1634 results about "Drug molecule" patented technology

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

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 relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The invention relates to the technical field of drug concentration detection, and relates to a clinical pharmaceutical blood concentration detection method, which comprises the following steps: capturing drug molecules in a blood sample based on a micro-fluidic chip, enabling a sensor to detect the drug molecules and combine with the surface of the drug molecules, and collecting high-sensitivity blood reaction data. According to the method, main variables in data are identified and utilized by adopting a principal component analysis method, so that the complexity of spectral data is effectively reduced, meanwhile, the most critical information for calculating the drug concentration is reserved, the data processing flow is optimized, the analysis speed and efficiency are improved, and the prediction of the drug concentration is more accurate; according to the method, more reliable data support is provided for clinic, the data is smoothed through the moving average algorithm, random fluctuation caused by sampling errors or external variables is reduced, the overall quality and credibility of the data are improved, errors can be obviously reduced, and therefore doctors are supported to conduct more accurate medicine dosage adjustment and curative effect evaluation.

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 baricitinib phthalate crystal form, and relates to the technical field of crystal form drug molecules. The crystal form is radiated by Cu-K alpha, and an X-ray diffraction pattern represented by 2 theta has characteristic peaks at 6.20 + / -0.2 degrees, 6.53 + / -0.2 degrees, 14.53 + / -0.2 degrees, 18.29 + / -0.2 degrees, 18.53 + / -0.2 degrees, 18.70 + / -0.2 degrees, 19.44 + / -0.2 degrees and 26.95 + / -0.2 degrees; the crystallographic measurement parameters are as follows: an orthorhombic system is adopted, and the space group is Pbcn; the cell parameters are as follows: # imgabs 0 # alpha = 90 degrees, beta = 90 degrees, gamma = 90 degrees, and the cell volume # imgabs 1 #. The invention also provides a related preparation method and application. The baricitinib phthalate crystal form disclosed by the invention is good in stability and relatively high in solubility and permeability.

The invention discloses a compound represented by a formula (1), or a tautomer, a mesomer, a raceme, an enantiomer, a diastereoisomer, or a mixture form thereof, or a pharmaceutically acceptable salt, a prodrug or a solvate thereof, the structure of the formula (1) is represented as follows: # imgabs0. Strong hydrophilic sulfonic acid inner salt and / or phosphoric acid inner salt side chains are introduced at the position of (AAA and the like)-PAB (self-destruction group), so that the druggability (including hydrophilicity and stability) of the antibody drug conjugate corresponding to the compound is greatly enhanced, the in-vivo drug removal effect is reduced, and the tumor treatment effect is improved.

The invention belongs to the technical field of deep learning, and discloses a drug and protein interaction prediction model construction method based on multi-modal fusion. Due to the lack of protein three-dimensional data, the three-dimensional data of the protein is subjected to high-accuracy complementation by using a prediction model AlphaFold 2, and for the modal fusion problem, the drug molecules are fused at the atomic level with finer granularity, so that the multi-modal fusion effect is further enhanced, and the multi-modal fusion effect is improved. Protein 1D data and protein 3D data are processed by using Cross-Attention, the processed protein 1D data and protein 3D data are subsequently fused by using Cross-Attention, multi-modal fusion is performed by using Cross-Attention, so that the interaction relationship between modals can be carefully captured, and in a word, the DTI task effect is improved by designing a multi-modal fusion mode.

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.

The invention provides an enhanced drug-drug interaction prediction method based on multi-modal drug characteristics. The method comprises the following steps: step 1, carrying out data processing on two medicines to obtain characteristic data of the two medicines, wherein the characteristic data comprises a three-dimensional molecular map, a molecular fingerprint and a one-dimensional SMIELS sequence; 2, based on the feature data of the two drugs, feature learning is conducted through a heterogeneous feature extraction network, and respective multi-modal feature embedding expressions of the two drugs are obtained; 3, performing feature fusion on the multi-modal feature embedding expressions of the two drugs to obtain respective fused features of the two drugs; and 4, performing fusion interaction on the fused features of the two drugs and the interaction category features, and predicting a fusion interaction result to obtain a prediction result. The method provided by the invention can integrate the multi-modal information of the drug molecules, does not depend on multivariate information and knowledge maps, and has better performance and wider applicability.

The invention discloses a drug molecule optimization method based on a hidden space diffusion model. The method comprises the steps of 1, constructing drug molecule data sets for different tasks; 2, constructing and pre-training a molecular SMILES auto-encoder; 4, constructing and training a molecular optimization model based on the hidden space diffusion model; according to the method, new molecules which have relatively high similarity with original molecules, have better attributes and are more diversified can be generated, so that the optimization efficiency of multiple attributes of drug molecules is improved.

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 five-membered sulfur-containing heterocyclic nucleoside derivative and application thereof in an anti-human immunodeficiency virus (HIV) active medicine, and belongs to the technical field of medicinal chemistry. A series of five-membered sulfur-containing heterocyclic ring nucleoside compounds are synthesized through a Sulfa-Michael / Aldol cascade reaction. The result shows that in an anti-HIV virus test, the five-membered sulfur-containing heterocyclic nucleoside lead compound 42a has the optimal anti-HIV-1 virus activity, and a new structural unit is provided for antiviral candidate drug molecules.

The invention discloses a molecular property prediction method based on a molecular line graph structure. The method comprises the following steps: firstly, collecting SMILES molecular formulas of drug molecules, and carrying out pretreatment to extract molecular fingerprints and molecular descriptors; then, the molecular line graph structure serves as model input, feature representation of a molecular line graph is calculated through a graph neural network MolGraphTrans based on Transformer, and molecular properties are predicted. According to the method, substructure masks, a multi-head attention mechanism, path coding and distance coding are introduced, so that molecular structure characteristics can be better captured. And finally, the prediction performance is optimized through a low-rank linear layer and a multi-head attention layer. Compared with the prior art, the accuracy and efficiency of molecular property prediction are improved, and the method is suitable for drug screening and optimization.

The invention relates to the technical field of drug interaction prediction, and discloses a drug interaction prediction method based on multilayer attention and elastic message passing, which comprises the following steps: firstly, extracting graph structure information of drug molecules from a 3D structure of the drug molecules, including associated features of atoms and chemical bonds; on the atomic feature level, learning updating and denoising of different atom and chemical bond features in drug molecules are achieved by using an elastic graph neural network and fusing the attention mechanism of Transform and combining a 3D position coding method based on the molecular mass center; on the molecular feature level, the attention mechanism is further utilized, and through supervised learning, comparative learning and structured learning, the potential interaction relationship of different drug molecules is mined, and potential network attacks are detected and defended; and interaction prediction of any two drug molecules i and j is realized. The results of four indexes of ACC, AUC, AP and F1 all reach 98% or above, and the method exceeds the most advanced method at present.

The invention discloses a molecule generation method and device, a storage medium, equipment and a program product, which are applied to a molecule generation scene. The method comprises the following steps: obtaining known modal protein information corresponding to a target protein; performing feature processing on the known modal protein information based on a feature processing module of the trained multi-modal language model to obtain multi-modal standardized features corresponding to the target protein; based on a molecule generation module of the trained multi-modal language model, performing molecule generation processing on the multi-modal standardized features to obtain a target molecule aiming at the target protein; the trained multi-modal language model is obtained by training based on a multi-modal data set, and the multi-modal data set comprises a sequence modal sample, a structure modal sample and a pocket modal sample of a sample protein corresponding to a sample molecule. According to the method, high-quality drug molecules can be generated under the condition that the target protein lacks certain modal information, and the drug design efficiency is improved.

A compound represented by formula (1), or a tautomer, mesomer, racemate, enantiomer, and diastereomer thereof, or mixture form thereof, or a pharmaceutically acceptable salt, prodrug or solvate thereof, wherein the structure of formula (1) is as shown below: formula (1). According to the compound, a strong hydrophilic sulfonate inner salt and / or phosphate inner salt side chain are introduced at the position of polypeptide (VC, VA, AAA, etc.)-PAB (self-immolative group) of a linker, greatly enhancing the druggability (comprising hydrophilicity and stability) of an antibody drug conjugate corresponding to the compound, reducing the in-vivo drug clearance effect, and improving the tumor treatment effect.

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.

According to the pre-training language model-based drug-target interaction prediction method designed by the invention, natural language processing and graph neural network technologies are fused, context semantic features are automatically extracted from drug molecule SMILES character strings and protein sequences, and by constructing a graph structure taking drug-target pairs as nodes, the drug-target interaction is predicted. The weight of an edge is defined according to the similarity between embedded vectors, and a simplified graph convolutional network is adopted to carry out graph structure modeling to realize complex relation learning, so that the accuracy, generalization and interpretability of prediction are improved, the limitation of a traditional method on the problems of sparse feature expression, mutual information loss and'words outside a vocabulary 'is overcome, and the prediction accuracy, generalization and interpretability are improved. And finally, the accuracy of predicting the drug-target interaction relationship is improved.

The invention belongs to the technical field of chemistry, and particularly relates to a diaryl methyl pyrimidine piperazine compound as well as a preparation method, a pharmaceutical composition and application thereof. The structural formula of the diaryl methyl pyrimidine piperazine compound is shown as a formula I, and R is a substituted aromatic group. The preparation process is simple, the prepared diaryl methyl pyrimidine piperazine compound or the pharmaceutically acceptable salt thereof has the activity of efficiently inhibiting WDR5-MLL 1, and a targeted therapy scheme and novel, safe and efficient candidate drug molecules are provided for treating acute myeloid leukemia with WDR5-MLL fusion mutation. Experimental results show that the tumor inhibition rates of mouse tumor-bearing groups treated by the compounds 5c, 5k and 5l prepared by the invention are 79%, 85% and 75% respectively and are superior to those of a positive control group, and the compounds have good development prospects.

The invention discloses a molecular structure characterization-based drug-drug interaction prediction method, which comprises the following steps of: acquiring drug molecule data and drug-drug interaction data, performing standardized preprocessing on the data, and constructing a drug molecule map according to the drug molecule data; inputting the drug molecule map into a model, extracting drug molecule multi-scale features through a multi-scale map convolutional network, and fusing the drug molecule multi-scale features through a dynamic attention mechanism to obtain drug molecule features; based on the drug molecular characteristics, through a full-connection neural network, learning the relationship between the drug molecular characteristics and the drug-drug interaction, and obtaining the prediction probability of the drug-drug interaction; and training the model by adopting a joint loss function according to the drug-drug interaction data and the prediction probability, and applying the trained model to drug-drug interaction prediction. And through the multi-scale image convolutional network, dynamic attention fusion and joint optimization, the prediction accuracy is significantly improved.

The invention discloses a drug target binding affinity prediction method based on a graph neural network, and belongs to the technical field of biological information, and the method comprises the following steps: S1, sample preprocessing; s2, constructing a model; s3, model training; and S4, DTA prediction is carried out. The method comprises the following steps: firstly, obtaining related data through a Davis data set and a KIBA data set, respectively obtaining a drug molecular map, a drug Morgan fingerprint and protein sequence information through data preprocessing, and respectively inputting the drug molecular map, the drug Morgan fingerprint and the protein sequence information into a GATv2 network, a multi-layer perceptron network and a multi-scale convolutional neural network for feature extraction; and performing feature splicing on the Morgan fingerprints and molecular map features through layer attention to obtain drug features, splicing the drug features with protein features, and inputting the spliced drug features into a prediction network to perform drug target binding affinity prediction.

The invention provides an asymmetric drug interaction prediction method based on a diffusion diagram attention network, and relates to the technical field of drug interaction prediction.The method comprises the steps that a directed graph network is constructed by using chemical structure features of drug molecules, and features are extracted from drug applying and receiving perspectives through a two-way diagram attention network; therefore, the asymmetry between the drugs is effectively represented. Furthermore, a diffusion model is introduced to carry out noise injection and de-noising processing on a graph structure, so that the adaptability of the model to sparse data and the robustness of feature extraction are remarkably enhanced. Finally, multi-modal features are fused through a deep neural network, and high-precision prediction of drug interaction is realized. Compared with the prior art, the method has the advantages that the prediction accuracy exceeds that of the prior art, the dependence on labeled data is greatly reduced, the feasibility and generalization ability of the model in practical application are greatly improved, and a new technical path is provided for safety evaluation and precise medical research of drug combination therapy.

The invention provides a new drug molecule design method and device fusing convex optimization and evolutionary learning, and belongs to the field of biological information.The method comprises the steps that in a generative adversarial network, a generator is adopted to generate a small molecule sequence, and the generator is established by adding an attention mechanism model in a long-short-term memory network; evaluating the authenticity of the small molecule sequence by adopting a discriminator, wherein the discriminator is established by adopting a convolutional neural network improved based on convex optimization; updating the parameters of the generator by adopting a strategy gradient method, and updating the parameters of the discriminator by adopting a gradient descent method; and generating a final small molecule sequence by the generator corresponding to the final generator parameter. According to the method, the small molecules which are highly similar to original samples and have higher effectiveness and innovativeness can be generated, the diversity and quality of the generated molecules are improved, and particularly, the method has excellent performance in the aspect of generating molecules with specific functions.

The invention provides a liposome preparation and a preparation method thereof, the liposome preparation comprises an ion pairing agent, phospholipid or a derivative thereof, sterol, a lipid molecule derivative and a drug compound, the ion pairing agent comprises at least one of a compound (I) and a compound (II), and the compound (II) comprises at least one of a compound (III) and a compound (III). Structures in the compound (I) and the compound (II) contain a plurality of functional groups (such as amide, carbonyl, thioether and the like), so that abundant binding sites are provided for drug molecules, and the compound (I) and the compound (II) can interact with different types of drug compounds (such as fat-soluble and water-soluble drugs); the liposome preparation is allowed to be combined with various drug molecules through hydrogen bonds, hydrophobic interaction or electrostatic interaction, and the liposome preparation adapts to physicochemical properties of different drugs. The compound (I) and the compound (II) further comprise different hydrophobic groups (such as alkyl chains) and hydrophilic groups (such as sulfonic acid, carboxylic acid and the like), so that the liposome preparation has the amphiphilic characteristic, not only can encapsulate hydrophobic drugs, but also can encapsulate hydrophilic drugs, and the variety and range of encapsulated drugs are improved.

The invention discloses a drug molecule generation method capable of explaining a federal large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, through a data feature extraction and model initialization step, extracting multi-modal features from distributed data, constructing and initializing a drug molecule generation model, and obtaining a drug molecule generation model; s2, performing parameter optimization on a federal learning client through the large model, including local training fine tuning based on a distributed client, and integrating fine-tuned parameters into a global model of a server to improve model performance and data utilization efficiency, S3, based on the optimized model, generating drug molecular structure information including a molecular description sequence and a molecular graph; according to the drug molecule generation method capable of explaining the federal large model, on the basis of guaranteeing data privacy, the generation capacity and diversity of new molecules are remarkably improved, meanwhile, the transparency and credibility of the model are enhanced through causal analysis and an attention mechanism, the performance and robustness of the model are improved, and the method has wide application prospects.

The invention provides a drug-target correlation prediction method based on a hierarchical representation learning framework, and the method comprises the steps: screening high-information-density nodes based on a dynamic fluctuation threshold value, reducing low-noise nodes, and reconstructing topological connection according to a virtual edge weight formula; performing tensor splicing on the drug molecular features extracted by the dynamic neighborhood search framework and the protein semantic features generated by the denoising auto-encoder to form drug-protein pair joint feature representation; a multi-head attention mechanism is utilized to allocate dynamic weights for multi-view features based on drug-protein pairs, multi-view feature vectors are spliced, after key information is screened through the attention mechanism, the key information is input into a full-connection neural network, and a correlation prediction value is output based on the full-connection neural network. The association prediction method solves the problems that the prediction precision of the model on the complex biological interaction is poor, and the understanding ability of the model on the multilevel feature learning association in the complex biological network is seriously limited.