Method and device for calculating combined free energy, medium and program product

Abstract

The invention aims to provide a method and device for calculating combined free energy, a medium and a program product, and the method comprises the steps: based on a trajectory file of molecular dynamics simulation, determining data information corresponding to each frame in the trajectory file, the data information corresponding to each frame in the trajectory file comprising distance distribution information; determining one or more data clusters based on data information corresponding to each frame in the track file; based on the one or more data clusters, a corresponding binding free energy is determined. Each frame of the trajectory file is clustered, and calculation combined with free energy is performed based on the clustering result, so that a large amount of calculation resources are saved while the calculation accuracy is guaranteed, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of bioinformatics, and in particular to a technology for calculating binding free energy. Background Art

[0002] Accurate calculation of binding free energy is an important issue in the field of molecular simulation. The change in free energy between the initial state (reactants) and the final state (products) determines the extent of the biological process. Classical methods for calculating binding free energy include free energy perturbation (FEP) and thermodynamic integration (TI). These two methods are mature methods with relatively accurate calculation results, but they require the collection of a large amount of data, and practical applications are limited by computing resources and system size. Molecular Mechanics-Poisson Bolzmann Surface Area (MM-PBSA) is a method for estimating binding free energy by post-processing the trajectory of molecular dynamics simulation. Although the accuracy of the MM-PBSA method is not as good as FEP and TI, this method has a small amount of calculation and is an effective method in molecular recognition and distinguishing the strength of binding. It has been successfully used in many calculations of binding free energy. At present, the calculation of binding free energy using the MM-PBSA method generally involves manually selecting some special frames for calculation, or calculating all or part of the frames extracted at set intervals. The former is affected by human factors and has a limited number of selected items, so the calculation accuracy is not high; while the latter has a large amount of calculation, requires computing resources, and has a slow calculation speed. Summary of the invention

[0003] One object of the present application is to provide a method, device, medium and program product for calculating binding free energy.

[0004] According to one aspect of the present application, a method for calculating binding free energy is provided, the method comprising:

[0005] Based on the trajectory file of molecular dynamics simulation, determining data information corresponding to each frame in the trajectory file, wherein the data information corresponding to each frame in the trajectory file includes distance distribution information;

[0006] Determine one or more data clusters based on data information corresponding to each frame in the trajectory file;

[0007] Based on the one or more data clusters, corresponding binding free energies are determined.

[0008] According to one aspect of the present application, a computer device for calculating binding free energy is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0009] According to one aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0010] According to one aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0011] According to one aspect of the present application, there is provided a device for calculating binding free energy, the device comprising:

[0012] A module for determining data information corresponding to each frame in the trajectory file based on the trajectory file of the molecular dynamics simulation, wherein the data information corresponding to each frame in the trajectory file includes distance distribution information;

[0013] A module one or two, for determining one or more data clusters based on data information corresponding to each frame in the trajectory file;

[0014] A module is used to determine the corresponding binding free energy based on the one or more data clusters.

[0015] Compared with the prior art, the present application determines the data information corresponding to each frame in the trajectory file based on the trajectory file of molecular dynamics simulation, and the data information corresponding to each frame in the trajectory file includes distance distribution information; based on the data information corresponding to each frame in the trajectory file, one or more data clusters are determined; based on the one or more data clusters, the corresponding binding free energy is determined. By clustering each frame of the trajectory file and calculating the binding free energy based on the clustering results, a large amount of computing resources can be saved and computing efficiency can be improved while ensuring the accuracy of the calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0017] Figure 1 A flow chart of a method for calculating binding free energy according to one embodiment of the present application is shown;

[0018] Figure 2A flow chart of a method for clustering frames of a trajectory file according to an embodiment of the present application is shown;

[0019] Figure 3 A flow chart of a method for calculating binding free energy according to one embodiment of the present application is shown;

[0020] Figure 4 A structural diagram of a device for calculating binding free energy according to one embodiment of the present application is shown;

[0021] Figure 5 An exemplary system is shown that can be used to implement the various embodiments described in this application.

[0022] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0023] The present application is described in further detail below in conjunction with the accompanying drawings.

[0024] In a typical configuration of the present application, the terminal, the device of the service network and the trusted party all include one or more processors (eg, a central processing unit (CPU)), an input / output interface, a network interface and a memory.

[0025] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory. Memory is an example of a computer-readable medium.

[0026] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, Phase-Change Memory (PCM), Programmable Random Access Memory (PRAM), Static Random-Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of random access memory (RAM), Read-Only Memory (ROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0027] The devices referred to in this application include but are not limited to user devices, network devices, or devices formed by integrating user devices and network devices through a network. The user devices include but are not limited to any mobile electronic products that can interact with users (for example, interact with users through a touchpad), such as smart phones, tablet computers, etc. The mobile electronic products can use any operating system, such as Android operating system, iOS operating system, etc. Among them, the network device includes an electronic device that can automatically perform numerical calculations and information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), digital signal processors (Digital Signal Processor, DSP), embedded devices, etc. The network devices include but are not limited to computers, network hosts, single network servers, multiple network server sets or multiple servers. The cloud is composed of a large number of computers or network servers based on cloud computing (Cloud Computing), wherein cloud computing is a type of distributed computing, a virtual supercomputer composed of a group of loosely coupled computer sets. The network includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a VPN network, a wireless self-organizing network (Ad Hoc network), etc. Preferably, the device may also be a program running on the user device, the network device, or a device formed by integrating the user device and the network device, the network device, the touch terminal, or the network device and the touch terminal through a network.

[0028] Of course, those skilled in the art should understand that the above-mentioned devices are only examples, and other existing or future devices that are applicable to the present application should also be included in the scope of protection of the present application and are included here by reference.

[0029] In the description of the present application, “plurality” means two or more, unless otherwise clearly and specifically defined.

[0030] Figure 1A flow chart of a method for calculating binding free energy according to an embodiment of the present application is shown, the method comprising: step S11, step S12 and step S13. In step S11, device 1 determines data information corresponding to each frame in the trajectory file based on the trajectory file of molecular dynamics simulation, wherein the data information corresponding to each frame in the trajectory file includes distance distribution information; in step S12, device 1 determines one or more data clusters based on the data information corresponding to each frame in the trajectory file; in step S13, device 1 determines corresponding binding free energy based on the one or more data clusters.

[0031] In step S11 , the device 1 determines data information corresponding to each frame in the trajectory file based on the trajectory file of the molecular dynamics simulation, where the data information corresponding to each frame in the trajectory file includes distance distribution information.

[0032] In some embodiments, the device 1 includes but is not limited to user devices and network devices with information processing or computing capabilities, such as tablet computers, computers, servers, etc.

[0033] In some embodiments, the molecular dynamics simulation refers to the use of the basic principles of Newtonian mechanics to generate a continuous configuration of a protein molecular system by integrating the equation of motion, thereby obtaining the motion trajectory of all atoms, that is, the process in which the position and velocity of each atom in the system change over time. Usually, simulation tools such as GROMACS (www.gromacs.org), AMBER (www.ambermd.org), or CHARMM (www.academiccharmm.org) can be used to perform the molecular dynamics simulation, thereby obtaining a corresponding trajectory file. The trajectory file contains detailed information of all atoms in the entire simulation process. The trajectory file is usually distinguished by frame, and each frame represents a snapshot at a specific time step. Each frame of the trajectory file usually contains the time step information associated with this frame, the coordinate information and velocity information of each atom in the time step, and other possible data (for example, temperature, pressure, size and shape of the simulation box, etc.).

[0034] In some embodiments, the device 1 can calculate the data information corresponding to each frame in the trajectory file based on the trajectory file, so as to be used for subsequent clustering of the frames of the trajectory file. The data information corresponding to each frame in the trajectory file includes distance distribution information. The distance distribution information includes the distance information of the target atom pairs in the frame. The target atom pair distance information can be the atom pair distance information calculated based on the specific atom pairs selected based on actual needs. For example, the atom pair distance between the atoms of the substrate (a small molecule that interacts with the protein biomolecule in the simulation) and the atom pair distance between the atoms of a part of the substrate and the atoms of the protein, the atom pair distance between the atoms of the substrate and the atoms of the protein, the atom pair distance between the atoms of the substrate and the atoms of the protein, and the atom pair distance between the atoms of the substrate and the atoms of the protein, etc.

[0035] In some embodiments, the data information corresponding to each frame in the trajectory file also includes the root mean square deviation of the protein structure and the solvent accessible surface area. The root mean square deviation of the protein structure refers to the root mean square deviation (RMSD) of the protein structure in the frame obtained by calculation relative to the initial structure or the average structure. The solvent accessible surface area (SASA) refers to the surface area of ​​the protein molecule accessible to the solvent. Here, those skilled in the art should understand that the above data information is only for example, and other existing or future data information that may appear, if applicable to the present application, should also be included in the scope of protection of the present application, and is included here by reference.

[0036] In some embodiments, the distance distribution information includes target atom pair distance information; the step S11 includes: device 1 determines the target atom in the substrate and the target atom in the protein for each frame in the trajectory file, wherein the target atom in the substrate and the target atom in the protein meet preset screening conditions; and determines the target atom pair distance information corresponding to the target atom in the substrate and the target atom in the protein.

[0037] In some embodiments, corresponding screening conditions can be designed based on actual needs to select corresponding target atoms. For example, substrate target atoms and protein target atoms that are directly related to protein functions can be selected, or substrate target atoms and protein target atoms can be selected based on different types of interactions such as hydrogen bonds, hydrophobic interactions, ionic bonds, and van der Waals forces, or substrate target atoms and protein target atoms that are close to each other in three-dimensional space can be selected. In some embodiments, the preset screening conditions include at least one of the following: the spatial distance between the target atom in the substrate and the target atom in the protein meets the preset distance condition; the charge number of the target atom in the substrate and the target atom in the protein meets the preset charge number condition. For example, select a spatial distance that is less than a distance threshold (for example, the distance threshold can be set to ) of the target atom in the substrate and the target atom in the protein, and / or the charge number of the selected target atom in the substrate and the target atom in the protein is an integer. After the target atom in the substrate and the target atom in the protein are selected, the distance between the two is directly calculated based on their coordinate information to obtain the distance information of the target atom pair.

[0038] In step S12, the device 1 determines one or more data clusters based on the data information corresponding to each frame in the trajectory file. For example, the device 1 can cluster the frames in the trajectory file based on the data information corresponding to each frame in the trajectory file using a corresponding clustering algorithm (e.g., a K-means clustering algorithm, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, a hierarchical clustering algorithm, etc.) to obtain one or more data clusters. Each data cluster includes at least one frame in the trajectory file.

[0039] In some embodiments, reference Figure 2The flowchart of the method for clustering each frame of the trajectory file is shown, and the step S12 includes: step S121, device 1 performs dimensionality reduction processing on the data information corresponding to each frame in the trajectory file to obtain the data information after dimensionality reduction; step S122, device 1 performs clustering processing on the data information after dimensionality reduction to determine one or more data clusters. In some embodiments, in order to save computing resources and improve computing efficiency, device 1 can first perform dimensionality reduction processing on the data information corresponding to each frame in the trajectory file, and then cluster the frames of the trajectory file based on the data information after dimensionality reduction. In some embodiments, dimensionality reduction algorithms such as t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), or multidimensional scaling (MDS) can be used to reduce the dimensionality of data information.

[0040] In some embodiments, the step S121 includes: step S1211 (not shown), device 1 determines the conditional probability distribution of the data information corresponding to each frame in the trajectory file in the original high-dimensional space; step S1212 (not shown), device 1 determines the initialization information of the data information corresponding to each frame in the trajectory file in the low-dimensional space and the corresponding conditional probability distribution in the low-dimensional space; step S1213 (not shown), device 1 iteratively optimizes the initialization information based on the conditional probability distribution in the original high-dimensional space and the conditional probability distribution in the low-dimensional space to obtain the corresponding data information after dimensionality reduction.

[0041] For example, the device 1 can calculate the similarity between the data information corresponding to each frame in the trajectory file using methods such as Euclidean distance or cosine similarity, thereby obtaining a similarity matrix of these data information in the original high-dimensional space. The similarity matrix can be normalized and converted into a probability form to form a conditional probability distribution of the data information in the original high-dimensional space. Before iterative optimization, initialization dimensionality reduction can be performed to obtain the initial value (i.e., initialization information) corresponding to the data information in the low-dimensional space. In some embodiments, the initialization information of the data information corresponding to each frame in the trajectory file in the low-dimensional space includes: linear dimensionality reduction of the data information corresponding to each frame in the trajectory file to obtain the initialization information of the data information corresponding to each frame in the trajectory file in the low-dimensional space. For example, a linear dimensionality reduction method such as principal component analysis (PCA, Principal Components Analysis) can be used to reduce the dimensionality of the data information in the original high-dimensional space to obtain the initialization information corresponding to these data information in the low-dimensional space. In some embodiments, these data information can also be randomly initialized to obtain random initialization information in the low-dimensional space. For the initialization information obtained in the low-dimensional space, the same method as the conditional probability distribution of the data information in the original high-dimensional space can be used to calculate the corresponding conditional probability distribution in the low-dimensional space. The difference between the conditional probability distribution of the original high-dimensional space and the low-dimensional space is measured by the corresponding objective function, and the initialization information is optimized by the gradient descent algorithm. The objective function is minimized through continuous iterative optimization, and finally the data information after dimensionality reduction is obtained.

[0042] In some embodiments, step S1213 includes: device 1 determines the relative entropy between the conditional probability distribution in the original high-dimensional space and the conditional probability distribution in the low-dimensional space; iteratively optimizes the initialization information by minimizing the relative entropy to obtain the corresponding data information after dimensionality reduction. For example, the difference between the original high-dimensional space and the conditional probability distribution in the low-dimensional space can be measured by relative entropy. Use the gradient descent algorithm to optimize the data information corresponding to the information in the low-dimensional space to minimize the relative entropy. Continue to iterate and optimize until the corresponding stop condition is met (for example, the maximum number of iterations is reached or the relative entropy change between two consecutive iterations is less than a preset threshold, etc.), and obtain the final data information after dimensionality reduction.

[0043] In step S13, the device 1 determines the corresponding binding free energy based on the one or more data clusters. For example, each data cluster includes at least one frame in the trajectory file. The device 1 can select a frame from each data cluster. The binding free energy corresponding to the selected frame in each data cluster is calculated by the MM-PBSA method. Based on the binding free energy corresponding to the selected frame in each data cluster, the final binding free energy is determined.

[0044] In some embodiments, reference Figure 3 The flowchart of the method for calculating binding free energy is shown in the figure, and the step S13 includes: step S131 (not shown), device 1 determines the target frame information corresponding to each data cluster in the one or more data clusters; step S132 (not shown), device 1 determines the corresponding binding free energy based on the target frame information. In some embodiments, the target frame information includes a target frame corresponding to each data cluster. The target frame can be a frame randomly selected from the data cluster; or it can be a frame closest to the cluster center in the data cluster. Device 1 determines the binding free energy corresponding to each target frame information by the MM-PBSA method, and then determines the corresponding binding free energy. For example, the binding free energy corresponding to each target frame information can be averaged; or the weight corresponding to each target frame can be determined, and the binding free energy can be determined in combination with the weight.

[0045] In some embodiments, the target frame information also includes weight information corresponding to the target frame; the step S132 includes: determining the binding free energy corresponding to the target frame; and determining the corresponding binding free energy based on the binding free energy corresponding to the target frame and the weight information corresponding to the target frame. In some embodiments, the ratio of the number of frames contained in the data cluster corresponding to the target frame to the total number of frames can be used as the weight information corresponding to the target frame. The binding free energy of the target frame determined by the MM-PBSA method and the corresponding weight information are inner-producted to obtain the final binding free energy of the trajectory.

[0046] In addition, the inventor conducted a test experiment with a set of 6 trajectory files, each with 250 frames. Table 1 is a comparison table of the calculation results of all frames using the method described in this application and traditional calculation. Table 2 is a comparison table of the calculation time consumption of the two methods. It can be seen from the following two tables that while ensuring the accuracy of the calculation, the present application can greatly improve the calculation efficiency.

[0047] Table 1 Comparison of calculation results

[0048] Trajectory File This application method Calculate all frames Calculate the difference in results D09 -14.04 -14.09 0.05 D10 -16.54 -16.24 -0.30 D11 -16.86 -17.01 0.15 D13 -19.13 -18.55 -0.58 D14 -21.51 -21.54 0.03 D15 -23.61 -23.89 0.28

[0049] Table 2 Comparison of calculation time

[0050]

[0051] Figure 4The structure diagram of a device for calculating binding free energy according to an embodiment of the present application is shown, and the device 1 includes a module 11, a module 12, and a module 13. The module 11 determines the data information corresponding to each frame in the trajectory file based on the trajectory file of molecular dynamics simulation, and the data information corresponding to each frame in the trajectory file includes distance distribution information; the module 12 determines one or more data clusters based on the data information corresponding to each frame in the trajectory file; the module 13 determines the corresponding binding free energy based on the one or more data clusters. Here, the Figure 4 The specific implementations corresponding to the one-one module 11, the one-two module 12 and the one-three module 13 shown are respectively the same as or similar to the specific embodiments of the aforementioned step S11, step S12 and step S13, so they are not repeated here and are included here by reference.

[0052] In some embodiments, the one-two module 12 includes a one-two-one unit 121 and a one-two-two unit 122. The one-two-one unit 121 performs dimensionality reduction processing on the data information corresponding to each frame in the trajectory file to obtain the data information after dimensionality reduction; the one-two-two unit 122 performs clustering processing on the data information after dimensionality reduction to determine one or more data clusters. Here, the specific implementation methods of the one-two-one unit 121 and the one-two-two unit 122 are respectively the same or similar to the specific implementation methods of the aforementioned step S121 and step S122, so they are not repeated here and are included here by reference.

[0053] In some embodiments, the one-two-one unit 121 includes a one-two-one-one subunit 1211 (not shown), a one-two-one-two subunit 1212 (not shown) and a one-two-one-three subunit 1213 (not shown). The one-two-one-one subunit 1211 determines the conditional probability distribution of the data information corresponding to each frame in the trajectory file in the original high-dimensional space; the one-two-one-two subunit 1212 determines the initialization information of the data information corresponding to each frame in the trajectory file in the low-dimensional space and the corresponding conditional probability distribution in the low-dimensional space; the one-two-one-three subunit 1213 iteratively optimizes the initialization information based on the conditional probability distribution in the original high-dimensional space and the conditional probability distribution in the low-dimensional space to obtain the corresponding data information after dimensionality reduction. Here, the specific implementation methods of the one-two-one-one subunit 1211, the one-two-one-two subunit 1212 and the one-two-one-three subunit 1213 are respectively the same or similar to the specific embodiments of the aforementioned steps S1211, step S1212 and step S1213, so they are not repeated here and are included here by reference.

[0054] In some embodiments, the one-three module 13 includes a one-three-one unit 131 and a one-three-two unit 132. The one-three-one unit 131 determines the target frame information corresponding to each data cluster in the one or more data clusters; the one-three-two unit 132 determines the corresponding binding free energy based on the target frame information. Here, the specific implementation methods of the one-three-one unit 131 and the one-three-two unit 132 are respectively the same or similar to the specific embodiments of the aforementioned step S131 and step S132, so they are not repeated here and are included here by reference.

[0055] Figure 5 An exemplary system is shown that can be used to implement the various embodiments described in this application.

[0056] like Figure 5 In some embodiments shown, the system 300 can be used as any of the devices in the various described embodiments. In some embodiments, the system 300 may include one or more computer-readable media (e.g., system memory or NVM / storage device 320) with instructions and one or more processors (e.g., (one or more) processors 305) coupled to the one or more computer-readable media and configured to execute instructions to implement modules to perform the actions described in this application.

[0057] For one embodiment, system control module 310 may include any suitable interface controller to provide any suitable interface to at least one of processor(s) 305 and / or any suitable device or component in communication with system control module 310 .

[0058] The system control module 310 may include a memory controller module 330 to provide an interface to the system memory 315. The memory controller module 330 may be a hardware module, a software module, and / or a firmware module.

[0059] The system memory 315 may be used, for example, to load and store data and / or instructions for the system 300. For one embodiment, the system memory 315 may include any suitable volatile memory, such as a suitable DRAM. In some embodiments, the system memory 315 may include double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0060] For one embodiment, system control module 310 may include one or more input / output (I / O) controllers to provide interfaces to NVM / storage device 320 and communication interface(s) 325 .

[0061] For example, NVM / storage device 320 may be used to store data and / or instructions. NVM / storage device 320 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable non-volatile storage device(s) (e.g., one or more hard disk drives (HDDs), one or more compact disk (CD) drives, and / or one or more digital versatile disk (DVD) drives).

[0062] NVM / storage device 320 may include storage resources that are physically part of the device on which system 300 is installed, or it may be accessible to the device without being part of the device. For example, NVM / storage device 320 may be accessed over a network via communication interface(s) 325.

[0063] Communication interface(s) 325 may provide an interface for system 300 to communicate over one or more networks and / or with any other suitable devices. System 300 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and / or protocols.

[0064] For one embodiment, at least one of the processor(s) 305 may be packaged together with the logic of one or more controllers of the system control module 310 (e.g., the memory controller module 330). For one embodiment, at least one of the processor(s) 305 may be packaged together with the logic of one or more controllers of the system control module 310 to form a system-in-package (SiP). For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with the logic of one or more controllers of the system control module 310. For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with the logic of one or more controllers of the system control module 310 to form a system on chip (SoC).

[0065] In various embodiments, the system 300 may be, but is not limited to: a server, a workstation, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the system 300 may have more or fewer components and / or a different architecture. For example, in some embodiments, the system 300 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit (ASIC), and a speaker.

[0066] In addition to the methods and devices described in the above embodiments, the present application also provides a computer-readable storage medium, which stores computer code. When the computer code is executed, the method described in any of the preceding items is executed.

[0067] The present application also provides a computer program product. When the computer program product is executed by a computer device, the method described in any of the preceding items is executed.

[0068] The present application also provides a computer device, the computer device comprising:

[0069] one or more processors;

[0070] a memory for storing one or more computer programs;

[0071] When the one or more computer programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any of the preceding items.

[0072] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0073] In addition, a part of the present application may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes but is not limited to source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0074] Communication media include media by which communication signals containing, for example, computer readable instructions, data structures, program modules, or other data are transmitted from one system to another. Communication media may include guided transmission media such as cables and wires (e.g., fiber optic, coaxial, etc.) and wireless (unguided transmission) media that can propagate energy waves, such as acoustic, electromagnetic, RF, microwave, and infrared. Computer readable instructions, data structures, program modules, or other data may be embodied as a modulated data signal in, for example, a wireless medium such as a carrier wave or similar mechanism such as embodied as part of spread spectrum technology. The term "modulated data signal" refers to a signal whose one or more characteristics are changed or set in such a manner as to encode information in the signal. Modulation may be analog, digital, or a hybrid modulation technique.

[0075] By way of example and not limitation, computer-readable storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memory, such as random access memory (RAM, DRAM, SRAM); and non-volatile memory, such as flash memory, various read-only memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories (MRAM, FeRAM); and magnetic and optical storage devices (hard disks, magnetic tapes, CDs, DVDs); or other media now known or later developed that can store computer-readable information / data for use with a computer system.

[0076] Here, according to an embodiment of the present application, a device is included, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run the methods and / or technical solutions based on the aforementioned multiple embodiments of the present application.

[0077] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for calculating binding free energy, wherein: The method comprises: Based on the trajectory file of molecular dynamics simulation, determining data information corresponding to each frame in the trajectory file, wherein the data information corresponding to each frame in the trajectory file includes distance distribution information; Determine one or more data clusters based on data information corresponding to each frame in the trajectory file; Based on the one or more data clusters, corresponding binding free energies are determined.

2. The method according to claim 1, wherein: The distance distribution information includes target atom pair distance information; The trajectory file based on molecular dynamics simulation determines the data information corresponding to each frame in the trajectory file, wherein the data information corresponding to each frame in the trajectory file includes distance distribution information including: For each frame in the trajectory file, determining a target atom in the substrate and a target atom in the protein, wherein the target atom in the substrate and the target atom in the protein meet a preset screening condition; Determine target atom pair distance information between the target atom in the substrate and the target atom in the protein.

3. The method according to claim 2, wherein: The preset screening condition includes at least one of the following: The spatial distance between the target atom in the substrate and the target atom in the protein satisfies a preset distance condition; The charge numbers of the target atom in the substrate and the target atom in the protein meet a preset charge number condition.

4. The method according to any one of claims 1 to 3, wherein: The determining one or more data clusters based on the data information corresponding to each frame in the trajectory file includes: Performing dimensionality reduction processing on the data information corresponding to each frame in the trajectory file to obtain the data information after dimensionality reduction; The data information after dimension reduction is clustered to determine one or more data clusters.

5. The method according to claim 4, wherein: The performing dimensionality reduction processing on the data information corresponding to each frame in the trajectory file to obtain the data information after dimensionality reduction includes: Determine the conditional probability distribution of the data information corresponding to each frame in the trajectory file in the original high-dimensional space; Determine initialization information of data information corresponding to each frame in the trajectory file in the low-dimensional space and the corresponding conditional probability distribution in the low-dimensional space; Based on the conditional probability distribution in the original high-dimensional space and the conditional probability distribution in the low-dimensional space, the initialization information is iteratively optimized to obtain corresponding data information after dimensionality reduction.

6. The method according to claim 5, wherein: The iterative optimization of the initialization information based on the conditional probability distribution in the original high-dimensional space and the conditional probability distribution in the low-dimensional space to obtain the corresponding data information after dimensionality reduction includes: Determine the relative entropy between the conditional probability distribution in the original high-dimensional space and the conditional probability distribution in the low-dimensional space; The initialization information is iteratively optimized by minimizing the relative entropy to obtain corresponding data information after dimensionality reduction.

7. The method according to claim 5 or 6, wherein: The determining of the initialization information of the data information corresponding to each frame in the trajectory file in the low-dimensional space includes: Linear dimensionality reduction is performed on the data information corresponding to each frame in the trajectory file to obtain initialization information of the data information corresponding to each frame in the trajectory file in a low-dimensional space.

8. The method according to any one of claims 1 to 7, wherein: Determining the corresponding binding free energy based on the one or more data clusters comprises: Determining target frame information corresponding to each data cluster in the one or more data clusters; Based on the target frame information, the corresponding binding free energy is determined.

9. The method according to claim 8, wherein: The target frame information includes a target frame corresponding to each data cluster, and the target frame is closest to a cluster center in the data cluster.

10. The method according to claim 9, wherein: The target frame information also includes weight information corresponding to the target frame; Determining the corresponding binding free energy based on the target frame information includes: determining a binding free energy corresponding to the target frame; Based on the binding free energy corresponding to the target frame and the weight information corresponding to the target frame, the corresponding binding free energy is determined.

11. The method according to any one of claims 1 to 10, wherein: The data information corresponding to each frame in the trajectory file also includes the root mean square deviation of the protein structure and the solvent accessible surface area.

12. A computer device for calculating binding free energy, comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 11.

13. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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