633 results about "Drug target" patented technology

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

The invention discloses a drug-target interaction prediction method based on double-flow collaborative attention and sparse feature fusion, and belongs to the technical field of computational biology. The method solves the problem that the existing method cannot capture the sub-structure discrimination features and the key binding region features of the drug-target interaction pair. According to the method, a double-flow collaborative attention strategy combining a multi-scale space attention mechanism and a channel enhanced attention mechanism is adopted to cooperatively capture discriminative features of substructures, the multi-scale space attention mechanism utilizes a multi-branch convolutional layer to adaptively integrate space substructure features, and molecular representation of each substructure is enhanced; the channel-enhanced attention mechanism mitigates the inconsistency of substructure features. The sparse attention mechanism can highlight the key features while suppressing the noise, and the cross attention mechanism improves the extraction capability of the features of the key combination region through feature interaction between the modeling drug and the target. The method can be applied to drug-target interaction prediction.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.

The invention discloses a multi-modal data fusion drug-target affinity prediction system based on Graph Transform. The system comprises a data preprocessing module, a graph representation module, a text representation module and an affinity prediction module. The data preprocessing module is responsible for analyzing and processing the SMILES character string of the compound and the protein ID, and extracting the structural information of the compound and the three-dimensional structural data of the protein. And the affinity prediction module performs multi-modal fusion on the graph features and the text features, processes fusion feature vectors through a feedforward neural network comprising three full-connection layers, and outputs a prediction result of drug-target affinity. According to the method, multi-modal information of a graph structure and a sequence text is combined, the accuracy of cross-domain drug-target affinity prediction can be effectively improved, and the method has a wide application prospect.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention belongs to the field of medical technology analysis, and discloses an analysis method and system for revealing a hidden binding pocket of a drug target, and the method comprises the steps: firstly obtaining a representative conformation metastable state of a target protein through conventional molecular dynamics simulation and clustering analysis; secondly, constructing a Markov state model to analyze a dynamic transformation rule between conformations; carrying out enhanced sampling by adopting meta-dynamics, and deeply exploring a rare conformation space; and finally, constructing a free energy landscape to quantitatively evaluate the relative stability of the conformation, and identifying a hidden binding pocket in the stable rare conformation. According to the method, the limitation of a single calculation means is overcome, a full-chain calculation system of dynamic conformation analysis-hidden cavity feature mining-novel ligand rational design is constructed, and the formation mechanism and potential druggability of the hidden pocket can be comprehensively revealed from the two dimensions of dynamics and thermodynamics; and an efficient and accurate calculation framework is provided for research and development of innovative drugs targeting difficult drug targets.

The invention discloses a deep learning prediction method fused with multi-modal features, which can accurately identify protein hidden binding sites in a ligand-free (apoo) state. The method comprises the following steps of: firstly, constructing a protein graph by taking residues as nodes and taking C alpha distance less than or equal to 14 as edges, wherein node feature sets comprise amino acid one-hot, secondary structures, atomic attributes, protein language model embedding and BLOSUM62 evolutionary information, and edge features comprise distance and angle similarity; then capturing three-dimensional geometric equivariant features by adopting an equivariant graph neural network (EGNN), and modeling a chemical topological relation by using a graph isomorphic network (GINE) with edge features; eGNN and GINE double-branch feature fusion and global dependence integration are realized through gating cross attention and gating multi-head attention; and finally, inputting the fusion features into a Kolmogorov-Arnold network (KAN) classifier, and predicting whether each residue belongs to a hidden binding site or not. The method can adapt to large-scale conformation change without coordinate alignment, AUC and F1 on a standard data set are remarkably superior to those of an existing method, high robustness and generalization are kept for multi-chain protein and complex conformation, and the method can be widely applied to drug target discovery and structure-driven drug design.

The invention discloses a drug target binding affinity prediction method based on collaborative attention, and belongs to the technical field of natural language processing, and the method comprises the steps: building a CLAT-DTA prediction model comprising an input data representation module, a feature extraction module, an information fusion module and a prediction module; converting drug molecules into fingerprint representation, and pre-training protein sequence data by using ESM; extracting drug data by using Encoder, and extracting protein data by using Bi-LSTM (Bidirectional Long Short-Term Memory); fusing the drug target data using a collaborative attention mechanism; three-layer full ligation is used to predict drug target binding affinity. According to the method, important information can be better polymerized, and the binding affinity between the drug and the protein can be predicted.

The invention discloses a drug targeted delivery device and method, and belongs to the technical field of intelligent medical treatment. The device comprises the following modules: an intelligent targeted recognition module for accurately recognizing a gastric cancer specific molecular marker and improving the selectivity and specificity of targeted recognition in combination with a dynamic surface modification strategy; the microenvironment real-time monitoring module is used for monitoring key physiological parameters of the gastric cancer microenvironment in real time; the intelligent path planning module is used for dynamically calculating and optimizing a drug delivery path and realizing accurate drug positioning; the personalized drug administration regulation and control module is used for accurately predicting the drug dosage demand of an individual and dynamically and finely adjusting the drug administration amount in real time; the drug resistance risk management module is used for early warning a drug resistance risk in advance and automatically generating a personalized alternative drug administration strategy through multi-dimensional drug resistance analysis and real-time early warning; and the intelligent drug release module adopts a temperature-sensitive / pH-sensitive polymer shell layer and a nano valve to control drug release, so that precise gradient release of drug concentration is realized.

According to the screening method for the potential protein biomarkers and the drug targets of the gastric cancer, Mendel randomization analysis in a proteome range is adopted, the genetic causal relationship between circulating plasma protein and the risk of the gastric cancer is evaluated, and finally the remarkably related protein is identified. According to the screening method of the potential protein biomarker and the drug target of the gastric cancer, provided by the invention, the potential association between circulating plasma protein and the gastric cancer is systematically revealed by integrating Mendel randomization, single-cell RNA sequencing analysis, space transcriptome analysis, virtual drug screening, molecular docking, molecular dynamics simulation and other methods.

The invention discloses a drug target prediction method based on fragment-level local and global feature fusion, and belongs to the technical field of computational biology and artificial intelligence drug design. Comprising the following steps: acquiring a medicine SMILES character string and a protein amino acid sequence; respectively segmenting the drug SMILES character string and the protein amino acid sequence to obtain a drug structure fragment sequence and a protein functional fragment sequence; and inputting the drug structure fragment sequence and the protein function fragment sequence into a pre-trained drug-target interaction prediction model to obtain a prediction probability of drug-target pair interaction. Compared with the prior art, the method has the advantages that convolution feature extraction, a multi-head attention mechanism and a gating fusion strategy are combined, an end-to-end DTI prediction framework is constructed, and the interaction between drugs and targets can be comprehensively mined.

The invention provides a TF and Her2 targeting bispecific antibody coupling drug as well as a preparation method and application thereof, and belongs to the technical field of biological drug preparation. The bispecific antibody provided by the invention can target TF and / or Her2 antigens in tumor cells, and has the advantages of high stability, easiness in expression, purification and coupling and the like. The bispecific antibody can be specifically combined with a tumor surface antigen and internalized into tumor cells, so that the tumor cells can be specifically killed. The bispecific antibody coupling drug prepared on the basis of the bispecific antibody has a good tumor inhibition effect in a cell model and an animal model, is nontoxic and harmless to animals, and has an excellent cancer treatment potential.

The invention provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention relates to the technical field of drug target prediction, and discloses a drug target intelligent prediction method and system, and the method comprises the following steps: 1, obtaining the structure data and sequence data of a target protein, and the homologous sequence data of the target protein; 2, performing molecular dynamics simulation according to the structural data, and calculating to obtain a dynamic conformation feature vector; according to the sequence data, an evolutionary information feature vector is obtained through calculation; analyzing the structural data to obtain an interaction feature vector; 3, obtaining an enhanced dynamic conformation feature vector, an enhanced evolutionary information feature vector and an enhanced interaction feature vector through an attention fusion mechanism, respectively obtaining importance weights through a gating fusion mechanism, and obtaining a fusion feature vector by adopting element-by-element multiplication and combining the importance weights; and 4, inputting the fusion feature vector into a graph neural network prediction model to obtain a target druggability probability.

The invention discloses a synthetic lethal prediction method based on non-generality forgetting and low-rank interaction multi-mode adaptive fusion, and relates to the technical field of bioinformatics, computational biology and drug target screening. The method comprises the following steps: firstly, constructing a multi-view gene embedding expression based on a gene graph structure, a hypergraph structure and a knowledge graph; then constructing a difference vector and an interaction vector for any gene pair so as to characterize the difference and potential complementary relationship between the genes; multi-source information fusion is realized by using a deep learning model containing interactive attention, a forgetting gate and a multi-modal fusion mechanism; on this basis, multi-task optimization is carried out through joint classification loss, modal interaction loss and redundancy suppression loss, and optimal discrimination is realized through a dynamic threshold search strategy; and finally, outputting a sorting result of the candidate synthetic lethal gene pairs according to the prediction score. According to the method, the accuracy and generalization ability of synthetic lethal relationship prediction can be effectively improved, and a high-reliability calculation auxiliary tool is provided for anti-cancer drug target screening and gene therapy strategy design.

The invention discloses folate receptor mediated manganese ion coordination type cabazitaxel-loaded albumin nanoparticles as well as a preparation method and application thereof. Human serum albumin is covalently linked with folic acid through an amide condensation reaction to obtain a folic acid-albumin modifier with folic acid targeting; further combining with manganese ions through metal coordination to form a folic acid-albumin-manganese ion modifier with a targeting function; finally, efficient loading of cabazitaxel is achieved through a simple heating polymerization method, and the folate receptor mediated cabazitaxel-loaded albumin nanoparticles are formed. According to the nanoparticles provided by the invention, the particle size is about 140 nm, the stability is good, and the drug loading capacity and the encapsulation efficiency of cabazitaxel are high; the compound has a slow release effect, is good in in-vitro release behavior, and has important application value in the aspect of targeted delivery of drugs to tumors. Compared with other traditional nanoparticles, the nanoparticles show a remarkable anti-tumor effect.

The invention discloses preparation and application of grouper enterospora sporowall protein SWP26 and a polyclonal antibody of the grouper enterospora sporowall protein SWP26, and belongs to the field of animal quarantine, the grouper enterospora sporowall protein SWP26 is obtained by amplifying grouper enterospora genes by using a PCR (Polymerase Chain Reaction) method, shearing a target band and transferring the target band into a pET-32a vector to construct a recombinant plasmid, and then transforming escherichia coli BL21 (DE3) for induced expression. A Ni-NTA affinity chromatography method is used for protein purification, animal immunization and polyclonal antibody purification are carried out on the purified SWP26 recombinant protein, western blot detection is carried out on the purified antibody, an obvious signal appears at about 43kDa, and it is proved that the anti-SWP26 polyclonal antibody can be subjected to a specific reaction with the SWP26 purified protein. The invention clones and identifies the high-abundance spore wall protein SWP26 positioned on the surface of the enterosporidium of the grouper for the first time, belongs to a specific protein of the enterosporidium of the grouper, and can be used as a drug target for treating enterocytozoonosis of the grouper.

The invention relates to the technical field of novel biological nanomaterials, in particular to a lung-targeted exosome complex as well as a preparation method and application thereof. The lung targeting type exosome complex is prepared from an NK cell exosome, lipid nanoparticles and an entrapped anti-tumor nucleic acid drug, the NK cell exosome is obtained by extracting a natural killer cell NK-92MI of a human malignant non-Hodgkin lymphoma patient through a differential centrifugation method. The anti-tumor nucleic acid drug is a specific nucleic acid drug targeting a key canceration driving gene in non-small cell lung cancer. The lung targeting type exosome complex prepared by the invention is an excellent and stable drug delivery carrier, has a good lung tissue targeting function, enhances the tumor targeting effect of the exosome, also exerts the tumor cell killing function of the exosome, and has important significance for the treatment of non-small cell lung cancer.

The invention provides a drug target prediction method based on cross-modal attention and uncertainty evaluation, and belongs to the technical field of drug target prediction. In order to solve the technical problems that the existing drug target prediction lacks quantitative evaluation on the reliability of a prediction result and a nonlinear interaction relationship between a drug and a target is difficult to establish, the method comprises the following steps: collecting data, and fusing graph structure features and sequence features of extracted drug molecules to obtain a final code of the drug molecules; extracting amino acid sequence characteristics of the target protein, and constructing protein sequence characteristic expression; inputting the drug molecular features and the protein sequence features into a cross-modal attention module, and aligning and fusing the drug features and the protein features by using a bidirectional cross attention mechanism to obtain drug-target combined feature representation; introducing an uncertainty quantification mechanism, and outputting uncertainty estimation of a drug-target interaction prediction label and a prediction result; the method is used for predicting drug target interaction.

The invention relates to a protein pocket detection system and method and a storage medium, and the detection system comprises a conformation generation module, a pocket detection module, a task scheduling and management module and a visualization module: the task scheduling and management module obtains to-be-detected protein data; the conformation generation module is used for generating a conformation track of the protein data by utilizing molecular dynamics simulation based on the protein data acquired by the task scheduling and management module; the pocket detection module is used for determining pocket data based on the conformation track generated by the conformation generation module; wherein the pocket data is used for representing the stable data of the spatial position and the geometric shape of the protein data in the change process of the conformation trajectory; and the visualization module is used for rendering and outputting the conformation track generated by the conformation generation module and the pocket data determined by the pocket detection module. The problem that in the prior art, key drug targets generated in the dynamic process of protein cannot be efficiently and conveniently found is solved.

The invention discloses an application of a Gnai2 protein inhibitor in preparation of a medicine for treating diabetic nephropathy, and belongs to the technical field of biological medicine, research shows that expression of Gnai2 in a diabetic nephropathy sample is up-regulated and is in positive correlation with a renal injury index, after the Gnai2 gene is knocked out, lipophagy can be promoted, lipid droplet deposition can be reduced, development of diabetic nephropathy can be relieved, and the Gnai2 protein inhibitor can be used for treating diabetic nephropathy. Overexpression of the Gnai2 gene can significantly inhibit lipophagy, promote lipid droplet deposition and aggravate development of diabetic nephropathy. The Gnai2 protein as a drug target can be used for screening and discovering substances for preventing and treating the diabetic nephropathy, and the Gnai2 protein inhibitor has a good application prospect in the aspect of preparing drugs for treating the diabetic nephropathy. According to the invention, the blank of'lipid metabolism disorder 'research in the pathogenesis of diabetic nephropathy is filled, and the normal form transformation of diabetic nephropathy from single sugar control treatment to'lipid metabolism-inflammation / fibrosis' multi-channel precise treatment is promoted.

The invention discloses a drug sequence descriptor-based drug and target interaction prediction method, and belongs to the technical field of drug target prediction. According to the method, the descriptors of the smiles sequences of the two drugs are adopted, and after the two descriptors are added, prediction of the model on the result is more accurate due to the fact that the two descriptors contain structural information and chemical information related to tasks; for protein sequences, the characteristics of a large language model ESM-2 and the characteristics of a BERT large language model are added, and the model weights of the ESM-2 model and the BERT model are derived from tens of thousands of protein sequences, so that the representation capability of output vectors is very high; the interaction between protein features and drug features from a source sequence is realized through the designed cross attention layer with the shared weight, the prediction precision of the model is further improved, and experiments show that the model designed by the invention has a better effect on multiple indexes on multiple data sets.

This invention discloses an AfBPP-based active molecular probe for thiostreptin, its preparation, and its application, belonging to the field of chemical biology. This invention introduces a bifunctional tag integrating a photocrosslinking group (bisacrididine) and a bioorthogonal reactive group (alkynyl group) into the structure of thiostreptin. While retaining the original biological activity of the parent compound, it endows the probe with highly efficient probe function, solving the technical problem of target loss during washing and purification of traditional non-covalent probes. After target labeling, the probe can specifically connect to reporter groups such as fluorescein or biotin through click-chemical reactions, achieving efficient enrichment of drug targets. Combined with mass spectrometry analysis, it enables global identification of potential targets in cells or complex biological samples at the omics level, such as chemical proteomics, providing a powerful molecular tool for in-depth revelation of the potential targets and pharmacological mechanisms of thiostreptin.

The invention discloses an application of astaxanthin in preparation of a regulator for improving the gentisic acid level. On the basis of non-targeted metabonomics analysis, it is found for the first time that in the SAP pathogenesis process, the level of a flora metabolite, namely gentisic acid (GA), in intestinal contents is remarkably reduced, and in-vivo gentisic acid level can be remarkably callback through astaxanthin intervention. Further research proves that exogenous supplement of gentisic acid can effectively simulate the protection effect of astaxanthin, including improvement of pancreatic tissue pathological damage, reduction of serum lipase and amylase activity, and inhibition of systemic inflammatory response (down-regulation of IL-1beta, IL-6 and TNF-alpha and up-regulation of IL-10). The invention not only discloses a new mechanism that astaxanthin plays a therapeutic role through an'intestinal flora-metabolite 'axis, but also determines that gentisic acid is used as a novel drug target for treating SAP, and constructs a new method for screening drugs for treating SAP according to the novel drug target.

The invention belongs to the technical field of biological medicines, and discloses a B cell and cartilage cell senescence targeting osteoarthritis treatment medicine. The invention provides an application of an anti-MIF antibody or an antigen binding fragment thereof as a B cell targeting immunomodulator in osteoarthritis treatment drugs, the anti-MIF antibody or the antigen binding fragment thereof regulates and controls an MIF / HMGB1 / MMP13 signal axis through a neutralization effect with MIF, so that the MIF and B cells are significantly co-localized, expression of HMGB1 in the B cells is promoted, and the osteoarthritis treatment effect is improved. The senescence of B cells is reduced and the B cells are promoted to regulate or repair phenotype transformation so as to destroy chronic inflammatory circulation and diffusion of pathogenic plasma cells, and the expression of MMP13 in joint tissues is reduced so as to maintain the steady state of cartilage tissues; the preparation method has an excellent application prospect in preparation of an OA intra-articular injection drug which realizes rapid anti-inflammatory and long-term cartilage protection effects and can realize a lasting curative effect through low-frequency drug delivery.

The application discloses a candidate drug ranking method and system based on topological coding supervision reconstruction, and belongs to the field of bioinformatics. The method comprises the following steps: step S1, obtaining drug design data; step S2, constructing a drug-biological entity association matrix; step S3, calculating a drug side topological matrix for describing the connection relationship between drugs and a biological entity side topological matrix for describing the connection relationship between biological entities according to the association matrix; step S4, constructing a finite-order topological filter and a spectral stable topological filter to form a bidirectional topological evidence field; step S5, obtaining a reconstruction supervision matrix through a supervised reconstruction objective function; and step S6, obtaining candidate drug ranking based on the reconstruction supervision matrix. The application can improve the high-order mechanism recognition ability, robustness and interpretability in drug-target prediction, drug repositioning, candidate molecule screening and molecular generation result ranking.

This invention provides a data storage method and system for tuberculosis detection, relating to the field of tuberculosis detection technology. The data storage method for tuberculosis detection includes the following steps: S1. Collecting whole-genome sequencing data of Mycobacterium tuberculosis, host serum IgG titer, and drug sensitivity test results; S2. Calculating genetic distance D based on a reverse evolution model to generate four-dimensional spatiotemporal coordinates (t, x, y); S3. Performing data partitioning and storage based on the drug target barrier value β; S4. Generating dynamic metadata using a host-pathogen dynamics model and compressing and storing it. This invention implements a dynamic storage entropy adjustment algorithm at the hardware and software collaborative level, continuously optimizing the matching efficiency of data compression and physical storage. This results in an intelligent data hub that can perceive the evolutionary pulse of pathogens and autonomously optimize resources, providing support for clinical tuberculosis prevention and control decisions with temporal depth, spatial correlation, and risk evolution.

This invention discloses a lncRNA biomarker associated with AML, characterized in that the lncRNA biomarker is lncRNA FOXN3-AS1, and the nucleotide sequence of lncRNA FOXN3-AS1 is shown in SEQ ID NO: 1. This invention also discloses the application of a detection kit and method in the diagnosis and prognosis of AML. The lncRNA FOXN3-AS1 of this invention, as an auxiliary diagnostic biomarker for clinical detection of AML, has the advantages of simple operation, low cost, and accurate detection, making it suitable for clinical screening. The biomarker provided by this invention can be applied to the preparation of early AML assessment products, which is beneficial for further elucidating the pathogenesis of AML and helps in the discovery of novel small molecule drug targets with potential therapeutic value.

The application provides a bispecific antibody conjugate drug targeting TF and Her2, and a preparation method and application thereof, and belongs to the technical field of biological drug preparation. The bispecific antibody provided by the application can target TF and / or Her2 antigens in tumor cells, has high stability, and has the advantages of easy expression, purification and conjugation. The bispecific antibody can specifically bind to tumor surface antigens and be internalized into tumor cells, and can specifically kill tumor cells. The bispecific antibody conjugate drug prepared based on the bispecific antibody has good tumor inhibition effect in a cell model and an animal model, is non-toxic and harmless to animals, and has excellent potential for treating cancer.

The invention belongs to the technical field of genetic engineering and pulmonary interstitial disease medicine, and particularly relates to an SNP marker for auxiliary diagnosis of idiopathic pulmonary fibrosis and application of the SNP marker. The invention discloses and verifies the application value of a specific marker combination composed of 76 SNPs in auxiliary diagnosis of idiopathic pulmonary fibrosis for the first time. By detecting the SNP spectrum, an important molecular tool and data basis can be provided for early discovery of idiopathic pulmonary fibrosis, disease assessment and even subsequent drug target mining. The invention further provides a kit for detecting the 76 SNP markers, the kit has high sensitivity and specificity, and effective diagnosis of idiopathic pulmonary fibrosis can be achieved by detecting the 76 SNP markers.