representation information into a model acceptable feature vector form; and S3, respectively storing the multi-modal feature vectors of the drug and the target as well as the binding affinity tags in a form of pytorchsensor (. Pt). According to the method, shallow characterization of the drug and protein sequences is learned by applying the pre-training model, and the problem that a drug target binding affinity data set is insufficient is solved.

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 affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

An apparatus for training a tunable data structure to predict internal ribosome entry site (IRES) activity includes at least a processor and a memory containing instructions configuring the at least a processor to assemble a training set including a plurality of nucleotide sequence data examples IRES sequences and a plurality of correlated observed IRES activity, partition the training set into at least a first section and a second section, train, using the first section at least an activity data structure to generate probable IRES activity using nucleotide sequence data, and iteratively retrain the at least an activity data structure using the second section, wherein each iteration of the iterative retraining includes generating a predicted IRES activity value using the at least an activity neural network and a nucleotide sequence data example, evaluating an error function, and tuning the activity data structure.

The invention discloses a drug and target interaction prediction method based on multi-scale convolution feature fusion, which comprises the following steps: acquiring drug molecule data, target protein data and drug and target interaction data, constructing a drug molecule map according to the drug molecule data, and coding a target protein sequence according to the target protein data; inputting the drug molecular map into a model, and obtaining drug features through a multi-scale map convolutional network and a dynamic gating attention mechanism; inputting a target protein sequence into the model, and obtaining target features through hierarchical cavity convolution and a bidirectional gating cycle unit; through multi-head cross attention, the drug features are aligned with the target features, local and global cross-modal fusion is carried out, and drug target fusion features are obtained; based on the drug target fusion features, outputting a drug and target interaction prediction probability; and training the model according to the drug and target interaction data and the prediction probability, and applying the trained model to drug and target interaction prediction.

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 method for drug screening and disease model construction of liver organoid, relates to the technical field of medical research, and adopts multi-modal data acquisition, fusion and intelligent analysis and quantum computing processing to optimize comprehensive application of data, so that the richness, accuracy and analysis efficiency of the data are remarkably improved, and the risk of drug screening and disease model construction of liver organoid is reduced. Multi-source data of genomics, transcriptomics and the like are collected comprehensively, an accurate research system of immune-hepatic disease interaction based on organoid is constructed, an in-vivo complex immune-hepatic disease microenvironment can be highly simulated, the proportion and activation state of immune cells are accurately regulated and controlled in an in-vitro model, functional and phenotypic changes of the immune cells are monitored in real time, and the immune-hepatic disease interaction accuracy is improved. According to the present invention, the interaction mechanism between the immune cells and the liver organ cells is deeply revealed, such that the accurate information basis is provided for the accurate determination of the drug action target, the development of the innovative drug for the immune check point or tumor microenvironment regulation is easily achieved, and the pertinence and the success rate of the drug research and development are substantially improved.

The invention is applicable to the technical field of biological information, and provides a drug target prediction method based on a graph neural network, which comprises the following steps of: respectively taking a drug and a target node as a core, and automatically extracting local sub-graphs of the drug and the target node to capture local context information of the nodes in a complex biological network; taking different types of edges as feature information and fusing the feature information into a feature learning process, dynamically adjusting contribution degrees of the different types of edges to node representation through an attention mechanism, introducing residual connection and a normalization processing strategy, and fusing node features; and performing deep mining on the fused node features by using a two-layer graph convolutional network, and calculating a potential interaction prediction score between the drug and the target. The invention further provides a drug target prediction model based on the graph neural network. According to the method, independent feature extraction and training are carried out on predicted drugs and target spots, and the accuracy and richness of node representation are effectively improved while the flexibility of the model is kept.

The invention relates to the technical field of biology, and discloses a chemically modified small nucleic acid drug zero sample prediction method and system based on a large language model and multi-view deep learning. The method comprises the following steps: collecting chemically modified RNA data; performing Token conversion by using an SMILES token regular expression, and performing embedding by using a pre-trained large language model; chemical modification position information is obtained and combined with SMILES in an embedded mode; the RNA sequence is embedded; processing the comprehensive embedded vector by using a self-attention mechanism; fusing RNA sequence embedding and comprehensive embedding by adopting a cross attention mechanism; performing feature extraction and dimension reduction by using a three-dimensional convolutional neural network; and inputting the features into a full-connection layer to predict the drug efficiency. According to the method, high-precision zero-sample prediction of novel chemical modification is realized, the generalization ability and prediction precision of the model are remarkably improved, and a flexible, efficient and accurate method is provided for design and development of RNAi drugs.

The invention provides a method for quantitatively analyzing and comparing beta-folded structures in silk fibroin materials based on machine learning, and relates to the field of material characterization technologies and biomedical materials. The method comprises a small-angle X-ray scattering detection experiment and machine learning model analysis based on big data. The SAXS experiment needs to be carried out on equipment meeting the detection sensitivity requirement, and scattered light intensity I data is obtained; after q-I data of an SAXS experiment of an unknown silk protein material is input into the machine learning model, the shape and the feature size of a beta-folding structure are output. The method provided by the invention has higher convenience; sample pretreatment is not needed, and a statistical result of structural characteristics in a sample can be visually reflected; the lt can be reflected; according to the method, the light scattering result is analyzed innovatively through a machine learning method according to the nano-structure information within the scale range of 500 nm, and the detailed structure information of the material is deduced more effectively, so that the material design is optimized.

A method for predicting one or more features of an antigen-binding protein is provided, comprising: providing the amino acid sequence of at least part of one or more chains of the antigen-binding protein or an encoded sequence derived therefrom as input to a deep learning model comprising a sequence encoding module configured to take as input an amino acid sequence and to produce as output an embedding corresponding to the input sequence, and predicting the one or more features of the antigen-binding protein using the embedding produced by the sequence encoding module The sequence encoding module has been trained as part of a machine learning model comprising a structure encoding module, and using training sequence data and structure data from a plurality of antigen-binding proteins. Related methods, systems and products are also described.

The invention discloses a drug target interaction prediction method based on a protein sequence. The method comprises the following steps: constructing a drug target database; constructing a target feature extractor based on a pre-trained protein language model; constructing a drug feature extractor based on a pre-trained molecular model; constructing a feature learning method by adopting a self-attention mechanism, constructing a drug target interaction learning method by adopting a cross attention mechanism, constructing a predictor in combination with a linear layer, and constructing a prediction drug target interaction model; training and optimizing parameters of the drug target interaction prediction model to obtain an optimized drug target prediction model; inputting a drug target into the optimized prediction model, outputting a prediction result, and finally marking potential active sites and important amino acid residues of the drug and the target through attention score visualization. According to the method, the precision of predicting the interaction between the drug and the target spot based on the protein sequence is improved, and the interpretability of the deep learning model is realized.

The invention relates to the technical field of test optimization, in particular to a biomedical test optimization system based on a graphic model, which comprises a data collection module, a graphic weight adjustment module, a path exploration module and an optimization adjustment module. According to the method, the drug concentration and the reaction data of the biomarker are synchronously integrated through real-time data, the whole test process can be covered, real-time updating and accurate mapping of the data can be ensured, the reliability and effectiveness of the test are greatly enhanced, and the dynamic data can be obtained through updating the weights of nodes and edges in a graphic model in real time. The model can accurately reflect the actual interaction effect between drugs, path analysis and pharmacological action relation marking are carried out by utilizing updated data, the pertinence and scientificity of the test are further enhanced, key variables and action paths can be quickly identified, a drug combination scheme is optimized, the research and development efficiency and cost control are remarkably improved, and the research and development efficiency is improved. The research and development period of the medicine is greatly shortened.

Provided are a protein language model training method, a method for extracting an amino acid sequence representation by using a protein language model, a method for obtaining a new amino acid sequence by using a protein language model, a method for obtaining a related amino acid sequence by using a protein language model, a method for predicting an amino acid sequence perplexity by using a protein language model, an electronic device, a computer-readable medium, and a program product. The protein language model training method comprises: during a training process, executing at least one training sub-task amongst two training sub-tasks included in a first type of training task and a second type of training task; and, on the basis of a first loss value corresponding to the first type of training task and a loss value corresponding to the second type of training task, adjusting parameters of a protein language model to obtain a trained protein language model.

The invention discloses a nucleic acid binding protein recognition method based on a protein map and a protein language model, and belongs to the technical field of bioinformatics. According to the method, high-dimensional sequence features extracted by a ProtT5 protein language model are combined with three-dimensional structure information predicted by AlphaFold, so that the model can capture rich feature information of the sequence and the structure at the same time, and the accuracy and reliability of NABPs prediction are remarkably improved; the expression capability of sequence features is enhanced through the GAT network and the BiLSTM network, so that the constructed prediction model can more comprehensively understand important information in the protein sequence, and the prediction performance of NABPs is improved; the features output by the GAT network and the BiLSTM network are fused, and NABPs classification is performed through the MLP, so that the prediction model can make full use of feature information from different sources, and the NABPs prediction performance and robustness are improved.

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

The invention 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 method and a system for screening and constructing a small molecule polypeptide simulant based on interaction between proteins, relates to the technical field of biology, and comprises the following steps: predicting an interaction model of RECK protein and ADAM10 protein through a molecular docking platform, and analyzing interaction sites of the RECK protein and the ADAM10 protein. And then, determining a KAZAL region of the RECK protein according to the interaction site, and predicting a sequence structure of the KAZAL region. And then carrying out molecular docking on the KAZAL region and ADAM10 protein, designing a plurality of peptide fragments with the length of 30 amino acids, and screening the peptide fragment sequence with the highest score as candidate polypeptide. And finally, performing species homology comparison and quality control detection on the candidate polypeptide, and verifying the binding capacity and biological function of the candidate polypeptide to ADAM10 protein through cell experiments. The invention provides a method for screening to obtain a polypeptide simulant specifically bound with ADAM10 protein. The polypeptide simulant can be used for treating ADAM10 related diseases.

The invention discloses a drug-target interaction prediction method and device based on multi-view feature fusion, and a medium, and the method comprises the steps: (1) extracting drug features: firstly generating a drug molecular map, constructing a plurality of sub-map structures through the extraction of molecular fragments, and carrying out the coding through a map encoder, thereby obtaining the drug features; (2) extracting target features: firstly, extracting a protein pocket of the target, dividing the protein pocket into a plurality of sub-pockets to obtain a plurality of sub-graph structures, and encoding the sub-graph structures by using a graph encoder to obtain pocket features of the target; in addition, a self-attention module is used for extracting sequence features of the target, and cross attention is used for fusing the pocket features and the sequence features to obtain target features; and (3) fusing the drug features and the target features by using bilinear attention, and calculating the probability of interaction between the drug and the target. According to the method, the biochemical attribute characteristics in the drug molecules and the target protein structure can be effectively excavated, and the prediction accuracy of the drug-target interaction is improved.

A method of predicting protein functions and interactions, including identifying a first protein and a second protein for analysis, generating a first embedded representation of a first amino acid sequence of the first protein and a second embedded representation of a second amino acid sequence of the second protein, generating a first contact map prediction for the first protein based on the first amino acid sequence and a second contact map prediction for the second protein based on the second amino acid sequence, generating first physiochemical features associated with the first protein, and second physiochemical features associated with the second protein, and generating a prediction score for a protein function or interaction between the first protein and the second protein based on the first embedded representation, the second embedded representation, the first contact map prediction, the second contact map prediction, the first physiochemical features and the second physiochemical features.

Systems and methods described herein include embodiments for generating a geometrical structure of a binding complex formed between a plurality of macromolecules, comprising: processing an input representation comprising a plurality of representations of the plurality of macromolecules to generate a geometry prior; sampling an initial geometrical structure of the binding complex based on the geometry prior; and processing, using a neural network, the initial geometrical structure to generate the geometrical structure of the binding complex formed by the plurality of macromolecules.

Disclosed herein is a method and system to generate 3D models of small molecules. The methods and systems are disclosed for an equivariant shape-based virtual screening operation that generates small molecule 3D structures that fit into and bind to a provided protein pocket. The model is configured to learn from existing 3D structures of small molecules in a data-driven manner using forward diffusion and backward diffusion processes to learn and represent the major characteristics of molecule 3D structures. Additional disclosure relates to the model that is further configured to learn from the 3D model of the protein pocket.