3884results 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 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.

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 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.

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 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.

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 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.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for embedding perturbation data via a machine learning model and filtering, aligning, and aggregating the embeddings to generate a genome-wide perturbation database for real-time generation of perturbation heatmaps. In particular, in one or more embodiments, the disclosed systems can receive a plurality of perturbation images portraying cells from a plurality of wells corresponding to a plurality of cell perturbations. Further, the systems can generate, utilizing a machine learning model, a plurality of well-level image embeddings from the plurality of perturbation images. Moreover, the systems can align, utilizing an alignment model, the plurality of well-level image embeddings to generate aligned well-level image embeddings. Additionally, the systems can aggregate, according to perturbations of one or more perturbation experiments, the well-level image embeddings to generate perturbation-level image embeddings. Furthermore, the systems can generate perturbation comparisons utilizing the perturbation-level image embeddings.

The invention relates to the technical field of protein prediction, and discloses a protein interaction prediction method based on cross-modal enhanced representation learning. According to the method, a Convform module and a Graphform module are designed, and a global attention mechanism is introduced into each module, so that the limitation that only local features or global features are concerned in an existing method is broken through. According to the design, the model can capture the local mode and the global dependency relationship of the protein at the same time, and more comprehensive protein representation is provided. The invention provides a cross-modal enhancement method based on comparative learning. The problem of difference between a sequence mode and a structural mode is solved through an explicit mode alignment process. The method not only can maintain unique information of each mode, but also can give full play to complementarity among the modes, so that richer protein expression is obtained. The cross-protein fusion module provided by the invention can more accurately capture essential characteristics of protein interaction, and the accuracy of PPI prediction is remarkably improved.

The invention discloses a molecular binding prediction model training method and device, equipment and a storage medium. Relates to the technical field of artificial intelligence. The method comprises the following steps: performing feature extraction on a sequence of a first biomolecule sample through a molecular binding prediction model to obtain a first feature; performing feature extraction on the sequence of the second biomolecule sample through a molecule combination prediction model to obtain a second feature; obtaining an interaction feature based on the first feature and the second feature through a molecular binding prediction model; obtaining a combination probability based on the first feature, the second feature and the interaction feature through a molecular combination prediction model; and adjusting parameters of the molecular binding prediction model based on the binding probability to obtain a trained molecular binding prediction model. By means of the method, the molecular binding prediction model can learn the interaction relation between the sequences of the biomolecules, and then the binding probability between the biomolecules can be predicted more accurately.

The invention provides a drug target prediction method based on a 3D structure and a multilevel attention mechanism, and belongs to the technical field of drug target prediction. The technical problem to be solved is to provide a drug target prediction method based on a 3D structure and a multi-level attention mechanism. According to the technical scheme, the method comprises the following steps: collecting data information of an interaction tag among a related drug, a target sequence and a drug target protein, and processing and extracting SMILES character string characteristics of the drug; learning the numeralized protein amino acid sequence by using a convolutional neural network, completing feature coding of a target spot on the sequence through multi-layer convolution and pooling operation, and extracting target spot sequence feature data; a PDB file is read and modeled into a pocket graph, feature calculation is carried out on the pocket graph, a final protein structure representation code is generated, and structural feature data of target protein is extracted by modeling the PDB file; the method is applied to drug target prediction.

A method for identifying a peptide fragment bound to a class MHC-I / HLA-I and recognized by TCR, a device, and a storage medium. The method comprises: acquiring a protein sequence, an MHC-I / HLA-I sequence, and a peptide fragment sequence; using the protein sequence to train a protein sequence tokenizer; using the protein sequence tokenized by the protein sequence tokenizer to train a protein characterization model, and obtaining a trained protein characterization pre-trained model; using the MHC-I / HLA-I sequence and the peptide fragment sequence tokenized by the protein sequence tokenizer to fine-tune the trained protein characterization pre-trained model, and obtaining a binary classification model; using the binary classification model to perform identification processing on a target wild-type / mutant peptide fragment, classifying the mutant peptide fragment, and acquiring a candidate neoantigen mutant peptide positively correlated with TCR recognition.

The invention relates to a beta secretase inhibitor molecular activity prediction method and system based on SMILES sequence and graph information fusion learning thereof, and belongs to the field of inhibitor molecular activity prediction. The method comprises the following steps: firstly, designing a BiLSTM-Transformer framework with an enhanced layered attention mechanism, and coding a functional subsequence derived from an SMILES character string of a beta secretase inhibitor molecule; functional subsequences are extracted from SMILES character strings and are divided into multiple levels on the functional group level, the ion level and the atom level, and understanding of molecular structure details can be enhanced. Secondly, a graph coding model with an atomic-scale feature attention mechanism is designed based on a molecular graph structure generated from an SMILES character string, so that key atoms and chemical bonds in the molecular graph structure are highlighted, and the feature representation capability of the molecular graph is further enhanced. And finally, through a global attention mechanism and a weighting module, carrying out fusion learning on molecular information of different modes to obtain a prediction result of the molecular activity of the beta-secretase inhibitor.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

According to the intelligent training method, the prediction method, the equipment and the medium for the pharmacokinetic properties of the polypeptide drugs, based on the prediction model obtained through training, the pharmacokinetic properties of at least two polypeptide drugs can be predicted, and compared with a traditional experiment method, the intelligent training method has the advantages of being efficient, accurate and comprehensive in effect and high in practicability. And the comprehensive systematic prediction platform can promote the unification of model data standards developed by different research teams, and effectively ensures the actual value of the polypeptide drug in early screening and optimization.

The invention relates to the technical field of bioinformatics, in particular to a method for identifying an elncRNAs regulation target gene in a single cell based on single cell ATAC-seq. According to the method provided by the invention, a regulatory network of an elncRNAs-target gene on a single cell whole genome is identified by utilizing chromatin co-accessibility based on scATAC-seq data. Meanwhile, the method is applied to immune cell differentiation, and the elncRNAs-target gene regulation dynamic state of immune cell differentiation is accurately recognized. In addition, the biological functions of the elncRNAs in different cell types are further analyzed. The method provided by the invention can help related researchers in the field of biomedicine to more elaborately and accurately predict the regulation effect and function of the elncRNAs under the single cell resolution.

A system is disclosed for encoding a data string of a first modality into a hierarchical tokenized representation for processing by a text-based deep neural network (DNN) trained on a second modality. The data string comprises multiple units, each having one or more attributes. Each attribute is represented in the tokenized string as a sequence of hierarchical tokens, with a first hierarchical token encoding one or more most significant bits and a subsequent hierarchical token encoding one or more less significant bits. The DNN processes the data string bidirectionally, across the sequence of units and within the token hierarchy, to select tokens that capture attribute information. The selected hierarchical tokens output by the DNN from a representation of the original data string that preserves attribute detail while enabling cross-modal processing using models trained on text.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

The invention discloses a small molecule ligand drug screening method and system based on affinity prediction, and belongs to the technical field of biological medicine. The invention aims to solve the technical problem of low drug screening precision caused by molecular expression limitation, geometric invariance deficiency and insufficient multi-modal information fusion when virtual drug screening is carried out by using protein-ligand affinity. Comprising the following steps: acquiring ligand and protein structure information, and preprocessing to obtain coordinates and a feature matrix of ligand / pocket / residue; performing comprehensive representation, multi-feature flow self-adaption, geometric algebraic multi-layer perception and feature alignment processing on the feature matrix to obtain corresponding feature space representation; performing cross attention fusion and multi-scale interactive learning processing on the feature space representation in sequence to obtain fusion features; inputting the fused features into a multi-scale interactive learning module, and outputting final features; and finally, predicting the binding affinity of the ligand and the protein according to the fusion characteristics to obtain a binding affinity value.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.