Drug screening method, device, electronic device and storage medium

Abstract

This disclosure provides a drug screening method, apparatus, electronic device, and storage medium, relating to artificial intelligence technologies such as machine learning and intelligent search. A specific implementation scheme involves using a pre-trained affinity prediction model to screen information on multiple candidate drugs corresponding to a target from a drug compound library; wherein the affinity prediction model is obtained through multi-task training based on the target and other targets; and based on the information on the multiple candidate drugs, information on the screened target drugs is obtained. The disclosed technology can effectively improve drug screening efficiency.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to the field of artificial intelligence technologies such as machine learning and intelligent search, and more particularly to a drug screening method, device, electronic device, and storage medium. Background Art

[0002] Virtual screening is an important early step in the drug discovery process. One of the main purposes of virtual screening is to obtain candidate compounds with high affinity to the target from the drug compound library.

[0003] Molecular docking is a computational method that simulates the intermolecular interactions between a compound and a target protein to predict their binding patterns and affinities. In recent years, as more and more protein structures have been resolved, molecular docking has become an important method for virtual screening. Summary of the Invention

[0004] The present disclosure provides a drug screening method, device, electronic device and storage medium.

[0005] According to one aspect of the present disclosure, there is provided a drug screening method comprising:

[0006] Using a pre-trained affinity prediction model, screening information of multiple candidate drugs corresponding to a target from a drug compound library; wherein the affinity prediction model is obtained by multi-task training based on the target and other targets;

[0007] Based on the information of the plurality of candidate drugs, information of several screened target drugs is obtained.

[0008] According to another aspect of the present disclosure, there is provided a drug screening device comprising:

[0009] A screening module, configured to screen information on multiple drug candidates corresponding to a target from a drug compound library using a pre-trained affinity prediction model, wherein the affinity prediction model is obtained by multi-task training based on the target and other targets;

[0010] The drug acquisition module is used to acquire information of several screened target drugs based on the information of the multiple candidate drugs.

[0011] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0012] at least one processor; and

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0015] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method of the above-mentioned aspect and any possible implementation manner.

[0016] According to yet another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the above-mentioned aspects and any possible implementation method when executed by a processor.

[0017] According to the technology disclosed in the present invention, the efficiency of drug screening can be effectively improved.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0020] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0021] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0022] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;

[0023] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0024] Figure 5 It is a block diagram of an electronic device used to implement the drug screening method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0026] Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0027] It should be noted that the terminal devices involved in the embodiments of the present disclosure may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0028] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0029] With the advancement of chemical synthesis methods, more and more compounds can be produced, and the size of drug compound libraries available for virtual screening has grown exponentially, making it increasingly time-consuming to directly use molecular docking technology for virtual screening of the entire drug compound library, increasing the cost and cycle of the drug development process.

[0030] In addition, considering the development of artificial intelligence, especially machine learning technology in recent years, it has become possible to use machine learning technology for virtual screening.

[0031] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure; Figure 1 As shown, this embodiment provides a drug screening method, which can be applied to a drug screening device or a drug screening application, and specifically may include the following steps:

[0032] S101. Using a pre-trained affinity prediction model, screen multiple candidate drug information corresponding to a target from a drug compound library; wherein the affinity prediction model is obtained by multi-task training based on the target target and other targets;

[0033] S102. Based on the information of multiple candidate drugs, obtain information of several screened target drugs.

[0034] The drug compound library of this embodiment can include molecular information of numerous drug compounds synthesized by chemical synthesis methods, and therefore can also be referred to as a molecular compound library. During the drug discovery process, drugs can be screened in the drug compound library based on the target, and then the next step in drug development can be entered based on the target drugs screened.

[0035] In this embodiment, to improve the accuracy of drug screening, an affinity prediction model, pre-trained through multi-task training based on the target and other targets, is used to implement drug screening. Because this affinity prediction model undergoes multi-task training, it not only learns to calculate the affinity between the current target and the drug, but also learns general knowledge from data on other targets and docked drugs to improve the prediction results for the current task of the target. Therefore, in this embodiment, using this affinity prediction model, it is possible to accurately screen multiple candidate drug information corresponding to the target target from the drug compound library. Furthermore, based on the information on multiple candidate drugs, it is possible to accurately obtain information on several target drugs to be screened.

[0036] The drug screening method of this embodiment, by adopting the above-mentioned technical solution, can provide a drug screening solution based on machine learning. Compared with the traditional computational molecular docking technology, it can effectively shorten the time of drug screening, effectively reduce the cost of drug screening, and effectively improve the efficiency of drug screening.

[0037] Figure 2 is a schematic diagram according to the second embodiment of the present disclosure; Figure 2 As shown, the drug screening method of this embodiment, in the above Figure 1 Based on the technical solutions of the embodiments shown, the technical solutions of the present application are further described in more detail. Figure 2 As shown, the drug screening method of this embodiment may specifically include the following steps:

[0038] S201, obtaining first type of training data based on the target and drug compound library;

[0039] For example, the following steps may be included:

[0040] (a) Obtain information on several drugs from a drug compound library;

[0041] The acquisition may be to randomly acquire information of any number of drugs from the drug compound library.

[0042] (b) obtaining the affinity of each drug to the target;

[0043] For example, molecular docking technology can be used to obtain the affinity of each drug with the target. For example, molecular docking software such as Autodock, Autodock vina, and Glide can be used to obtain the affinity of each drug with the target.

[0044] Since the information of several drugs obtained is very small compared to the number of drug compounds in the drug compound library, the workload of using molecular docking technology to calculate the affinity of each drug with the target will not be very large, but it can be sufficient to ensure the accuracy of the affinity of each drug with the target.

[0045] (c) Generate a first training data set based on the target, the acquired information of the plurality of drugs, and the affinity of each drug with the target.

[0046] Specifically, the first training dataset may include multiple training data items, each of which may include information about a target, a drug, and the affinity of the drug for the target. The first training dataset generated using the above method includes data related to the target, enabling the affinity prediction model to learn relevant knowledge about the target.

[0047] S202, obtaining a second training data set corresponding to other targets;

[0048] Specifically, it is first necessary to obtain information about other targets, docked drugs for other targets, and the affinity between other targets and docked drugs; then, based on the information about other targets, docked drugs for other targets, and the affinity between other targets and docked drugs, a second training data set is generated.

[0049] In practical applications, it is possible to collect existing molecular docking data of other relevant targets. For example, these data can be from previous drug development projects, public data, or other legal sources. Based on this, more other targets, information on the docking drugs corresponding to the other targets that have been disclosed, and the affinity of the two can be obtained to form a second training data set. Each piece of training data in the second training data set can include information on one other target, one docking drug for the other target, and the affinity of the two. The second training data set obtained in this way can ensure the accuracy of the relevant data of other targets obtained, and thus enable the affinity prediction model to accurately learn the relevant knowledge of other targets.

[0050] S203, performing multi-task training on the affinity prediction model based on the first training data set and the second training data set;

[0051] Specifically, the affinity prediction model can be trained using the training data in the first training data set and the second training data set at the same time, so that the affinity prediction model can not only learn the relevant knowledge of the current target in the first training data set, but also learn general knowledge from the relevant data of other targets in the second training data set, thereby realizing multi-task training of the affinity prediction model, and further effectively improving the accuracy of the affinity prediction model's task prediction of the target target.

[0052] In one embodiment of the present disclosure, step S203 may further include the following training methods:

[0053] (1) Using the second training data set, the affinity prediction model is trained in the first phase;

[0054] (2) Using the first training data set, the affinity prediction model is trained in the second stage.

[0055] In this approach, the affinity prediction model is trained in stages. In the first stage, the affinity prediction model is trained using the second training dataset, allowing it to first learn general knowledge about other targets. The affinity prediction model is then precisely trained using the data of the current target in the first training dataset, allowing it to quickly and accurately learn knowledge about the target and accurately predict tasks for the target.

[0056] S204, using the trained affinity prediction model to screen information of multiple candidate drugs corresponding to the target from the drug compound library;

[0057] The screening process involves screening all drug compound information in the drug compound library. The information and target of each drug compound are input into the affinity prediction model, which can predict the affinity of each drug compound with the target.

[0058] S205. Using molecular docking technology, screen information on several target drugs that best match the target from information on multiple candidate drugs.

[0059] Compared to step S204, this step can be a secondary screening. To improve screening accuracy, this screening method can be different from the screening method in step S204. For example, in this step, molecular docking technology can be used to calculate the affinity of each candidate drug with the target. The higher the affinity, the more compatible the candidate drug is with the target. Based on this, information on several target drugs with the highest affinity scores can be screened out from the information on multiple candidate drugs to serve as the information on the target drugs that best match the target.

[0060] Alternatively, step S205 may be omitted, and instead, based on the affinity between each candidate drug information and the target predicted by the affinity prediction model obtained in step S204, information on several target drugs with the greatest affinity may be screened from the multiple candidate drug information. Alternatively, other characteristics of the drug molecule, such as the size of the molecule, the age of the molecule's production date, etc., may be referenced to screen several target drug information from the multiple candidate drug information.

[0061] In actual applications, the number of target drugs is much smaller than the number of candidate drugs. Based on the information of the target drug, the next step of drug development can be entered.

[0062] In one embodiment of the present disclosure, step S205 may also be omitted. The number of drugs and multiple candidate drugs obtained in the first training dataset is far less than the number of drugs in the entire drug compound library. Therefore, compared to molecular docking of all drugs in the drug compound library, this process can still effectively accelerate virtual screening based on molecular docking.

[0063] The affinity prediction model of this embodiment can be a network model such as a convolutional neural network, a graph neural network, a transformer, or a multi-layer perceptron.

[0064] The drug screening method of this embodiment, by employing the above technical solution, enables multi-task training of the affinity prediction model using both target and other target data, making the affinity prediction model more accurate. Furthermore, based on the trained affinity prediction model, drugs in a drug compound library can be virtually screened. Compared to traditional computational molecular docking techniques, this method can significantly shorten the time and cost of drug screening, while significantly improving drug screening efficiency.

[0065] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure; Figure 3 As shown, this embodiment provides a drug screening device 300, comprising:

[0066] A screening module 301 is configured to screen information on multiple candidate drugs corresponding to a target from a drug compound library using a pre-trained affinity prediction model; wherein the affinity prediction model is obtained by multi-task training based on the target and other targets;

[0067] The drug acquisition module 302 is used to acquire information of several screened target drugs based on information of multiple candidate drugs.

[0068] The drug screening device 300 of this embodiment realizes the implementation principle and technical effect of drug screening by adopting the above-mentioned modules, which is the same as the implementation of the above-mentioned related method embodiments. For details, please refer to the records of the above-mentioned related method embodiments, which will not be repeated here.

[0069] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure; Figure 4 As shown, this embodiment also provides a drug screening device 400, including Figure 3 The modules with the same name and the same functions in the illustrated embodiment are a screening module 401 and a drug acquisition module 402 .

[0070] like Figure 4 As shown, the drug screening device 400 of this embodiment further includes:

[0071] A generating module 403 is used to generate a first training data set based on the target and the drug compound library;

[0072] The data acquisition module 404 is used to acquire a second training data set corresponding to other targets;

[0073] The training module 405 is configured to perform multi-task training on the affinity prediction model based on the first training data set and the second training data set.

[0074] In one embodiment of the present disclosure, the generating module 403 is configured to:

[0075] Obtain information on several drugs from a drug compound library;

[0076] Obtain the affinity of each drug to the target;

[0077] A first training data set is generated based on the target, information of several drugs, and the affinity of each drug to the target.

[0078] In one embodiment of the present disclosure, the generating module 403 is configured to:

[0079] Molecular docking technology is used to obtain the affinity between each drug and the target.

[0080] In one embodiment of the present disclosure, the data acquisition module 404 is configured to:

[0081] Obtain information about other targets, docking drugs for other targets, and the affinity between other targets and docking drugs;

[0082] A second training data set is generated based on information about other targets, docked drugs for other targets, and affinities between other targets and docked drugs.

[0083] In one embodiment of the present disclosure, the training module 405 is configured to:

[0084] Using the second training data set, the affinity prediction model is trained in the first phase;

[0085] The affinity prediction model is trained in the second stage using the first training data set.

[0086] In one embodiment of the present disclosure, the drug acquisition module 402 is configured to:

[0087] Molecular docking technology is used to screen the information of several target drugs that best match the target from the information of multiple candidate drugs.

[0088] The drug screening device 400 of this embodiment realizes the implementation principle and technical effect of drug screening by adopting the above-mentioned modules, which is the same as the implementation of the above-mentioned related method embodiments. For details, please refer to the records of the above-mentioned related method embodiments, which will not be repeated here.

[0089] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0090] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0091] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0092] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the device 500 can also be stored in the RAM 503. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0093] Various components in device 500 are connected to I / O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, optical disk, etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0094] The computing unit 501 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units for running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the above-mentioned drug screening method of the present disclosure. For example, in some embodiments, the above-mentioned drug screening method of the present disclosure can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the above-mentioned drug screening method of the present disclosure described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the above-mentioned drug screening method of the present disclosure in any other appropriate manner (for example, by means of firmware).

[0095] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0100] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0101] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0102] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A drug screening method comprising: Using a pre-trained affinity prediction model, screening information of multiple candidate drugs corresponding to a target from a drug compound library; wherein the affinity prediction model is obtained by multi-task training based on the target and other targets; Based on the information of the plurality of candidate drugs, obtaining information of several screened target drugs; Before screening information of multiple candidate drugs corresponding to the target from the drug compound library using the pre-trained affinity prediction model, the method further includes: Based on the target and the drug compound library, a first training data set is generated; the first training data set includes a plurality of training data, each of which includes information about the target, the drug, and the affinity between the drug and the target; Obtaining a second training data set corresponding to the other target; the second training data set includes information about the other target, the docking drug for the other target, and the affinity between the other target and the docking drug; Using the second training data set, performing a first phase of training on the affinity prediction model; The affinity prediction model is trained in the second stage using the first training data set.

2. The method according to claim 1, wherein Based on the target and the drug compound library, generating a first training data set includes: obtaining information of a plurality of drugs from the drug compound library; Obtaining the affinity of each of the drugs to the target; The first training data set is generated based on the target, information about the multiple drugs, and the affinity between each drug and the target.

3. The method according to claim 2, wherein: Obtaining the affinity of each of the drugs to the target comprises: Molecular docking technology is used to obtain the affinity of each drug with the target.

4. The method according to claim 1, wherein Obtaining a second training data set corresponding to the other target includes: Obtaining information about the other target, the docked drug of the other target, and the affinity between the other target and the docked drug; The second training data set is generated based on the other target, information about the docked drug of the other target, and affinity between the other target and the docked drug.

5. The method according to any one of claims 1 to 4, wherein: Based on the information of the plurality of candidate drugs, information of a plurality of screened target drugs is obtained, including: Molecular docking technology is used to screen information on the multiple candidate drugs that best match the target.

6. A drug screening device comprising: A screening module, configured to screen information on multiple drug candidates corresponding to a target from a drug compound library using a pre-trained affinity prediction model, wherein the affinity prediction model is obtained by multi-task training based on the target and other targets; a drug acquisition module, configured to acquire information of a plurality of screened target drugs based on the information of the plurality of candidate drugs; The device further comprises: A generating module, configured to generate a first training data set based on the target and the drug compound library; the first training data set includes a plurality of training data, each of which includes information about the target, the drug, and the affinity between the drug and the target; a data acquisition module, configured to acquire a second training data set corresponding to the other target; the second training data set includes information about the other target, the docked drug for the other target, and the affinity between the other target and the docked drug; Training modules for: Using the second training data set, performing a first phase of training on the affinity prediction model; The affinity prediction model is trained in the second stage using the first training data set.

7. The device according to claim 6, wherein The generating module is used to: obtaining information of a plurality of drugs from the drug compound library; Obtaining the affinity of each of the drugs to the target; The first training data set is generated based on the target, information about the multiple drugs, and the affinity between each drug and the target.

8. The device according to claim 7, wherein The generating module is used to: Molecular docking technology is used to obtain the affinity of each drug with the target.

9. The device according to claim 6, wherein The data acquisition module is used to: Obtaining information about the other target, the docked drug of the other target, and the affinity between the other target and the docked drug; The second training data set is generated based on the other target, information about the docked drug of the other target, and affinity between the other target and the docked drug.

10. The device according to any one of claims 6 to 9, wherein: The drug acquisition module is used to: Molecular docking technology is used to screen information on the multiple candidate drugs that best match the target.

11. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

13. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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