Drug-target interaction prediction method and device based on characterization alignment space

Abstract

The invention discloses a characterization alignment space-based drug-target interaction prediction method and device. The method comprises the following steps of: initializing parameters of a protein characterization dimension, a drug characterization dimension, a public embedding characterization dimension, the number of layers of a multi-layer sensor and a batch dimension; acquiring a plurality of single-source network data through a plurality of pharmaceutical databases, and performing comparison processing to construct a biological medicine heterogeneous network; according to a comparative learning strategy, integrating a protein target and drug characterization into a public embedding characterization space, and carrying out DTI association training to set a model; and inputting the protein targets and the corresponding drug representations into the trained setting model for DTI prediction to obtain a plurality of most relevant drugs. According to the method, by constructing the biological medicine heterogeneous network, the DTI association related to unknown medicines can be predicted; an embedded characterization alignment frame is built, a high-quality characterization method can be utilized to characterize a biological medicine network and a biological entity, and the problem of training data sparsity can be solved by utilizing contrast loss.

Description

Technical Field

[0001] The present invention relates to the technical field of new drug discovery prediction, and in particular to a drug-target interaction prediction method and device based on characterization alignment space. Background Art

[0002] Drug discovery is a complex and multi-stage process that aims to identify and develop new compounds that can effectively treat specific diseases. Predicting drug-target interactions (DTI) helps scientists determine which drug molecules may effectively bind to and regulate specific biological targets, which is a key challenge in the drug discovery process. However, traditional drug discovery methods, such as protein structure analysis, are not only time-consuming but also costly to identify DTI. In addition, experimental operations may also be limited by aspects such as sample preparation, which further increases the difficulty of drug discovery.

[0003] Currently, in order to solve this problem, researchers have developed machine learning and artificial intelligence driven models to improve prediction efficiency and accuracy. These algorithms can predict possible DTI patterns and associations in the network by analyzing heterogeneous biomedical networks composed of biological entities such as drugs, target structures, and drug targets.

[0004] Existing methods cannot predict DTI associations for new drugs outside of biomedical networks because they rely on a large amount of prior data and known associations. When this data is lacking, the model has difficulty making accurate predictions, which is known as the cold start problem. Solving the cold start problem is key to new drug discovery and improving the accuracy of new drug-target association predictions.

[0005] At the same time, network-based methods have become increasingly popular in DTIs prediction due to their ability to effectively utilize biomedical heterogeneous networks (BioHNs), especially graph neural networks (GNNs), which aggregate neighborhood information through a message passing mechanism and show significant advantages in DTI prediction.

[0006] However, methods that rely on knowledge graphs or BioHNs usually treat DTI prediction as a binary classification or regression task, focusing mainly on memorizing the binding annotation ratios in the training data. This approach ignores the learning of molecular interaction features and limits the generalization ability to new data. In other words, GNNs-based methods have difficulty effectively predicting DTI in the absence of graph associations of unknown entities, and such methods are not suitable for new drug discovery.

[0007] Therefore, how to invent a method that can effectively predict DTI has become an urgent problem to be solved. Summary of the invention

[0008] To this end, the present invention provides a drug-target interaction prediction method and device based on representation alignment space. The method provides multi-layer association knowledge by constructing a large-scale biomedical heterogeneous network, which is conducive to predicting DTI associations related to unknown drugs. At the same time, based on the idea of ​​contrastive learning, an embedded representation alignment framework is built, which can not only use high-quality representation methods to characterize biomedical networks and biological entities, but also use contrastive loss to solve the problem of sparse training data.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a drug-target interaction prediction method based on characterizing alignment space, comprising:

[0010] Initialize the protein representation dimension, drug representation dimension, common embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters;

[0011] Acquire a number of single-source network data through a number of pharmaceutical databases; perform comparison processing on the single-source network data to construct a biomedical heterogeneous network;

[0012] Integrate the protein target and drug representations obtained by the network representation strategy into a common embedding representation space according to a contrastive learning strategy; and set the model by DTI association training using the contrastive learning strategy;

[0013] The protein target and several drug representations corresponding to the protein target are input into the trained setting model for DTI prediction to obtain several drugs most relevant to the protein target.

[0014] As a preferred solution of the drug-target interaction prediction method based on characterizing the alignment space, in the process of obtaining the plurality of single-source network data through a plurality of pharmaceutical databases, the plurality of pharmaceutical databases include the drug database DrugBank, the therapeutic target database TTD, the pharmacogenomics database PharmGKB, the comprehensive drug database DrugCentral and the drug repositioning database repoDB;

[0015] The single-source network data include drug-target interactions, drug-disease interactions, and drug-drug interactions.

[0016] As a preferred solution of the drug-target interaction prediction method based on characterizing the alignment space, in the process of comparing and processing a number of the single-source network data to construct the biomedical heterogeneous network, the single-source network data are used to remove non-compliant data and redundant data according to set rules to construct the biomedical heterogeneous network; the drugs, diseases and targets in the biomedical heterogeneous network are used as nodes, the interactions between entities are used as edges, and the nodes in the biomedical heterogeneous network are numbered.

[0017] As a preferred scheme of the drug-target interaction prediction method based on representation alignment space, in the process of integrating the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the comparative learning strategy, the drug and protein target are respectively mapped to independent representation spaces through the network representation strategy or the biological entity representation strategy; and a multilayer perceptron is set as a projection module to map the drug representation and protein representation of set dimensions to the common embedding representation space of the same dimension.

[0018] As a preferred solution of the drug-target interaction prediction method based on characterizing the alignment space, in the process of training the set model by DTI association using the contrast learning strategy, the parameters of the projection module are optimized by calculating the bidirectional cross entropy loss function, and the formula for calculating the bidirectional cross entropy loss function is:

[0019]

[0020] In the formula, n is the number of labels; and Respectively represent sim and sim T The probability of the label corresponding to the i-th row in .

[0021] The present invention also provides a drug-target interaction prediction device based on the characterization alignment space, based on the above drug-target interaction prediction method based on the characterization alignment space, comprising:

[0022] Parameter initialization module, used to initialize the protein representation dimension, drug representation dimension, common embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters;

[0023] A biomedical heterogeneous network construction module is used to obtain a number of single-source network data through a number of pharmaceutical databases; compare and process the single-source network data to construct a biomedical heterogeneous network;

[0024] A common embedding representation space representation integration module is used to integrate the protein target and drug representations obtained by the network representation strategy into the common embedding representation space according to a contrastive learning strategy; using the contrastive learning strategy, a model is set through DTI association training;

[0025] The DTI prediction module is used to input the protein target and the drug representations corresponding to the protein target into the trained setting model to perform DTI prediction and obtain the drugs most relevant to the protein target.

[0026] As a preferred solution of the drug-target interaction prediction device based on characterizing the alignment space, in the biomedical heterogeneous network construction module, in the process of obtaining the plurality of single-source network data through a plurality of pharmaceutical databases, the plurality of pharmaceutical databases include a drug database DrugBank, a therapeutic target database TTD, a pharmacogenomics database PharmGKB, a comprehensive drug database DrugCentral and a drug repositioning database repoDB;

[0027] The single-source network data include drug-target interactions, drug-disease interactions, and drug-drug interactions.

[0028] As a preferred solution of the drug-target interaction prediction device based on characterizing the alignment space, in the biomedical heterogeneous network construction module, in the process of comparing and processing a number of the single-source network data to construct the biomedical heterogeneous network, the single-source network data are compared and processed, and non-compliant data and redundant data are removed according to set rules to construct the biomedical heterogeneous network; the drugs, diseases and targets in the biomedical heterogeneous network are taken as nodes, the interactions between entities are taken as edges, and the nodes in the biomedical heterogeneous network are numbered.

[0029] As a preferred solution of the drug-target interaction prediction device based on representation alignment space, in the common embedding representation space representation integration module, in the process of integrating the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the comparative learning strategy, the drug and protein target are respectively mapped to independent representation spaces through the network representation strategy or the biological entity representation strategy; a multilayer perceptron is set as a projection module to map the drug representation and protein representation of set dimensions to the common embedding representation space of the same dimension.

[0030] As a preferred solution of the drug-target interaction prediction device based on the representation alignment space, in the public embedding representation space representation integration module, in the process of using the contrast learning strategy to train the set model through DTI association, the parameters of the projection module are optimized by calculating the bidirectional cross entropy loss function, and the formula for calculating the bidirectional cross entropy loss function is:

[0031]

[0032] In the formula, n is the number of labels; and Respectively represent sim and sim T The probability of the label corresponding to the i-th row in .

[0033] The present invention has the following advantages: initializing the protein representation dimension, drug representation dimension, public embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters; obtaining a number of single-source network data through a number of pharmaceutical databases; performing comparison processing on a number of said single-source network data to construct a biomedical heterogeneous network; integrating the protein target and drug representation obtained through the network representation strategy into a public embedding representation space according to a contrastive learning strategy; using the contrastive learning strategy, training a setting model through DTI association; inputting the protein target and a number of drug representations corresponding to the protein target into the trained setting model for DTI prediction, and obtaining a number of drugs most relevant to the protein target. The present invention proposes an embedding representation alignment framework based on contrastive learning, constructs a public embedding representation space, and extensively learns DTI association patterns by learning sparse DTI labels in the network, and further expands to predict the relevant DTI of new drugs. A multi-layer perceptron collaborative projection mapping module is adopted to generate a cross-dimensional public embedding representation space, thereby effectively unifying multi-source, multi-modal, heterogeneous network representations and entity representations. The present invention also proposes a weak label loss function label calculation method, which can reduce the overfitting learning of the remaining drug-target pairs, thereby improving generalization and being effectively applicable to new drugs. The present invention constructs a large-scale biomedical heterogeneous network for multi-source, heterogeneous biomedical data, including multiple biological entities and multiple entity associations, provides multi-layer association knowledge, contains more hidden biological meanings, and is conducive to predicting DTI associations related to unknown drugs; based on the idea of ​​contrastive learning, an embedded representation alignment framework is built, which can not only use high-quality representation methods to characterize biomedical networks and biological entities, but also use contrastive loss to solve the problem of sparse training data; the designed weak label calculation method does not explicitly specify the degree of association between the remaining drug-target pairs, while helping the model learn the actual associated drug-target pairs, it reduces the overfitting of the remaining drug-target pairs, thereby retaining the learning ability of one-to-many and many-to-many DTI. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0035] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0036] Figure 1 This is a schematic diagram of the process of the drug-target interaction prediction method based on characterization alignment space provided in Example 1 of the present invention;

[0037] Figure 2 A schematic diagram of a public embedding representation alignment space constructed based on BioHNs in the drug-target interaction prediction method based on representation alignment space provided in Example 1 of the present invention;

[0038] Figure 3 This is a schematic diagram of a correlation degree calculation and ranking prediction module in the drug-target interaction prediction method based on characterization alignment space provided in Example 1 of the present invention;

[0039] Figure 4 Schematic diagram of the architecture of the drug-target interaction prediction device based on characterization alignment space provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0040] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] See also Figure 1 Embodiment 1 of the present invention provides a method for predicting drug-target interactions based on characterizing an alignment space, comprising the following steps:

[0043] S1, initializing the protein representation dimension, drug representation dimension, common embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters;

[0044] S2. Acquire a number of single-source network data through a number of pharmaceutical databases; perform comparison processing on the single-source network data to construct a biomedical heterogeneous network;

[0045] S3, integrating the protein target and drug representations obtained by the network representation strategy into a common embedding representation space according to a contrastive learning strategy; setting a model through DTI association training using the contrastive learning strategy;

[0046] S4. Inputting the protein target and several drug representations corresponding to the protein target into the trained setting model to perform DTI prediction, and obtaining several drugs most relevant to the protein target.

[0047] In this embodiment, in step S1, the protein representation dimension, drug representation dimension, common embedding representation dimension, the number of multi-layer perceptron layers and batch dimension parameters are initialized.

[0048] Specifically, the protein representation dimension dim p , drug characterization dimension dim d , the public embedding representation dimension dim, the number of multilayer perceptron layers l, the batch dimension n and other parameters are initialized.

[0049] In this embodiment, in step S2, in the process of acquiring the plurality of single-source network data through a plurality of pharmaceutical databases, the plurality of pharmaceutical databases include a drug database DrugBank, a therapeutic target database TTD, a pharmacogenomics database PharmGKB, a comprehensive drug database DrugCentral, and a drug repositioning database repoDB;

[0050] The single-source network data include drug-target interactions, drug-disease interactions, and drug-drug interactions.

[0051] Specifically, multiple single-source network data such as drug-target interaction, drug-disease interaction, and drug-drug interaction are obtained through the drug database DrugBank, the therapeutic target database TTD, the pharmacogenomics database PharmGKB, the comprehensive drug database DrugCentral, and the drug repositioning database repoDB.

[0052] In this embodiment, in step S2, it also includes removing non-compliant data and redundant data from the single-source network data according to set rules in the process of comparing and processing the single-source network data to construct the biomedical heterogeneous network, and constructing the biomedical heterogeneous network; taking drugs, diseases and targets in the biomedical heterogeneous network as nodes, and the interactions between entities as edges, and numbering the nodes in the biomedical heterogeneous network.

[0053] Specifically, all databases are compared, and according to the two rules that the target has a unique UniProt number and the target is marked as "reviewed", the non-compliant data and redundant data are removed to form a biomedical heterogeneous network. Drugs, diseases, and targets in the biomedical heterogeneous network are taken as nodes, and the interactions between entities are taken as edges. The nodes in the biomedical heterogeneous network are numbered: d represents the drug node; p represents the protein target node; di represents the disease node; where num d 、num p 、num di Represent the total number of drug, target, and disease nodes, respectively.

[0054] In this embodiment, in step S3, if Figure 2 As shown, in the process of integrating the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the contrastive learning strategy, the drug and protein target are respectively mapped to independent representation spaces through the network representation strategy or the biological entity representation strategy; a multilayer perceptron is set as a projection module to map the drug representation and protein representation of the set dimension to the common embedding representation space of the same dimension.

[0055] Specifically, based on mature network representation methods or biological entity representation methods, drugs and protein targets are mapped to independent representation spaces respectively; a multi-layer perceptron (MLP) is deployed as a projection module to map the dim d The drug representation and dimension of dim p The protein representations are mapped to a common embedding representation space with the same dimension dim.

[0056] In this embodiment, in step S3, it also includes using the contrast learning strategy to train the set model through DTI association.

[0057] Specifically, the cos similarity is calculated as the distance matrix sim between the drug and the target in the representation space and its transposed sim T Represents the bidirectional distance matrix. Based on contrastive learning, the drug-target pairs that actually exist in the biomedical network are positive samples, and the rest are negative samples;

[0058] The labels are constructed in a weakly associated way. Each sample is regarded as an independent category, and its label directly corresponds to the index in the batch, that is, label = [0, 1, 2, ..., n-1]. The bidirectional cross entropy loss function is calculated to optimize the projection module parameters. The formula for calculating the bidirectional cross entropy loss function is:

[0059]

[0060] In the formula, n is the number of labels; and Respectively represent sim and sim T The probability of the label corresponding to the i-th row in .

[0061] In this embodiment, in step S4, the protein target and the representations of several drugs corresponding to the protein target are input into the trained setting model for DTI prediction to obtain several drugs most relevant to the protein target.

[0062] Specifically, Figure 3 As shown, the protein target and N drug representations are input into the trained setting model to predict the top k drugs related to the protein target; they are mapped to the common embedding representation space through the projection module, and the cos similarity between the protein target representation and the N drug representations is calculated to measure the degree of association; the drugs are sorted from large to small according to the degree of association, and the top k drugs are the k drugs predicted by the setting model to be associated with the protein target.

[0063] In summary, the present invention initializes the protein representation dimension, drug representation dimension, public embedding representation dimension, multilayer perceptron layer number and batch dimension parameters; obtains a number of single-source network data through a number of pharmaceutical databases; compares and processes a number of said single-source network data to construct a biomedical heterogeneous network; integrates the protein target and drug representation obtained by the network representation strategy into the public embedding representation space according to the contrastive learning strategy; uses the contrastive learning strategy to train the setting model through DTI association; inputs the protein target and a number of drug representations corresponding to the protein target into the trained setting model for DTI prediction, and obtains a number of drugs most relevant to the protein target. The present invention proposes an embedding representation alignment framework based on contrastive learning, constructs a public embedding representation space, and learns DTI association patterns extensively by learning sparse DTI labels in the network, and further expands to predict the relevant DTI of new drugs. A multilayer perceptron collaborative projection mapping module is adopted to generate a cross-dimensional public embedding representation space, thereby effectively unifying multi-source, multi-modal, heterogeneous network representations and entity representations. The present invention also proposes a weak label loss function label calculation method, which can reduce the overfitting learning of the remaining drug-target pairs, thereby improving generalization and being effectively applicable to new drugs. The present invention constructs a large-scale biomedical heterogeneous network for multi-source, heterogeneous biomedical data, including multiple biological entities and multiple entity associations, provides multi-layer association knowledge, contains more hidden biological meanings, and is conducive to predicting DTI associations related to unknown drugs; based on the idea of ​​contrastive learning, an embedded representation alignment framework is built, which can not only use high-quality representation methods to characterize biomedical networks and biological entities, but also use contrastive loss to solve the problem of sparse training data; the designed weak label calculation method does not explicitly specify the degree of association between the remaining drug-target pairs, while helping the model learn the actual associated drug-target pairs, it reduces the overfitting of the remaining drug-target pairs, thereby retaining the learning ability of one-to-many and many-to-many DTI.

[0064] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0065] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] Example 2

[0067] See also Figure 4 Embodiment 2 of the present invention also provides a drug-target interaction prediction device based on characterizing the alignment space, comprising:

[0068] Parameter initialization module 001, used to initialize the protein representation dimension, drug representation dimension, common embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters;

[0069] The biomedical heterogeneous network construction module 002 is used to obtain a number of single-source network data through a number of pharmaceutical databases; compare and process the single-source network data to construct a biomedical heterogeneous network;

[0070] The common embedding representation space representation integration module 003 is used to integrate the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the contrastive learning strategy; and to set the model through DTI association training using the contrastive learning strategy;

[0071] The DTI prediction module 004 is used to input the protein target and the drug representations corresponding to the protein target into the trained setting model to perform DTI prediction, and obtain the drugs most relevant to the protein target.

[0072] In this embodiment, in the biomedical heterogeneous network construction module 002, in the process of obtaining the plurality of single-source network data through a plurality of pharmaceutical databases, the plurality of pharmaceutical databases include a drug database DrugBank, a therapeutic target database TTD, a pharmacogenomics database PharmGKB, a comprehensive drug database DrugCentral and a drug repositioning database repoDB;

[0073] The single-source network data include drug-target interactions, drug-disease interactions, and drug-drug interactions.

[0074] In the process of comparing and processing a number of the single-source network data to construct the biomedical heterogeneous network, the single-source network data are used to remove non-compliant data and redundant data according to set rules to construct the biomedical heterogeneous network; the drugs, diseases and targets in the biomedical heterogeneous network are used as nodes, the interactions between entities are used as edges, and the nodes in the biomedical heterogeneous network are numbered.

[0075] In this embodiment, in the common embedding representation space representation integration module 003, in the process of integrating the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the comparative learning strategy, the drug and protein target are respectively mapped to independent representation spaces through the network representation strategy or the biological entity representation strategy; a multilayer perceptron is set as a projection module to map the drug representation and protein representation of the set dimension to the common embedding representation space of the same dimension.

[0076] In the process of using the contrastive learning strategy to train the set model through DTI association, the parameters of the projection module are optimized by calculating the bidirectional cross entropy loss function. The formula for calculating the bidirectional cross entropy loss function is:

[0077]

[0078] In the formula, n is the number of labels; and Respectively represent sim and sim T The probability of the label corresponding to the i-th row in .

[0079] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0080] Example 3

[0081] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a drug-target interaction prediction method based on characterization alignment space is stored, and the program code includes instructions for executing the drug-target interaction prediction method based on characterization alignment space of embodiment 1 or any possible implementation thereof.

[0082] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0083] Example 4

[0084] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0085] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the drug-target interaction prediction method based on characterization alignment space of Example 1 or any possible implementation thereof.

[0086] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0087] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0088] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0089] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A drug-target interaction prediction method based on characterization alignment space, characterized in that: include: Initialize the protein representation dimension, drug representation dimension, common embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters; Acquire a number of single-source network data through a number of pharmaceutical databases; perform comparison processing on the single-source network data to construct a biomedical heterogeneous network; According to the contrastive learning strategy, the protein target and drug representations obtained by the network representation strategy are integrated into the common embedding representation space; Using the contrastive learning strategy, the model is set through DTI association training; The protein target and several drug representations corresponding to the protein target are input into the trained setting model for DTI prediction to obtain several drugs most relevant to the protein target.

2. The drug-target interaction prediction method based on characterization alignment space according to claim 1, characterized in that: In the process of obtaining the single-source network data through several pharmaceutical databases, the pharmaceutical databases include the drug database DrugBank, the therapeutic target database TTD, the pharmacogenomics database PharmGKB, the comprehensive drug database DrugCentral and the drug repositioning database repoDB; The single-source network data include drug-target interactions, drug-disease interactions, and drug-drug interactions.

3. The drug-target interaction prediction method based on characterization alignment space according to claim 2, characterized in that: In the process of comparing and processing a number of the single-source network data to construct the biomedical heterogeneous network, the single-source network data are used to remove non-compliant data and redundant data according to set rules to construct the biomedical heterogeneous network; the drugs, diseases and targets in the biomedical heterogeneous network are used as nodes, the interactions between entities are used as edges, and the nodes in the biomedical heterogeneous network are numbered.

4. The drug-target interaction prediction method based on characterization alignment space according to claim 3, characterized in that: In the process of integrating the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the contrastive learning strategy, the drug and protein target are respectively mapped to independent representation spaces by the network representation strategy or the biological entity representation strategy; A multi-layer perceptron is set as a projection module to map the drug representation and protein representation of set dimensions to the common embedding representation space of the same dimension.

5. The drug-target interaction prediction method based on characterization alignment space according to claim 4, characterized in that: In the process of using the contrastive learning strategy to train the set model through DTI association, the parameters of the projection module are optimized by calculating the bidirectional cross entropy loss function. The formula for calculating the bidirectional cross entropy loss function is: In the formula, n is the number of labels; and Respectively represent sim and sim T The probability of the label corresponding to the i-th row in .

6. A drug-target interaction prediction device based on characterization alignment space, using any of the drug-target interaction prediction methods based on characterization alignment space of claims 1 to 5, characterized in that: include: Parameter initialization module, used to initialize the protein representation dimension, drug representation dimension, common embedding representation dimension, multi-layer perceptron layer number and batch dimension parameters; A biomedical heterogeneous network construction module is used to obtain a number of single-source network data through a number of pharmaceutical databases; compare and process the single-source network data to construct a biomedical heterogeneous network; A common embedding representation space representation integration module is used to integrate the protein target and drug representations obtained by the network representation strategy into the common embedding representation space according to the contrastive learning strategy; Using the contrastive learning strategy, the model is set through DTI association training; The DTI prediction module is used to input the protein target and the drug representations corresponding to the protein target into the trained setting model to perform DTI prediction and obtain the drugs most relevant to the protein target.

7. The drug-target interaction prediction device based on characterization alignment space according to claim 6, characterized in that: In the biomedical heterogeneous network construction module, in the process of obtaining the single-source network data through the pharmaceutical databases, the pharmaceutical databases include the drug database DrugBank, the therapeutic target database TTD, the pharmacogenomics database PharmGKB, the comprehensive drug database DrugCentral and the drug repositioning database repoDB; The single-source network data include drug-target interactions, drug-disease interactions, and drug-drug interactions.

8. The drug-target interaction prediction device based on characterization alignment space according to claim 7, characterized in that: In the biomedical heterogeneous network construction module, in the process of comparing and processing a number of the single-source network data to construct the biomedical heterogeneous network, the single-source network data are used to remove non-compliant data and redundant data according to set rules to construct the biomedical heterogeneous network; the drugs, diseases and targets in the biomedical heterogeneous network are used as nodes, the interactions between entities are used as edges, and the nodes in the biomedical heterogeneous network are numbered.

9. The drug-target interaction prediction device based on characterization alignment space according to claim 8, characterized in that: In the common embedding representation space representation integration module, in the process of integrating the protein target and drug representation obtained by the network representation strategy into the common embedding representation space according to the contrastive learning strategy, the drug and protein target are respectively mapped to independent representation spaces by the network representation strategy or the biological entity representation strategy; A multi-layer perceptron is set as a projection module to map the drug representation and protein representation of set dimensions to the common embedding representation space of the same dimension.

10. The drug-target interaction prediction device based on characterization alignment space according to claim 9, characterized in that: In the common embedding representation space representation integration module, in the process of using the contrastive learning strategy to train the set model through DTI association, the parameters of the projection module are optimized by calculating the bidirectional cross entropy loss function, and the formula for calculating the bidirectional cross entropy loss function is: In the formula, n is the number of labels; and Respectively represent sim and sim T The probability of the label corresponding to the i-th row in .

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