90 results about "Molecular Fingerprint" patented technology

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 response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

The invention relates to the technical field of classified storage of biological data, in particular to a classified storage method and system for biological cell data. The method comprises the following steps: acquiring multisource cytomics data; performing data standardization on the multisource cytomics data to obtain standard cell characteristic data; performing characteristic matrix conversion on the standard cell characteristic data to obtain a cell characteristic digital matrix; performing molecular fingerprint construction on the biological cells based on the cell characteristic digital matrix to obtain cell molecular characteristic fingerprint data; performing hierarchical classification on the standard cell characteristic data to obtain a cell phenotype classification system; performing hierarchical labeling on the standard cell characteristic data according to the cell phenotype classification system to obtain cell phenotype labeling data; and performing ontology mapping on the cell phenotype labeling data to obtain a cell phenotype relationship network. According to the method, refined classification, dynamic updating and efficient storage of the biological cell data are realized.

The invention discloses a deep learning method and device for evaluating the carcinogenic risk of a compound based on multi-modal data fusion and a storage medium, and belongs to the technical field of chemical health risk evaluation, and the method comprises the steps: (1) building a carcinogenicity prediction data set; (2) constructing a cross-modal knowledge graph, and learning the cross-modal knowledge graph by using a convolutional neural network to obtain cross-modal feature representation; (3) extracting a molecular fingerprint feature representation and a molecular map feature representation, and fusing the molecular fingerprint feature representation and the cross-modal feature representation to obtain a fused feature representation; (4) a fusion model is constructed, a first deep neural network and a second deep neural network in the fusion model are used for processing different feature representations, and a classifier is used for prediction; (5) carrying out supervised training on the fusion model to optimize parameters of the fusion model; and (6) performing carcinogenic risk assessment on a to-be-detected compound by using the optimized fusion model. The method can comprehensively and accurately evaluate the carcinogenic risk of the compound and explain the carcinogenic mechanism.

The present disclosure relates to systems and methods for chemical toxicity prediction. The methods of the present disclosure comprise: receiving test data from an analysis instrument; selecting a candidate chemical according to the test data; determining a hazard translated level and a hazard evaluation level of the candidate chemical according to a molecular fingerprint of the candidate chemical; and predicting the toxicity of the candidate chemical by using a quantitative structure-activity relationship (QSAR) model based on the hazard translated level and the hazard evaluation level.

The invention discloses a molecular fingerprint prediction method and system based on CNN (Convolutional Neural Network) and Transform network feature fusion. The method comprises the following steps: firstly, acquiring mass spectrum data, precursor ions and SMILES of a compound to form a data set, and dividing the data set into a training set and a test set; constructing a fusion model comprising a Transform feature extraction model, a CNN feature extraction model and a fusion network, and training and testing the fusion model; and finally, predicting molecular fingerprints by using the trained and tested model. The Transform model is responsible for extracting global molecular fingerprint features, the CNN model is used for extracting local molecular fingerprint features, and the fusion network fuses the global molecular fingerprint features and the local molecular fingerprint features and then performs MLP prediction. According to the method, molecular information is comprehensively captured from different overall and local perspectives, the defect of a single model in feature extraction is overcome, and the precision of molecular fingerprint prediction is remarkably improved.

The invention relates to a molecular property prediction method and system based on multi-task pre-training and multi-modal fusion. The method comprises the following steps: collecting a data set and performing molecular conversion; performing multi-task pre-training, which comprises the following steps: generating a heterogeneous enhanced view; constructing a pseudo label; performing comparative learning according to the structure enhanced view and the heterogeneous enhanced view, and constructing a maximum similar task; capturing semantic differences among molecules according to the pseudo labels; carrying out multi-modal fusion, namely introducing functional group structure information, and extracting molecular sequence characteristics based on Transform and Mamba2 to obtain fused molecular multi-modal representation; and analyzing and predicting the classification or regression task. A heterogeneous enhanced view is established in a multi-task pre-training stage, multi-task self-supervision is performed in combination with multi-granularity features of molecular fingerprints, and functional group structure information is introduced in a multi-modal fusion stage, so that deep cross-modal interaction is realized, downstream prediction performance is improved, and candidate drug screening and molecular property evaluation processes are accelerated.

The invention relates to the technical field of artificial intelligence drug research and development, in particular to a molecular ADMET property prediction algorithm based on multi-mode and multi-scale characteristics. According to the algorithm, multi-scale feature extraction is carried out on a small molecule SMILES character string by constructing a multi-modal feature fusion framework, and training and prediction are completed on 73 ADMET properties covering absorption, distribution, metabolism, excretion, toxicity and general characteristics. The invention provides a multi-modal and multi-scale feature fusion strategy, atomic features, molecular fingerprint features and physicochemical property features are integrated, and a multi-scale feature set covering molecules is formed. A progressive information extraction architecture is combined with a graph neural network (GCN), a gated loop unit (GRU) and an attention mechanism to extract features, so that the accuracy of ADMET property prediction is remarkably improved. In a word, the method marks an important step for predicting the ADMET property of the medicine.

The invention discloses a chemical active structure identification method based on a Morga fingerprint and an optimized Shapley value. The method comprises the following steps of: generating a feature matrix for a training data set for a model established by using Morga molecular fingerprints as features, loading a model weight by taking the feature matrix as an explanation background, explaining the model by using an SHAP method, generating a Shapley matrix, searching and deleting an interference column, finding a substructure which appears at low frequency and is highly related to an activity end point of a chemical through definition of an activity index, and carrying out chemical activity detection on the substructure. Highly active molecular fragments are found.

The invention provides a new pollutant classification prediction method and a related device, and relates to the technical field of new pollutant classification. Comprising the following steps: collecting chemical information (molecular structure, molecular fingerprint and physicochemical property) of a target pollutant and carrying out standardization treatment; preliminary classification is calculated based on Tanimoto similarity; simulating a migration path and a conversion behavior of the pollutants in the environment by using a graph neural network; multi-mechanism comprehensive evaluation is carried out by comprehensively considering durability, mobility and biological accumulation; introducing an environmental factor correction model, and correcting scores according to parameters such as temperature, pH value and dissolved oxygen concentration; integrating the molecular structure, the migration path, the mechanism characteristics and the environment correction result, and calculating a final classification prediction value; and dividing risk levels (high, medium and low risks) according to predicted values. Pollutant risks are comprehensively assessed through a machine learning technology, the assessment precision and reliability are improved, and a scientific basis is provided for pollution prevention and control.

The invention provides a cell-targeted drug preliminary screening model training method, a drug preliminary screening method and equipment, and the method comprises the steps: obtaining molecular fingerprint feature data corresponding to each compound sample with label information to form a corresponding original data set, and carrying out the data screening of the original data set in a distance measurement mode, and training a regression model based on a graph neural network to learn the structural topology of each compound sample, and training the model as a cell-targeted drug preliminary screening model for predicting whether the compound has potential cell-targeted drug activity or not. According to the application, the comprehensiveness of molecular characterization can be effectively improved in the training process of the cell-targeted drug preliminary screening model, and the generalization ability of the trained cell-targeted drug preliminary screening model can be effectively improved, so that the application universality of the trained cell-targeted drug preliminary screening model can be effectively improved; and the accuracy and the reliability of a potential cell targeted drug activity prediction result predicted by the model can be improved.

The invention relates to the technical field of mine water disaster prevention and control, in particular to a mine water disaster treatment slurry stone body recognition system and method based on an infrared spectrum. According to the technical scheme, the mine water disaster treatment slurry stone body recognition system and method based on the infrared spectrum comprises a rock debris preprocessing and conveying control module, a spectral data acquisition and control module and an intelligent recognition and output control module; by arranging the rock debris conveying and preprocessing module, continuous receiving and rapid preparation of rock debris are achieved, meanwhile, the near infrared spectrum detection module conducts spectrum scanning on flowing rock debris above a conveying belt and captures molecular fingerprint information, and generated spectrum data are transmitted to the data processing and intelligent recognition module; the recognition result is synchronously fused with parameters such as drilling depth and pumping pressure, a dynamic slurry diffusion map is generated, the recognition process and the drilling process are kept synchronous, the distribution condition of slurry stone bodies at different depths can be visually seen, and therefore the real-time performance of slurry stone body recognition is effectively improved.

The invention belongs to the technical field of bioinformatics and deep learning, and particularly relates to a DNA methylation site prediction method, system and device and an electronic medium, and the method comprises the following steps: respectively coding single bases in a DNA sequence into four molecular fingerprints, and constructing each base into a molecular map structure; performing high-order feature extraction on the four molecular fingerprints through a multi-layer perceptron to generate molecular fingerprint features; modeling the molecular graph structure by adopting a multi-head graph attention network, calculating and normalizing attention scores among nodes, carrying out weighted summation on neighbor node features, and carrying out multi-head attention fusion to generate molecular graph features; splicing the molecular fingerprint features and molecular map features, and inputting the spliced molecular fingerprint features and molecular map features into a deep convolution gated channel attention module to generate fused features; and obtaining a prediction probability of the fused features, and judging whether the features are methylation sites or not according to the prediction probability. According to the invention, the identification capability of methylation sites can be improved.

The invention relates to the technical field of data processing, in particular to an active cliff data set construction method and device, electronic equipment and a storage medium, and aims to improve the accuracy and consistency of active cliff recognition. The method comprises the following steps: for each molecule which is in a pre-constructed molecular data set, can be combined with the same biological target spot and is subjected to biological activity measurement under the same experimental condition, obtaining the similarity between molecular fingerprints of every two molecules in each molecule, and analyzing the similarity of every two molecules on substructures to obtain the bioactivity of the molecules. Selecting a matching molecule pair meeting a preset similarity evaluation condition; for each biological target spot, selecting a similarity as a molecular activity cliff threshold value corresponding to the biological target spot according to distribution conditions of the corresponding matched molecule pairs under different similarities, and selecting a target molecule pair from the molecules according to the molecular activity cliff threshold value; and constructing an activity cliff data set based on the biological activity parameter difference corresponding to each selected target molecule pair.

The invention belongs to the technical field of organic matter screening, and particularly relates to an optimal selection method of an alternative standard substance in a standard-sample-free substance semi-quantitative process. The optimal selection method comprises the following steps: acquiring response factors and various molecular fingerprints of the standard substance; for various molecular fingerprints of each standard sample, respectively calculating the similarity between each standard sample and other standard samples by using a plurality of similarity calculation methods, and selecting other standard samples with the highest similarity as substitutes according to an obtained similarity matrix; performing error evaluation to obtain an optimal circular fingerprint type, an optimal structural key fingerprint type, an optimal topological fingerprint type and an optimal similarity calculation method; generating a composite fingerprint; and evaluating response factor errors of the optimal structural key fingerprints, the circular fingerprints, the topological fingerprints and the composite fingerprints, and preferably selecting molecular fingerprints with the minimum errors and a similarity calculation method. According to the method, quantitative errors of different molecular fingerprint types representing chemicals and a similarity calculation method are evaluated, so that the effect of minimizing the semi-quantitative error of the standard-sample-free substance is achieved.

This invention provides a system and program that facilitates the process of generating and / or visualizing molecular networks for library molecular structure content. [Solution] In system 500, the molecular network generation system 502 for library molecular structure content includes an evaluation component 512 that performs a comparison between a first molecular fingerprint including first molecular structure data of a first molecular structure and a second molecular fingerprint including second molecular structure data of a second molecular structure, and a visualization component 516 that generates display data that visualizes a representation of the structural similarity score obtained from the first molecular structure, the second molecular structure and the comparison. The representation by the visualization component includes edges corresponding to the structural similarity score that extend between pairs of nodes corresponding to the first molecular structure and the second molecular structure.

The present invention provides a method for predicting drug-target interactions by integrating multi-layer drug structure information. First, the drug and target information in the pharmacomics database is preprocessed to extract the drug and target information with interactions; secondly, the drug SMILES molecular fingerprint is represented as a molecular graph structure, and the molecular completion graph convolutional neural network and the Transformer network are used to extract drug feature information; then, the target sequence information is processed using a convolutional neural network, and the target feature information is extracted; finally, the extracted drug feature information and target feature information are sent to a classification model for training, the model is saved, and the relationship between the drug and the target is predicted. The present invention effectively extracts the feature information in the drug molecular structure, has a higher accuracy rate when predicting the drug-target relationship, improves the efficiency and accuracy of drug-target relationship verification, effectively shortens the drug research and development cycle, and greatly reduces the cost of new drug research and development.

The invention provides an ISSR-PCR reaction system for identifying kiwi fruit varieties with different colors and a method and application of the ISSR-PCR reaction system, and belongs to the technical field of molecular markers. The ISSR-PCR reaction system for identifying the kiwi fruit varieties with different colors is as follows: the DNA template is 70ng / mu L, the primer is 1.0 mu mol / L, the Mg < 2 + > is 2mM, the mix is 10mu L, and the remaining ddH2O is prepared into 20 mu L of the system. The amplification procedure is as follows: the pre-denaturation temperature is 94 DEG C, and the time is 5 minutes; the denaturation temperature is 94 DEG C for 30 seconds; the annealing temperature is set according to different primer conditions, and the time is 50s; the extension temperature is 72 DEG C, and the extension time is 1 min; circulation is performed for 35 times; and then extending at 72 DEG C for 7 minutes, and storing at 10 DEG C. The system is used for amplifying 42 kiwi fruit germplasm resources with different colors, the amplification result shows that the established ISSR-PCR reaction system is good in repeatability and high in stability, and the established ISSR molecular fingerprint spectrum can be used for variety identification.

The present invention discloses a drug molecular property prediction and classification method and system based on BiLSTM; wherein the method includes: obtaining a drug molecule to be predicted, and converting the molecular structure of the drug analysis to obtain a drug molecular descriptor and a drug molecular fingerprint; inputting both the drug molecular descriptor and the drug molecular fingerprint into a trained drug molecular property prediction and classification network, and outputting a drug molecular property prediction result; the working principle of the trained drug molecular property prediction and classification network includes: respectively extracting features from the drug molecular descriptor and the drug molecular fingerprint to obtain two drug molecular feature vectors; performing feature fusion on the two drug molecular feature vectors; performing classification prediction on the fused feature vectors, and outputting a drug molecular property prediction result.

The compound molecular fingerprint prediction belongs to the technical field of mass spectrum data analysis, and from the perspective of fully mining the implicit information of mass spectrum data, neutral loss information is added behind corresponding ion peaks when a vector representation of mass spectrum data is constructed, natural language processing technology is adopted to learn the relationship between peaks and between peaks and neutral losses in the mass spectrum, a multi-dimensional spectrum vector is constructed, and molecular fingerprints are predicted. Due to the addition of neutral loss of corresponding ion peaks in the spectrum vector, more abundant structural information is contained, and the accuracy of molecular fingerprint prediction can be effectively improved.

The invention discloses a bitter compound prediction method based on machine learning, and belongs to the technical field of compound prediction. According to the method, the problems of long time consumption and low accuracy of a traditional method are solved. According to the method, 15 molecular fingerprint combinations of compounds are obtained, and the characteristics of each molecular fingerprint combination are sorted according to the sequence of mutual information values from large to small. Using a feature increment selection method to input the molecular fingerprint combination features of each compound sample after sorting into a bitter compound prediction model, calculating the prediction accuracy of each molecular fingerprint combination, and generating a feature increment selection curve graph of the corresponding molecular fingerprint combination according to each molecular fingerprint combination feature and the prediction accuracy thereof; all features before the feature with the highest accuracy rate are selected, then the optimal molecular fingerprint combination and the optimal feature number are obtained, the feature vector of the compound to be predicted under the optimal molecular fingerprint combination serves as model input, and a prediction result is obtained. The method can be applied to compound prediction.

The invention discloses a terahertz molecular fingerprint sensor based on an over-coupling metasurface and electronic equipment, and relates to the field of terahertz detection. Comprising fingerprint detection unit arrays which are periodically arranged and have the same structure; each fingerprint detection unit comprises a substrate layer, a first metal layer and a protruding structure. The first metal layer is arranged above the substrate layer and has the same cross sectional area with the substrate layer; the protruding structure is arranged above the first metal layer, and the cross section area of the protruding structure is smaller than that of the first metal layer. When the terahertz molecular fingerprint sensor detects a to-be-detected sample, the target position of the first metal layer and the upper surface of the protruding structure are coated with the to-be-detected sample. Terahertz waves are set to vertically enter the over-coupling metasurface, the coupling state of the terahertz waves and the metasurface is changed by adjusting the size of the protruding structure, and qualitative and quantitative detection is carried out on a to-be-detected sample. According to the invention, only a single device is needed, single detection is carried out through a single incident signal, and broadband fingerprint detection and'one-device double-detection 'of a trace sample to be detected are realized.

The invention relates to the field of optical sensing, in particular to a composite structure SERS detection substrate and a preparation method thereof.The composite structure SERS detection substrate comprises a GaN distributed Bragg reflector structure, a first metal layer and a second metal layer, the first metal layer and the second metal layer are deposited on the GaN distributed Bragg reflector structure, and monodisperse metal particles of the first metal layer are selected from Au, Ag or Cu; the monodisperse metal particles of the second metal layer are selected from Pt or Pb. Based on the plasmon resonance characteristic of a small-size multi-metal structure and the high reflection characteristic of a distributed Bragg reflector, the high-sensitivity optical detection of organic molecules is realized by utilizing the organic molecule fingerprint characteristic of a Raman spectrum, and meanwhile, the plasmon catalytic effect of the multi-metal composite structure is modulated by utilizing the multi-metal composite structure, so that the high-sensitivity optical detection of the organic molecules is realized. The optical sensing device is a novel optical sensing device with a wide application prospect.

The invention relates to the technical field of carbohydrate informatics, in particular to a carbohydrate biomacromolecule property prediction method and system based on a graph neural network and chemical fingerprints. Comprising the following steps: modeling a carbohydrate chain sequence into an undirected graph, regarding monosaccharide and glucosidic bonds as nodes in the graph, introducing a virtual node for storing fingerprint features, extracting molecular fingerprint information from the sequence, constructing two different adjacent matrixes Afull and Aori with virtual node connection and without virtual node connection, and constructing two adjacent matrixes Afree and Aori; the dimension of the fingerprint features is reduced to be consistent with the node features, the features are replaced with molecular fingerprint features by positioning the positions of virtual nodes in the graph, the first three layers transmit Aori to enable a model to learn topological structure information of the graph, and the last layer transmits Afull to achieve fusion of the graph structure and the fingerprints. And pooling and splicing node features obtained after convolution of each layer to obtain final sugar chain representation for predicting different properties of sugar chains. According to the method, the graph structure and chemical fingerprint information of the sugar chain can be effectively combined to obtain more meaningful sugar chain representation.

The present invention discloses a method for predicting the cytotoxicity of disinfection by-products, which uses a method based on machine learning algorithms to predict the cytotoxicity of DBPs by means of the molecular structure and physicochemical properties of compounds. The method process includes: collecting the cytotoxicity values of DBPs to establish a database; converting all DBPs into SMILES; calculating the molecular fingerprints of all DBP samples, and normalizing and standardizing the sample data; constructing a toxicity prediction model based on multiple machine learning algorithms and selecting the optimal model; after inputting the SMILES expression of the DBP to be measured, directly outputting the predicted cytotoxicity value of the DBP to be measured.

The application provides a set of methods for constructing antigen-antibody complex mutual recognition interface descriptors (i.e., protein molecular fingerprints), which maximally present the structure and physicochemical characteristics of specific recognition by describing the binding interface of the three-dimensional structure of the antibody-antigen from both sides. Based on the protein molecular fingerprints generated by the set of methods, based on the specific interaction recognition rules of the antigen-antibody, the epitope prediction algorithm specific to the antibody and the virtual screening model of the antibody are designed, and the existing machine learning or deep learning tools are docked, so that the antigen epitope prediction based on the antibody and the high-throughput virtual screening of the antibody based on the specific epitope can be quickly realized.

The present invention belongs to the technical field of organic screening, and specifically relates to a preferred method for replacing standard substances in the semi-quantitative process of non-standard substances. The preferred method includes: obtaining the response factor and multiple molecular fingerprints of the standard substance; using several similarity calculation methods for the various molecular fingerprints of each standard sample, respectively, to calculate the similarity between each standard sample and other standard samples, and selecting other standard samples with the highest similarity as substitutes based on the obtained similarity matrix; error evaluation, obtaining the optimal circular fingerprint, structural bond fingerprint and topological fingerprint types and the optimal similarity calculation method; generating a composite fingerprint; evaluating the response factor error of the optimal structural bond fingerprint, circular fingerprint and topological fingerprint with the composite fingerprint, and selecting the molecular fingerprint with the smallest error and the similarity calculation method. The present invention achieves the effect of minimizing the semi-quantitative error of non-standard substances by evaluating the quantitative errors of different molecular fingerprint types and similarity calculation methods for characterizing chemicals.

The invention discloses a drug target affinity prediction method, electronic equipment and a computer readable storage medium, the method comprises the following steps: feature extraction is carried out on input small molecule SMILES and protein sequences, and the small molecule adopts 10 molecular fingerprints of RDK, Topological, MACCS, AtomPair, ECFP4, FCFP4, FCFP6, Avalon, Layered and Pattern to construct mixed fingerprint features; compressing the high-dimensional molecular fingerprint features to vector dimensions consistent with protein features through linear mapping to realize feature balance, and splicing to form a fusion feature vector; and performing nonlinear interaction and expression enhancement on the fusion features by adopting a multi-expert hybrid module, and finally outputting an affinity prediction value between the small molecule and the protein through a linear regression layer. Through fusion of multi-source chemical fingerprints and deep protein pre-training representation, higher feature expression ability and stronger model generalization are realized; the complexity of the model is reduced through feature mapping and a lightweight multi-expert hybrid model, so that the model has better stability and expandability.

The application provides a cell-targeting drug primary screening model training method, a drug primary screening method and equipment. The method comprises the following steps: obtaining molecular fingerprint feature data corresponding to each compound sample with label information to form a corresponding original data set, performing data screening on the original data set by using a distance measurement method, and training a regression model based on a graph neural network to learn the structure topology of each compound sample. The model is trained as a cell-targeting drug primary screening model for predicting whether a compound has potential cell-targeting drug activity. The application can effectively improve the comprehensiveness of molecular representation during the training process of the cell-targeting drug primary screening model, effectively improve the generalization ability of the cell-targeting drug primary screening model obtained by training, effectively improve the applicability of the cell-targeting drug primary screening model obtained by training, and improve the accuracy and reliability of the prediction result of the potential cell-targeting drug activity predicted by the model.

The present invention proposes a method and device for intelligently generating molecular fingerprints for non-targeted metabolomics, which relates to the field of data processing technology, including: performing weak enhancement processing on labeled data in mass spectrometry sample data, and performing weak enhancement processing and strong enhancement processing on unlabeled data in mass spectrometry sample data to obtain mass spectrometry training data; using a Transformer encoder to extract features from the mass spectrometry training data to obtain a Morgan molecular fingerprint, and using the Morgan molecular fingerprint as a constraint condition, using the forward diffusion process to generate random Gaussian noise as the basis for reverse diffusion to generate a target molecular fingerprint; using the screened target molecular fingerprint to train a preset network model to obtain a target network model to complete the processing of the mass spectrometry data to be processed. The present invention extracts features from mass spectrometry data and uses them as constraints to generate molecular fingerprints of metabolites to be analyzed, thereby realizing the training of the diffusion model and improving the generalization ability of the model.