Information processing program, information processing method, and information processing device

Abstract

To improve the efficiency of coarse-grained molecular dynamics simulations. [Solution] For each of the multiple initial conditions, the information processing device 100 acquires a first trajectory 110 having a time length of a first multiple obtained from a molecular dynamics simulation of the target substance performed under that initial condition. For each of the multiple multiples, the information processing device 100 generates a second trajectory 120 obtained by combining all or part of the data having a time length of that multiple in each of the acquired first trajectories 110. Based on the feature quantities of the DCCM 130 corresponding to each of the generated second trajectories 120, the information processing device 100 controls how to set the potential for the coarse-grained molecular dynamics simulation.

Description

[Technical Field]

[0001] The present invention relates to an information processing program, an information processing method, and an information processing apparatus. [Background technology]

[0002] Traditionally, molecular dynamics simulation techniques have been used in fields such as drug discovery to investigate structural changes in proteins. Specifically, from the perspective of computational efficiency, there is a technique called coarse-grained molecular dynamics simulation, which treats proteins coarsely by setting beads at the amino acid residue level of the protein rather than at the atomic level, and setting springs to connect the beads.

[0003] Prior art includes, for example, techniques for determining parameters for charge equilibrium methods in molecular dynamics simulations. Other techniques include, for example, techniques for calculating interaction energies between target molecules and candidate drug molecules. Furthermore, for example, techniques for identifying compounds that bind to target proteins. And, for example, techniques for predicting or verifying the effectiveness of sirtuin-activating compounds for the binding of nicotinamide adenine dinucleotide to sirtuins. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2011-96241 [Patent Document 2] International Publication No. 2016 / 051587 [Patent Document 3] U.S. Patent Application Publication No. 2018 / 0312999 [Patent Document 4] U.S. Patent Application Publication No. 2022 / 0215904 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] However, in the prior art, it is difficult to efficiently perform coarse-grained molecular dynamics simulations. Specifically, when performing a coarse-grained molecular dynamics simulation, it is difficult to set a spring with an appropriate strength to connect the beads.

[0006] On one aspect, the present invention aims to improve the efficiency of coarse-grained molecular dynamics simulations.

Means for Solving the Problems

[0007] According to one embodiment, for each of a plurality of initial conditions, a first trajectory having a time length of a first multiple of a predetermined time length, which represents the behavior of molecules over time obtained by performing a molecular dynamics simulation on a target substance performed under the initial conditions, is acquired. For each of a plurality of multiples of the predetermined time length that are less than or equal to the first multiple and include the first multiple, a second trajectory obtained by combining all or part of the data having the time length of the multiple in each of the acquired first trajectories is generated. Based on the feature amount of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, an information processing program, an information processing method, and an information processing apparatus are proposed that control the implementation of potential setting for the coarse-grained molecular dynamics simulation of the target substance according to the time length corresponding to any one of the feature amounts that satisfy a predetermined condition.

Effects of the Invention

[0008] According to one aspect, it becomes possible to improve the efficiency of coarse-grained molecular dynamics simulations.

Brief Description of the Drawings

[0009] [Figure 1] FIG. 1 is an explanatory diagram showing an example of an information processing method according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram showing an example of an information processing system 200. [Figure 3]Figure 3 is a block diagram showing an example of the hardware configuration of the information processing device 100. [Figure 4] Figure 4 is a block diagram showing an example of the functional configuration of the information processing device 100. [Figure 5] Figure 5 is an explanatory diagram (part 1) showing an example of the operation of the information processing device 100. [Figure 6] Figure 6 is an explanatory diagram (part 2) showing an example of the operation of the information processing device 100. [Figure 7] Figure 7 is an explanatory diagram showing an example of the effects of the information processing device 100. [Figure 8] Figure 8 is a flowchart showing an example of the overall processing procedure. [Modes for carrying out the invention]

[0010] Embodiments of the information processing program, information processing method, and information processing apparatus according to the present invention will be described in detail below with reference to the drawings.

[0011] (An embodiment of the information processing method according to the embodiment) Figure 1 is an explanatory diagram showing one embodiment of the information processing method according to the embodiment. The information processing device 100 is a computer for improving the efficiency of coarse-grained molecular dynamics simulations. The information processing device 100 is, for example, a server or a PC (Personal Computer).

[0012] Coarse-grained molecular dynamics simulation is a coarse-grained molecular dynamics calculation. Coarse-grained molecular dynamics simulation is used, for example, in the field of drug discovery to investigate the structural changes of proteins. Coarse-grained molecular dynamics simulation, from the perspective of computational efficiency, for example, uses a coarse-grained model to coarsely handle proteins. The coarse-grained model includes, for example, a plurality of beads representing different elements that form a protein. Each pair of beads is connected by springs with different strengths. The springs correspond to a potential indicating the interaction between the beads. The coarse-grained model is, for example, a Grained Elastic Network Model. The coarse-grained model is also called, for example, a bead-spring model.

[0013] Specifically, in coarse-grained molecular dynamics simulation, beads are set in units of amino acid residues of a protein rather than in atomic units. The potential is specifically defined by the following formula (1). Here, i and j are the indices of the beads. K ij (r ij -r ij,0 ) is the strength of the spring connecting the beads of i and j. K ij is the spring constant. r ij is the distance between the beads of i and j. r ij,0 is the reference value for the distance between the beads of i and j. K ij (r ij -r ij,0 ) indicates the action of the spring where the energy increases as the distance between the beads of i and j deviates from the reference value. For the Grained Elastic Network Model, specifically, Reference 1 below can be referred to.

[0014]

Number

[0015] Reference 1: Lyman, Edward, Jim Pfaendtner, and Gregory A. Voth. “Systematic multiscale parameterization of heterogeneous elastic network models of proteins.” Biophysical journal 95.9 (2008): 4183-4192.

[0016] Therefore, when performing coarse-grained molecular dynamics simulations, the spring strength must be determined. By determining the spring strength, the spring arrangement can be specified. For example, setting the spring strength between any two beads to 0 essentially means that no spring is placed between those two beads.

[0017] Here, a method can be considered for determining the spring strength using a Dynamic Cross-correlation Coefficient Map. Specifically, the spring strength can be determined based on the strength of the correlation in the Dynamic Cross-correlation Coefficient Map, which is generated based on the trajectory obtained from molecular dynamics simulations. Molecular dynamics simulations are molecular dynamics calculations. Each coefficient C in the Dynamic Cross-correlation Coefficient Map ij Specifically, it is defined by the following formula (2). Here, i and j are indices of amino acid residues. i The coordinates are those of the amino acid residue i. (Arrows are indicated.) <r i > is r with an arrow. i This is the time average. For details on the dynamic cross-correlation coefficient map, please refer to Reference 2 below.

[0018]

number

[0019] Reference 2: Kanada, Ryo, et al. “Enhanced conformational sampling with an adaptive coarse-grained elastic network model using short-time all-atom molecular dynamics.” Journal of Chemical Theory and Computation 18.4 (2022): 2062-2074.

[0020] However, conventional techniques make it difficult to efficiently perform coarse-grained molecular dynamics simulations. For example, it is difficult to appropriately determine the spring strength when performing coarse-grained molecular dynamics simulations. Specifically, the type of dynamic cross-correlation coefficient map generated differs depending on the trajectory duration. Therefore, the determined spring strength also differs depending on the trajectory duration. Furthermore, it is unclear what trajectory duration is preferable from the standpoint of efficiency or accuracy in determining the spring strength. In addition, attempting to prepare a trajectory with a long duration in order to appropriately determine the spring strength leads to an increase in the processing load and processing time required for coarse-grained molecular dynamics simulations. Consequently, it is difficult to efficiently perform coarse-grained molecular dynamics simulations.

[0021] Therefore, this embodiment describes an information processing method that can improve the efficiency of coarse-grained molecular dynamics simulations. In the following description, the dynamic cross-correlation coefficient map may be referred to as "DCCM".

[0022] In Figure 1, the information processing device 100 stores several initial conditions for performing molecular dynamics simulations of a target substance. The target substance is, for example, a protein. The initial conditions include, for example, the initial positions of the atoms forming the target substance, or the initial velocities of the atoms forming the target substance.

[0023] The information processing device 100 stores a predetermined time length. The information processing device 100 sets multiple multiples of the predetermined time length. The multiple multiples include the first multiple of the predetermined time length, and each is less than or equal to the first multiple of the predetermined time length. The first multiple is set, for example, by the user. In the example in Figure 1, the first multiple is specifically 3 times. Also in the example in Figure 1, the multiple multiples are specifically 1 time, 2 times, and 3 times.

[0024] (1-1) For each of the multiple initial conditions, the information processing device 100 obtains a first trajectory 110 having a time length of a first multiple, which is obtained from a molecular dynamics simulation of the target substance performed under that initial condition. The molecular dynamics simulation estimates the behavior of the target substance over a period of a specific time length. Here, the specific time length is a time length of a first multiple. The first trajectory 110 is data that represents the behavior of molecules over time with respect to a time length of a first multiple. For example, the information processing device 100 obtains multiple first trajectories 110 by performing a molecular dynamics simulation of the target substance under each of the multiple initial conditions. In the example in Figure 1, the information processing device 100 specifically obtains first trajectories 111 to 113.

[0025] (1-2) For each of the multiples, the information processing device 100 generates a second trajectory 120 obtained by combining all or part of the data in each of the acquired first trajectories 110 that have a time length corresponding to that multiple. For example, for each of the multiples, the information processing device 100 generates the second trajectory 120 by combining all or part of the data in each of the first trajectories 110 that have a time length corresponding to that multiple. In the example of Figure 1, the information processing device 100 specifically generates second trajectories 121 to 123.

[0026] Specifically, the second trajectory 121 is data obtained by combining all the data from each of the first trajectories 111 to 113 that have a time length three times a predetermined length. Specifically, the second trajectory 122 is data obtained by extracting and combining a portion of the data from the beginning of each of the first trajectories 111 to 113 that has a time length twice a predetermined length. Specifically, the second trajectory 123 is data obtained by extracting and combining a portion of the data from the beginning of each of the first trajectories 111 to 113 that has a time length one time time. As a result, the information processing device 100 can prepare multiple second trajectories 120, each with a different time length.

[0027] (1-3) The information processing device 100 controls how to set the potential for the coarse-grained molecular dynamics simulation based on the feature quantities of the DCCM 130 corresponding to each of the generated second trajectories 120. The coarse-grained molecular dynamics simulation is related to the substance of interest. For example, the coarse-grained molecular dynamics simulation investigates the properties of the substance of interest.

[0028] The potential is a setpoint that defines the spring properties in the coarse-grained elastic network model used in coarse-grained molecular dynamics simulations. The setpoint, for example, represents the spring strength. Feature quantities include the sum of coefficients in DCCM130, the ratio of positive to negative coefficients in DCCM130, or the sum of the convergence rates of each cell in DCCM130.

[0029] The information processing device 100 generates DCCM 130 corresponding to each of the generated second trajectories 120. In the example in Figure 1, the information processing device 100 specifically generates DCCM 131 to 133. The information processing device 100 calculates the feature quantities of each of the generated DCCM 130. Based on the calculated feature quantities, the information processing device 100 controls the system to perform potential setting for coarse-grained molecular dynamics simulation according to the time length corresponding to any of the feature quantities that satisfy predetermined conditions.

[0030] For example, a predetermined condition for any first feature specifically includes at least one of a first condition and a second condition. The first condition specifically indicates that the first feature is less than or equal to a first threshold. The second condition specifically indicates that the change between the first feature and the second feature corresponding to the second time length which is the next longest time length after the first time length corresponding to the first feature is less than or equal to a second threshold. The first threshold is set, for example, by the user. The second threshold is set, for example, by the user. The information processing device 100 specifically performs the potential setting for the coarse-grained molecular dynamics simulation based on the DCCM 130 corresponding to the second trajectory 120 having the shortest time length corresponding to the feature less than or equal to the first threshold.

[0031] This allows the information processing device 100 to evaluate whether it is preferable to use each of the multiple time lengths when setting the potential for the coarse-grained molecular dynamics simulation. Therefore, the information processing device 100 can appropriately set the potential for the coarse-grained molecular dynamics simulation.

[0032] The information processing device 100 can, for example, appropriately set the trajectory time length when setting the potential for a coarse-grained molecular dynamics simulation. Specifically, the information processing device 100 can evaluate the validity of DCCM130 corresponding to each of multiple time lengths based on the features of DCCM130, and appropriately set the trajectory time length. Therefore, the information processing device 100 can improve the efficiency or accuracy of the coarse-grained molecular dynamics simulation.

[0033] (1-4) After setting the potential, the information processing device 100 performs a coarse-grained molecular dynamics simulation. The coarse-grained molecular dynamics simulation estimates the behavior of the target substance over a specific time period. Here, the coarse-grained dynamics simulation can estimate the behavior of the target substance over a longer time period than, for example, the dynamics simulation performed when generating the first trajectory. This allows the information processing device 100 to perform the coarse-grained molecular dynamics simulation efficiently and accurately. The information processing device 100 can investigate the properties of the target substance through the coarse-grained molecular dynamics simulation.

[0034] Here, we have described the case where the functions of the information processing device 100 are realized by a single computer, but this is not the only case. For example, the functions of the information processing device 100 may be realized through the collaboration of multiple computers. For example, the functions of the information processing device 100 may be realized on the cloud.

[0035] For example, the information processing device 100 may collaborate with other computers capable of performing molecular dynamics simulations to acquire multiple first trajectories. For example, the information processing device 100 may collaborate with other computers capable of performing coarse-grained molecular dynamics simulations to control the potential settings for the coarse-grained molecular dynamics simulations on the other computers. Specifically, an example of how the information processing device 100 collaborates with other computers will be described later with reference to Figure 2.

[0036] (An example of information processing system 200) Next, using Figure 2, we will describe an example of an information processing system 200 to which the information processing device 100 shown in Figure 1 is applied.

[0037] Figure 2 is an explanatory diagram showing an example of an information processing system 200. In Figure 2, the information processing system 200 includes an information processing device 100, a computing device 201, and a client device 202.

[0038] In the information processing system 200, the information processing device 100 and the computing device 201 are connected via a wired or wireless network 210. The network 210 is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet. Also in the information processing system 200, the information processing device 100 and the client device 202 are connected via a wired or wireless network 210.

[0039] The information processing device 100 is a computer for improving the efficiency of coarse-grained molecular dynamics simulations. The information processing device 100 receives a processing request from the client device 202 requesting to perform a coarse-grained molecular dynamics simulation. The processing request includes, for example, information indicating the substance to be subjected to the coarse-grained molecular dynamics simulation. The target substance is, for example, a protein. The processing request may also include, for example, initial conditions for performing the coarse-grained molecular dynamics simulation.

[0040] The information processing device 100 controls the potential settings for the coarse-grained molecular dynamics simulation of the target substance in response to a processing request. For example, the information processing device 100 sets the spring strength, which is the potential for the coarse-grained molecular dynamics simulation, based on DCCM corresponding to a trajectory with an appropriate time length.

[0041] The information processing device 100 sends a processing request to the computing device 201 requesting that it perform a coarse-grained molecular dynamics simulation including the set potential. The information processing device 100 receives the results of the coarse-grained molecular dynamics simulation from the computing device 201. The information processing device 100 sends the results of the coarse-grained molecular dynamics simulation to the client device 202. The information processing device 100 is, for example, a server or a PC.

[0042] Computing device 201 is a computer that performs coarse-grained molecular dynamics simulations. Computing device 201 receives a processing request from information processing device 100 requesting to perform a coarse-grained molecular dynamics simulation. Based on the processing request, computing device 201 sets the potential for the coarse-grained molecular dynamics simulation and performs the simulation. Computing device 201 transmits the results of the coarse-grained molecular dynamics simulation to information processing device 100. Computing device 201 is, for example, a server or a PC.

[0043] The client device 202 is a computer that sends a processing request to the information processing device 100 requesting to perform a coarse-grained molecular dynamics simulation. The client device 202 is used by users who wish to perform a coarse-grained molecular dynamics simulation. For example, the client device 202 generates a processing request based on user input and sends it to the information processing device 100.

[0044] The client device 202 receives the results of the coarse-grained molecular dynamics simulation from the information processing device 100. The client device 202 outputs the results of the coarse-grained molecular dynamics simulation so that the user can refer to them. The client device 202 may be, for example, a PC, a tablet terminal, or a smartphone.

[0045] This section describes a case where the information processing device 100 is a different computer from the computing device 201, but it is not limited to this case. For example, the information processing device 100 may have the functionality of a computing device 201 and may operate as a computing device 201. Similarly, this section describes a case where the information processing device 100 is a different computer from the client device 202, but it is not limited to this case. For example, the information processing device 100 may have the functionality of a client device 202 and may operate as a client device 202.

[0046] (Example of hardware configuration of information processing device 100) Next, an example of the hardware configuration of the information processing device 100 will be described using Figure 3.

[0047] Figure 3 is a block diagram showing an example of the hardware configuration of the information processing device 100. In Figure 3, the information processing device 100 includes a CPU (Central Processing Unit) 301, a memory 302, and a network interface 303. The information processing device 100 also includes a recording medium interface 304, a recording medium 305, a display 306, and an input device 307. Each component is connected by a bus 300.

[0048] Here, the CPU 301 is responsible for the overall control of the information processing device 100. The memory 302 includes, for example, ROM (Read Only Memory), RAM (Random Access Memory), and flash ROM. Specifically, for example, flash ROM and ROM store various programs, and RAM is used as the work area for the CPU 301. Programs stored in memory 302 are loaded into the CPU 301, causing the CPU 301 to execute the coded processes.

[0049] The network interface 303 is connected to network 210 via a communication line, and then connects to other computers via network 210. The network interface 303 manages the internal interface with network 210 and controls the input and output of data from other computers. The network interface 303 is, for example, a modem or a LAN adapter.

[0050] The recording medium interface (I / F) 304 controls the reading and writing of data to the recording medium 305 according to the control of the CPU 301. The recording medium interface (I / F) 304 is, for example, a disk drive, an SSD (Solid State Drive), or a USB (Universal Serial Bus) port. The recording medium 305 is a non-volatile memory that stores the data written under the control of the recording medium interface (I / F) 304. The recording medium 305 is, for example, a disk, semiconductor memory, or USB memory. The recording medium 305 may be detachable from the information processing device 100.

[0051] Display 306 displays data such as cursors, icons, toolboxes, documents, images, or functional information. Display 306 is, for example, a CRT (Cathode Ray Tube), a liquid crystal display, or an organic EL (Electroluminescence) display. Input device 307 has keys for inputting characters, numbers, or various instructions, and performs data input. Input device 307 is, for example, a keyboard or a mouse. Input device 307 may also be, for example, a touch panel input pad or a numeric keypad.

[0052] The information processing device 100 may have, in addition to the components described above, a camera, for example. Furthermore, the information processing device 100 may have, in addition to the components described above, a printer, scanner, microphone, or speaker, for example. Also, the information processing device 100 may have multiple recording medium interfaces 304 and recording mediums 305, for example. Furthermore, the information processing device 100 does not necessarily have, for example, a display 306 or an input device 307. Also, the information processing device 100 does not necessarily have, for example, recording medium interfaces 304 and recording mediums 305.

[0053] (Example of hardware configuration for computing device 201) The hardware configuration example of the computing device 201 is specifically the same as the hardware configuration example of the information processing device 100 shown in Figure 3, so a detailed explanation is omitted. Here, the computing device 201 does not necessarily have to have a display 306, an input device 307, etc.

[0054] (Example hardware configuration for client device 202) The hardware configuration example for client device 202 is specifically the same as the hardware configuration example for information processing device 100 shown in Figure 3, so a detailed explanation is omitted.

[0055] (Example of the functional configuration of the information processing device 100) Next, an example of the functional configuration of the information processing device 100 will be described using Figure 4.

[0056] Figure 4 is a block diagram showing an example of the functional configuration of the information processing device 100. The information processing device 100 includes a storage unit 400, an acquisition unit 401, a first setting unit 402, a first generation unit 403, a second generation unit 404, a third generation unit 405, a second setting unit 406, an implementation unit 407, and an output unit 408.

[0057] The storage unit 400 is implemented by a storage area such as the memory 302 or recording medium 305 shown in Figure 3. The following description will focus on the case where the storage unit 400 is included in the information processing device 100, but is not limited to this case. For example, the storage unit 400 may be included in a device different from the information processing device 100, and the contents of the storage unit 400 may be accessible from the information processing device 100.

[0058] The acquisition unit 401 to the output unit 408 function as an example of a control unit. Specifically, the acquisition unit 401 to the output unit 408 realize their functions, for example, by having the CPU 301 execute a program stored in a storage area such as the memory 302 or recording medium 305 shown in Figure 3, or by using the network I / F 303. The processing results of each functional unit are stored in a storage area such as the memory 302 or recording medium 305 shown in Figure 3.

[0059] The memory unit 400 stores various information that is referenced or updated during the processing of each functional unit. For example, the memory unit 400 stores N initial conditions for performing molecular dynamics simulations of a target substance. The target substance is, for example, a protein. The initial conditions describe, for example, the structure of the target substance. Specifically, the initial conditions include the initial positions of the atoms forming the target substance, or the initial velocities of the atoms forming the target substance. The initial conditions are acquired, for example, by the acquisition unit 401. The initial conditions are pre-set, for example, by the user.

[0060] The storage unit 400 stores, for example, a predetermined time length L. The storage unit 400 stores, for example, multiple multiples of the predetermined time length L. The multiple multiples include, for example, the first multiple M × L of the predetermined time length L. The multiple multiples are, for example, each less than or equal to the first multiple M × L. Specifically, the multiple multiples include k × L for each of several different integers k. k is less than or equal to M. The multiple multiples are acquired, for example, by the acquisition unit 401. The multiple multiples are set, for example, by the user. The multiple multiples are set, for example, by the first setting unit 402.

[0061] The memory unit 400 stores, for example, N first trajectories. The N first trajectories are obtained by molecular dynamics simulations of a target substance performed under different initial conditions. The first trajectories represent the behavior of molecules over time. The first trajectories have a time length of a first multiple M × L. The first trajectories are acquired, for example, by the acquisition unit 401. The first trajectories are pre-set, for example, by the user. The first trajectories are generated, for example, by the first generation unit 403.

[0062] The memory unit 400 stores, for example, M second trajectories. A second trajectory is obtained by combining all or part of the data in each of the N first trajectories. Each of the M second trajectories corresponds to a different k and is obtained by combining all or part of the data in each of the N first trajectories, each having a time length of any multiple k × L. Thus, a second trajectory has a time length of k × L × N. The second trajectories are generated, for example, by the second generation unit 404.

[0063] The memory unit 400 stores, for example, M DCCMs. Each of the M DCCMs corresponds to a different second trajectory. Each of the M DCCMs is generated, for example, based on a different second trajectory. The DCCMs represent, for example, the correlation coefficients between elements that are units of coarse-graining. The elements that are units of coarse-graining are, for example, amino acid residues. The DCCMs are generated, for example, by the third generation unit 405.

[0064] The memory unit 400 stores, for example, an evaluation function for calculating the features of a DCCM. The features are, for example, the sum of the coefficients in a DCCM. The features are, for example, the ratio of positive to negative coefficients in a DCCM. The features are, for example, the sum of the convergence rates of each cell in a DCCM. The evaluation function is, for example, pre-set by the user. The evaluation function is acquired, for example, by the acquisition unit 401. The memory unit 400 stores, for example, the features of each of the M DCCMs. The features are calculated, for example, by the third generation unit 405.

[0065] The memory unit 400 stores, for example, a coarse-grained elastic network model used in coarse-grained molecular dynamics simulations. The coarse-grained elastic network model includes a plurality of beads representing different elements that make up the material in question, and springs connecting the beads. The springs correspond to potentials that indicate the interactions between the beads. The potentials are, for example, set values ​​that define the properties of the springs. The potentials include, for example, the strength of the springs. Specifically, the memory unit 400 stores the potentials in the coarse-grained elastic network model. The coarse-grained elastic network model is, for example, pre-set by the user. The coarse-grained elastic network model is acquired, for example, by the acquisition unit 401.

[0066] The memory unit 400 stores predetermined conditions to be applied to the features of DCCM. For example, a predetermined condition for a first feature is that it satisfies at least one of a first condition for the first feature and a second condition for the first feature. For example, the first condition for the first feature indicates that the first feature is less than or equal to a first threshold. For example, the second condition for the first feature indicates that the change between the first feature and the second feature corresponding to the second time length which is the next longest time length after the first time length corresponding to the first feature is less than or equal to a second threshold. The predetermined conditions are set in advance by the user, for example. The predetermined conditions are acquired by the acquisition unit 401, for example.

[0067] The acquisition unit 401 acquires various types of information used in the processing of each functional unit. The acquisition unit 401 stores the acquired information in the storage unit 400 or outputs it to each functional unit. The acquisition unit 401 may also output the information stored in the storage unit 400 to each functional unit. The acquisition unit 401 acquires various types of information, for example, based on user input. The acquisition unit 401 may also receive various types of information from a device other than the information processing device 100, for example.

[0068] The acquisition unit 401 acquires a processing request, for example, a request to perform a coarse-grained molecular dynamics simulation. The processing request may include, for example, N initial conditions. The processing request may include, for example, multiple multiples of a predetermined time length L. The processing request may include, for example, an evaluation function. The processing request may include, for example, a coarse-grained elastic network model to be used as a template. The processing request may include, for example, predetermined conditions.

[0069] The acquisition unit 401 acquires, for example, N initial conditions. Specifically, the acquisition unit 401 acquires N initial conditions by accepting N initial conditions as input. Specifically, the acquisition unit 401 may acquire N initial conditions by receiving N initial conditions from another computer. The other computer is, for example, a client device 202. Specifically, the acquisition unit 401 may acquire N initial conditions by extracting N initial conditions from a processing request.

[0070] The acquisition unit 401 acquires, for example, multiple multiples of a predetermined time length L. Specifically, the acquisition unit 401 acquires multiple multiples of a predetermined time length L by receiving input of multiple multiples of a predetermined time length L. Specifically, the acquisition unit 401 may acquire multiple multiples of a predetermined time length L by receiving multiple multiples of a predetermined time length L from another computer. The other computer is, for example, a client device 202. Specifically, the acquisition unit 401 may acquire multiple multiples of a predetermined time length L by extracting multiple multiples of a predetermined time length L from a processing request.

[0071] The acquisition unit 401 acquires, for example, N first trajectories. The acquisition unit 401 acquires, for example, N first trajectories if the first generation unit 403 does not generate N first trajectories. Specifically, the acquisition unit 401 acquires N first trajectories by accepting input of N first trajectories. Specifically, the acquisition unit 401 may acquire N first trajectories by receiving N first trajectories from another computer. The other computer is, for example, a client device 202. Specifically, the acquisition unit 401 may acquire N first trajectories by extracting N first trajectories from a processing request.

[0072] The acquisition unit 401 acquires, for example, an evaluation function. Specifically, the acquisition unit 401 acquires an evaluation function by accepting an input of an evaluation function. Specifically, the acquisition unit 401 may acquire an evaluation function by receiving an evaluation function from another computer. The other computer may be, for example, a client device 202. Specifically, the acquisition unit 401 may acquire an evaluation function by extracting an evaluation function from a processing request.

[0073] The acquisition unit 401 acquires, for example, a coarse-grained elastic network model to be used as a template. Specifically, the acquisition unit 401 acquires a coarse-grained elastic network model by receiving an input of a coarse-grained elastic network model. Specifically, the acquisition unit 401 may acquire a coarse-grained elastic network model by receiving a coarse-grained elastic network model from another computer. The other computer is, for example, a client device 202. Specifically, the acquisition unit 401 may acquire a coarse-grained elastic network model by extracting a coarse-grained elastic network model from a processing request.

[0074] The acquisition unit 401 acquires, for example, predetermined conditions. Specifically, the acquisition unit 401 acquires predetermined conditions by accepting input of predetermined conditions. Specifically, the acquisition unit 401 may acquire predetermined conditions by receiving predetermined conditions from another computer. The other computer may be, for example, a client device 202. Specifically, the acquisition unit 401 may acquire predetermined conditions by extracting predetermined conditions from a processing request.

[0075] The acquisition unit 401 may receive a start trigger to initiate processing in any of the functional units. A start trigger may be, for example, a predetermined operation input by the user. A start trigger may also be, for example, the receipt of predetermined information from another computer. A start trigger may also be, for example, the output of predetermined information by any of the functional units.

[0076] The acquisition unit 401 may, for example, accept the acquisition of a processing request as a start trigger to initiate a series of processes involving the first setting unit 402, the first generation unit 403, the second generation unit 404, the third generation unit 405, and the second setting unit 406. In the following description, the information processing device 100 performs the series of processes involving the first setting unit 402, the first generation unit 403, the second generation unit 404, the third generation unit 405, and the second setting unit 406 one or more times.

[0077] The first setting unit 402 sets multiple multiples. For example, when performing the first series of processes, the first setting unit 402 sets an initial value for M and sets a multiplier k × L for each of several integers k less than or equal to M, thereby setting each of several multipliers including the first multiple M × L. For example, the first setting unit 402 sets a multiplier k × L for each of several integers k with a width a increment less than or equal to M. a is, for example, 1. a may be, for example, 2 or more. This allows the first setting unit 402 to prepare to perform the first series of processes.

[0078] If none of the features calculated by the third generation unit 405 meet the predetermined conditions, the first setting unit 402 resets multiple multiples by setting a new value for M that is greater than the current value of M when performing the series of processes from the second time onward. Specifically, the first setting unit 402 sets a new value for M that is the current value of M plus a width b. b is, for example, 1. b may also be, for example, 2 or more. Specifically, the first setting unit 402 sets a multiplier k × L for each of the multiple integers k with width a steps less than or equal to M. This allows the first setting unit 402 to prepare to perform the series of processes from the second time onward.

[0079] The first generation unit 403 is obtained by generating N first trajectories. For example, when performing the first series of processes, the first generation unit 403 generates first trajectories for each of several initial conditions by performing molecular dynamics simulations under those initial conditions. The molecular dynamics simulations relate to the substance in question. In this way, the first generation unit 403 can prepare various first trajectories that will serve as the basis for second trajectories.

[0080] The first generation unit 403, for example, when performing a series of processes from the second time onward, generates N new first trajectories, each having a time length of M × L, a first multiple set by the first setting unit 402, based on each of the N first trajectories acquired in the previous operation. Specifically, the first generation unit 403 generates N new first trajectories by extending each of the N first trajectories acquired in the previous operation to a time length of M × L, a first multiple set by the first setting unit 402, through molecular dynamics simulation.

[0081] Specifically, the first generation unit 403 may generate a new first trajectory having a time length of the first multiple M × L set in this instance, by performing molecular dynamics simulations for each of the multiple initial conditions. In this way, the first generation unit 403 can prepare various first trajectories that will serve as the basis for the second trajectory.

[0082] The second generation unit 404 acquires M second trajectories. The second generation unit 404 acquires these M second trajectories, for example, by generating them. Specifically, for each of several multiples, the second generation unit 404 generates the second trajectories by combining all or part of the data from each of the N first trajectories acquired by the first generation unit 403, each having a time length corresponding to that multiple. This allows the second generation unit 404 to obtain an index for considering the appropriate time length of trajectories for setting the potential in coarse-grained molecular dynamics simulations.

[0083] The third generation unit 405 generates DCCMs corresponding to each of the M second trajectories generated by the second generation unit 404. The third generation unit 405 calculates the feature quantities of each DCCM corresponding to each of the M second trajectories generated by the second generation unit 404. This allows the third generation unit 405 to determine whether or not predetermined conditions are met.

[0084] The second setting unit 406 controls the potential setting for the coarse-grained molecular dynamics simulation of the target substance based on M feature quantities calculated by the third generation unit 405, by referring to predetermined conditions. For example, if any of the M feature quantities calculated by the third generation unit 405 satisfies a predetermined condition, the second setting unit 406 controls the potential setting to be performed according to the time length corresponding to any of the feature quantities that satisfy the predetermined condition.

[0085] Specifically, the second setting unit 406 identifies a DCCM corresponding to a second trajectory corresponding to a time length corresponding to any feature quantity that satisfies predetermined conditions. Specifically, when the implementation unit 407 performs a coarse-grained molecular dynamics simulation, the second setting unit 406 sets a potential based on the identified DCCM and notifies the implementation unit 407. This enables the implementation unit 407 to perform the coarse-grained molecular dynamics simulation efficiently and accurately.

[0086] Specifically, when performing coarse-grained molecular dynamics simulations on another computer, the second setting unit 406 sets the potential based on the identified DCCM and notifies the output unit 408. This allows the second setting unit 406 to enable the other computer to properly set the potential via the output unit 408. As a result, the second setting unit 406 can perform coarse-grained molecular dynamics simulations on another computer efficiently and accurately.

[0087] Specifically, the second setting unit 406 may notify the output unit 408 of the time length corresponding to any feature quantity that satisfies predetermined conditions when performing coarse-grained molecular dynamics simulations on another computer. This allows the second setting unit 406 to provide information to the other computer via the output unit 408 for appropriate potential setting.

[0088] Specifically, when performing coarse-grained molecular dynamics simulations on another computer, the second setting unit 406 may notify the output unit 408 of a second trajectory corresponding to the time length of any feature that satisfies predetermined conditions. This allows the second setting unit 406 to provide information to the other computer via the output unit 408 for appropriate potential setting.

[0089] Specifically, the second setting unit 406 may notify the output unit 408 of the identified DCCM when performing coarse-grained molecular dynamics simulations on another computer. This allows the second setting unit 406 to provide information to the other computer via the output unit 408 for appropriate potential setting.

[0090] The second setting unit 406 controls the first setting unit 402 to repeat the series of processes without performing potential setting if, for example, none of the M feature quantities calculated by the third generation unit 405 satisfy the predetermined conditions. This allows the second setting unit 406 to repeatedly perform the series of processes so that potential setting can be performed appropriately.

[0091] The implementation unit 407 performs a coarse-grained molecular dynamics simulation based on the results of the potential setting. This allows the implementation unit 407 to perform the coarse-grained molecular dynamics simulation efficiently and accurately on its own device.

[0092] The output unit 408 outputs the processing result of at least one of the functional units. The output format is, for example, display on a display, print output to a printer, transmission to an external device via the network I / F 303, or storage in a storage area such as memory 302 or recording medium 305. This allows the output unit 408 to notify the user of the processing result of at least one of the functional units, thereby supporting the management and operation of the information processing device 100, such as updating the settings of the information processing device 100, and improving the usability of the information processing device 100.

[0093] The output unit 408 outputs the results of the coarse-grained molecular dynamics simulation performed by the implementation unit 407. This makes the results of the coarse-grained molecular dynamics simulation available externally.

[0094] The output unit 408 may, for example, transmit the potential set in the second setting unit 406 to another computer performing a coarse-grained molecular dynamics simulation. This enables the output unit 408 to perform the coarse-grained molecular dynamics simulation efficiently and accurately on the other computer.

[0095] The output unit 408 may, for example, transmit the time length corresponding to any feature quantity that satisfies predetermined conditions to another computer performing a coarse-grained molecular dynamics simulation. This allows the output unit 408 to enable the other computer to properly perform potential setting.

[0096] The output unit 408 may, for example, transmit a second trajectory corresponding to the time length of any feature quantity that satisfies predetermined conditions to another computer performing a coarse-grained molecular dynamics simulation. This allows the output unit 408 to enable the other computer to properly perform potential setting.

[0097] The output unit 408 may, for example, transmit the DCCM identified by the second setting unit 406 to another computer performing coarse-grained molecular dynamics simulations. This allows the output unit 408 to enable the other computer to properly perform potential setting.

[0098] Here, we have described the case in which the second generation unit 404 generates M second trajectories based on N first trajectories acquired by the first generation unit 403, but the invention is not limited to this case. For example, the second generation unit 404 may acquire M second trajectories, each having a different time length within a range below an upper limit, obtained by molecular dynamics simulation.

[0099] In this case, the first generation unit 403 does not need to acquire N first trajectories. In this case, the information processing device 100 will perform the series of processes of the first setting unit 402, the second generation unit 404, the third generation unit 405, and the second setting unit 406, excluding the first generation unit 403, at least once.

[0100] The first setting unit 402 sets an upper limit on the time length when performing the first series of processes. Specifically, the second generation unit 404 generates M second trajectories by performing molecular dynamics simulations under specific initial conditions. Specifically, if none of the features calculated by the third generation unit 405 satisfy the predetermined conditions, the first setting unit 402 sets a new upper limit on the time length that is greater than the current upper limit when performing the second and subsequent series of processes.

[0101] Here, we have described a case in which the information processing device 100 includes a first setting unit 402, a first generation unit 403, a second generation unit 404, a third generation unit 405, a second setting unit 406, and an implementation unit 407, but it is not limited to this. For example, the information processing device 100 may not include any of the functional units. Specifically, the information processing device 100 may cooperate with another computer that includes any of the functional units.

[0102] (Example of operation of the information processing device 100) Next, we will explain an example of the operation of the information processing device 100 using Figures 5 and 6.

[0103] Figures 5 and 6 are explanatory diagrams showing an example of the operation of the information processing device 100. In Figure 5, the information processing device 100 controls the variable k max Set k to 0. max This indicates the upper limit of the integer k.

[0104] The information processing device 100 acquires N initial conditions. The initial conditions describe, for example, the structure of the substance in question. The structure includes, for example, the initial positions of the atoms forming the substance, or the initial velocities of the atoms forming the substance. In the example in Figure 5, N=3. In the example in Figure 5, the information processing device 100 specifically acquires three structures 510, 520, and 530, each representing different initial conditions.

[0105] The information processing device 100 is k maxAdd Δk1 to this. Δk1 is, for example, 1 or greater. In the example in Figure 5, Δk1 is specifically 3.

[0106] (5-1) The information processing device 100 performs molecular dynamics simulations for each of the acquired N structures, max A first trajectory with a time length of ×L is generated. In the example in Figure 5, the information processing device 100 specifically generates a first trajectory 511 with a time length of 3 × L by performing a molecular dynamics simulation on the structure 510.

[0107] Furthermore, the information processing device 100 generates a first trajectory 521 with a time length of 3 × L by performing a molecular dynamics simulation on the structure 520. Furthermore, the information processing device 100 generates a first trajectory 531 with a time length of 3 × L by performing a molecular dynamics simulation on the structure 530.

[0108] (5-2) Information processing device 100 is k max For each of the following integers k, a second trajectory is generated by extracting and combining a portion of the data representing a time length of k × L in each of the N first trajectories. In the example in Figure 5, the information processing device 100 specifically generates the second trajectory 501 by extracting and combining a portion of the data representing a time length of 1 × L in each of the first trajectories 511, 521, and 531 for the integer k=1.

[0109] Furthermore, the information processing device 100 generates a second trajectory 502 by extracting and combining data representing a time length of 2 × L in each of the first trajectories 511, 521, and 531 for an integer k=2. Furthermore, the information processing device 100 generates a second trajectory 503 by extracting and combining all the data representing a time length of 3 × L in each of the first trajectories 511, 521, and 531 for an integer k=3.

[0110] As a result, the information processing device 100 examines the validity of each different time length k from the perspective of setting the potential for coarse-grained dynamics simulation. max A number of second trajectories can be prepared. In the example shown in Figure 5, the information processing device 100 can specifically prepare second trajectories 501 to 503, as shown in Graph 500.

[0111] (5-3) Information processing device 100 is k max DCCM is generated for each of the individual second trajectories. Based on the generated DCCM, the information processing device 100 generates k max For each of the following integers k, we calculate F(k). F(k) is defined, for example, by equation (3) below. Equation (3) below corresponds, for example, to the sum of the convergence rates of each cell in DCCM. Here, C ij k This shows the correlation coefficient in DCCM corresponding to the second trajectory of the time length corresponding to k. residue This indicates the number of residues. |C ij k |<10 -2 For this cell, it is preferable to consider the convergence rate value as 0.

[0112]

number

[0113] (5-4) The information processing device 100 determines whether the convergence conditions are met based on the calculated F(k). The convergence conditions are, for example, that at least one of the following conditions is met: a first condition that F(k) is less than or equal to a first threshold, and a second condition that |F(k)-F(k+1)| is less than or equal to a second threshold. The first threshold is, for example, 0.1. The second threshold is, for example, 0.01.

[0114] If the information processing device 100 satisfies the convergence condition for any k, it sets the DCCM corresponding to the smallest k that satisfies the convergence condition to be used for potential setting. This enables the information processing device 100 to perform potential setting appropriately.

[0115] On the other hand, if the convergence condition is not met for any k, the information processing device 100 will then... max Δk2 is added to k. Δk2 is, for example, 1 or greater. In the example in Figure 5, Δk2 is specifically 1. In the example in Figure 5, the information processing device 100 specifically determines that the convergence condition is not satisfied for any k, and k max Let's add Δk²=1 to it. Therefore, k max The answer is 4. Next, we will move on to the explanation of Figure 6.

[0116] In Figure 6, (6-1) the information processing device 100 extends each of the N generated first trajectories, so that each of them is k max N first trajectories with a time length of ×L minutes are obtained. The information processing device 100 extends the first trajectory by, for example, performing molecular dynamics simulations on the structure at the end of each of the N generated first trajectories.

[0117] In the example shown in Figure 6, the information processing device 100 specifically generates a first trajectory 611 with a time length of 4 × L by performing a molecular dynamics simulation on structure 610. Furthermore, the information processing device 100 specifically generates a first trajectory 621 with a time length of 4 × L by performing a molecular dynamics simulation on structure 620. Furthermore, the information processing device 100 specifically generates a first trajectory 631 with a time length of 4 × L by performing a molecular dynamics simulation on structure 630.

[0118] (6-2) Information processing device 100 is k max For each of the following integers k, a second trajectory is obtained by combining a portion of the data representing a time length of k × L in each of the N first trajectories. In the example in Figure 6, the information processing device 100 specifically obtains a second trajectory 601 for integer k=1 by combining a portion of the data representing a time length of 1 × L in each of the first trajectories 611, 621, and 631. More specifically, the information processing device 100 obtains the second trajectory 501 as the second trajectory 601. More specifically, the information processing device 100 may generate the second trajectory 601.

[0119] Furthermore, the information processing device 100 specifically obtains a second trajectory 602 by combining data representing the time length of 2 × L in each of the first trajectories 611, 621, and 631 for an integer k=2. More specifically, the information processing device 100 obtains the second trajectory 502 as the second trajectory 602. More specifically, the information processing device 100 may generate the second trajectory 602.

[0120] Furthermore, the information processing device 100 specifically obtains a second trajectory 603 by combining some of the data, which is the time length of 3 × L in each of the first trajectories 611, 621, and 631, for an integer k=3. More specifically, the information processing device 100 obtains the second trajectory 503 as the second trajectory 603. More specifically, the information processing device 100 may generate the second trajectory 603.

[0121] Furthermore, the information processing device 100 specifically obtains a second trajectory 604 for an integer k=4, which is obtained by combining all the data representing a time length of 4 × L in each of the first trajectories 611, 621, and 631. More specifically, the information processing device 100 obtains the second trajectory 604 by generating it.

[0122] As a result, the information processing device 100 examines the validity of each different time length k from the perspective of setting the potential for coarse-grained dynamics simulation. max k second trajectories can be prepared. Since the information processing device 100 utilizes N first trajectories, k max Each of the N second trajectories can reflect N initial conditions, improving its validity as a DCCM generator. As a result, the information processing device 100 can prepare appropriate second trajectories that contribute to improving the accuracy of potential setting in coarse-grained dynamics simulations. In the example in Figure 6, the information processing device 100 can specifically prepare second trajectories 601 to 604, as shown in Graph 600.

[0123] (6-3) Information processing device 100 is k max For each of the following integers k, F(k) is obtained. In the example in Figure 6, the information processing device 100 specifically obtains the calculated F(k) for the range k=1 to 3. Specifically, for k=4, the information processing device 100 obtains F(k) by generating a DCCM corresponding to the second trajectory and calculating it.

[0124] (6-4) The information processing device 100 determines whether the convergence conditions are met based on the acquired F(k). The convergence conditions are, for example, that at least one of the following conditions is met: a first condition that F(k) is less than or equal to a first threshold, and a second condition that |F(k)-F(k+1)| is less than or equal to a second threshold.

[0125] If the information processing device 100 satisfies the convergence condition for any k, it sets the DCCM corresponding to the smallest k that satisfies the convergence condition to be used for potential setting. This enables the information processing device 100 to perform potential setting appropriately.

[0126] On the other hand, if the convergence condition is not met for any k, the information processing device 100 will then... max Δk2 is added to k. Δk2 is, for example, 1 or greater. In the example in Figure 6, Δk2 is specifically 1. If the convergence condition is not satisfied for any of k, the information processing device 100 repeats the series of processes shown in Figure 6.

[0127] In the example shown in Figure 6, the information processing device 100 specifically determines that the convergence condition is satisfied for k=4. In this case, the information processing device 100 specifically sets the DCCM corresponding to k=4 as the DCCM used for potential setting.

[0128] (6-5) The information processing device 100 performs potential setting based on the set DCCM. The information processing device 100 performs coarse-grained molecular dynamics simulation based on the potential. The information processing device 100 outputs the results of the coarse-grained molecular dynamics simulation for the user to refer to. This enables the information processing device 100 to perform coarse-grained molecular dynamics simulation efficiently and accurately.

[0129] Specifically, the information processing device 100 uses the DCCM feature quantities as a basis and, from the perspective of potential setting, sets different time lengths kmax The validity of the DCCM corresponding to each individual second trajectory can be evaluated. Therefore, the information processing device 100 can appropriately set the potential according to the DCCM corresponding to a second trajectory of an appropriate time length, and can perform coarse-grained molecular dynamics simulations efficiently and accurately.

[0130] The information processing device 100 is k max Since the convergence conditions can be determined while sequentially increasing the time, the processing load and processing time required when determining an appropriate DCCM to be used for potential setting can be controlled. Specifically, the information processing device 100 can determine an appropriate DCCM to be used for potential setting that corresponds to the minimum time length within a range where the DCCM does not change easily even when the time length is increased. Therefore, the information processing device 100 can suppress the increase in processing load and processing time required when determining an appropriate DCCM to be used for potential setting, for example.

[0131] Here, the information processing device 100, based on N first trajectories, k max We have described the case in which k second trajectories are generated, but this is not the only case. For example, the information processing device 100 may perform a molecular dynamics simulation for one initial state, thereby generating k max It is also possible to generate a second trajectory. This allows the information processing device 100 to reduce the processing time and processing load required for potential setting.

[0132] (An example of the effects of the information processing device 100) Next, an example of the effects of the information processing device 100 will be explained using Figure 7.

[0133] Figure 7 is an explanatory diagram illustrating an example of the effects of the information processing device 100. In Figure 7, the information processing device 100 performs coarse-grained molecular dynamics simulations for various proteins with N=5 and L=10ns. Specifically, the information processing device 100 performs coarse-grained molecular dynamics simulations for ADK_apo type proteins, Integrin proteins, and Spike protein_up type proteins.

[0134] Here, the number of residues N for ADK_apo type proteins. residue It is 214. Also, the number of residues N in the Integrin protein. residue The number of residues N for spike protein-up type proteins. residue The answer is 3438.

[0135] Graph 700 shows the relationship between the value of k and the value of F(k) for various proteins. As shown in Graph 700, the information processing device 100 can determine an appropriate DCCM to use for setting the potential in coarse-grained molecular dynamics simulations for various proteins, depending on F(k). Specifically, for ADK_apo type proteins, since |F(4)-F(5)| is below the second threshold, the information processing device 100 can appropriately set the potential based on the DCCM corresponding to k=4.

[0136] Furthermore, specifically for Integrin proteins, the information processing device 100 can appropriately set the potential based on DCCM corresponding to k=4, since F(4) is below the first threshold. Furthermore, specifically for Spike protein-up type proteins, the information processing device 100 can appropriately set the potential based on DCCM corresponding to k=5, since |F(5)-F(6)| is below the second threshold.

[0137] In this case, the information processing device 100 is as shown in Figures 5 and 6, k maxSince the convergence conditions can be determined while sequentially increasing the time, the processing load and processing time required when determining an appropriate DCCM to be used for potential setting can be controlled. Specifically, the information processing device 100 can determine an appropriate DCCM to be used for potential setting that corresponds to the minimum time length within a range where the DCCM does not change easily even when the time length is increased. For example, the information processing device 100 can suppress the increase in processing load and processing time required when determining an appropriate DCCM to be used for potential setting.

[0138] (Overall processing procedure) Next, an example of the overall processing procedure executed by the information processing device 100 will be explained using Figure 8. The overall processing is realized, for example, by the CPU 301 shown in Figure 3, storage areas such as memory 302 and recording medium 305, and network I / F 303.

[0139] Figure 8 is a flowchart showing an example of the overall processing procedure. In Figure 8, the information processing device 100 is k max The value is set to 0 (step S801). Next, the information processing device 100 sets up N different initial structures (step S802). Then, the information processing device 100 proceeds to the process in step S803.

[0140] In step S803, the information processing device 100, k max Δk is added to (step S803). Next, the information processing device 100 calculates k based on the set N structures by molecular dynamics calculations. max Obtain N first trajectories with a time length of ×L minutes (step S804).

[0141] Then, the information processing device 100, k max For each of the following integers k, a second trajectory is generated by extracting and combining data representing the time length of k × L in each of the N first trajectories (step S805).

[0142] Next, the information processing device 100, k max A DCCM corresponding to each of the second trajectories is generated (step S806). Then, based on the generated DCCM, the information processing device 100 generates k max For each of the following integers k, calculate F(k) (step S807).

[0143] Next, the information processing device 100 determines whether or not a predetermined condition is met (step S808). If the predetermined condition is not met (step S808: No), the information processing device 100 proceeds to the process in step S809. On the other hand, if the predetermined condition is met (step S808: Yes), the information processing device 100 proceeds to the process in step S810.

[0144] In step S809, the information processing device 100 sets up N different latest structures (step S809). Then, the information processing device 100 returns to the process in step S803.

[0145] In step S810, the information processing device 100 determines the smallest k that satisfies a predetermined condition. x Identify the identified k x The DCCM corresponding to the second trajectory, which has a time length of ×L, is output (step S810). Then, the information processing device 100 terminates the overall processing. Here, the information processing device 100 may execute some of the steps in Figure 8 in a different order. Also, the information processing device 100 may omit some of the steps in Figure 8.

[0146] (Examples of applications of the information processing device 100) The information processing device 100 can be applied, for example, to the field of drug discovery. Specifically, the information processing device 100 can be applied when it is desirable to perform coarse-grained molecular dynamics simulations in order to investigate structural changes of proteins.

[0147] As explained above, the information processing device 100 can obtain a first trajectory having a time length that is a first multiple of a predetermined time length, obtained from molecular dynamics simulations of the target substance performed under each of several initial conditions. The information processing device 100 can generate a second trajectory by combining all or part of the data having a time length of that multiple in each of the obtained first trajectories for each of several multiples of the predetermined time length that is less than or equal to the first multiple. The information processing device 100 can obtain the feature quantities of the DCCM corresponding to each of the generated second trajectories. Based on the obtained feature quantities, the information processing device 100 can control the setting of the potential for the coarse-grained molecular dynamics simulation of the target substance according to the time length corresponding to any of the feature quantities that satisfy a predetermined condition. As a result, the information processing device 100 can appropriately set the potential for the molecular dynamics simulation, thereby improving the efficiency or accuracy of the molecular dynamics simulation potential.

[0148] According to the information processing device 100, if none of the features meet the predetermined conditions, a value greater than the current value of the first multiple can be newly set as the first multiple. According to the information processing device 100, a new first trajectory having a time length of the first multiple set this time can be newly acquired based on each of the first trajectories acquired in the previous session. As a result, the information processing device 100 can control the potential setting of the molecular dynamics simulation while sequentially increasing the first multiple, thereby suppressing the increase in processing load and processing time incurred during potential setting.

[0149] According to the information processing device 100, a predetermined condition can be set for a first feature, which is one of the feature quantities, that at least one of the first condition and the second condition be satisfied. According to the information processing device 100, the first condition can be set to be that the first feature quantity is less than or equal to a first threshold. According to the information processing device 100, the second condition can be set to be that the change between the first feature quantity and the second feature quantity, which corresponds to the second time length that is the next longest after the first time length corresponding to the first feature quantity, is less than or equal to a second threshold. As a result, the information processing device 100 can appropriately evaluate which DCCM corresponding to which time length of the second trajectory is preferable to use for potential setting.

[0150] According to the information processing device 100, the potential can be set to a value that defines the spring characteristics in the coarse-grained elastic network model used in coarse-grained molecular dynamics simulations. This allows the information processing device 100 to appropriately set the coarse-grained elastic network model and improve the efficiency of coarse-grained dynamics simulations.

[0151] According to the information processing device 100, the feature quantity can be the sum of coefficients in DCCM, the ratio of positive to negative coefficients in DCCM, or the sum of the convergence rates of each cell in DCCM. This allows the information processing device 100 to appropriately evaluate which DCCM corresponding to which time length of second trajectory is preferable to use for potential setting.

[0152] According to the information processing device 100, by extending each of the previously acquired first trajectories to a time length that is a multiple of the currently set number, a new first trajectory with a time length that is a multiple of the currently set number can be acquired. This allows the information processing device 100 to reduce the processing burden and processing time required when acquiring a new first trajectory.

[0153] The information processing method described in this embodiment can be implemented by executing a pre-prepared program on a computer such as a PC or workstation. The information processing program described in this embodiment is recorded on a computer-readable recording medium and executed by being read from the recording medium by the computer. The recording medium can be a hard disk, flexible disk, CD (Compact Disc)-ROM, MO (Magneto Optical Disc), DVD (Digital Versatile Disc), etc. Furthermore, the information processing program described in this embodiment may be distributed via a network such as the Internet.

[0154] With regard to the embodiments described above, the following additional information is disclosed.

[0155] (Note 1) For each of the multiple initial conditions, a first trajectory is obtained, which represents the behavior of molecules over time, obtained from molecular dynamics simulations of the target substance performed under the said initial conditions, and has a time length that is a first multiple of a predetermined time length. For each of the multiple multiples of the predetermined time length that include the first multiple and are less than or equal to the first multiple, a second trajectory is generated by combining all or part of the data in each of the acquired first trajectories that have a time length equal to the multiple. Based on the features of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing program characterized by having a computer perform the processing.

[0156] (Note 2) If none of the above features satisfy the predetermined conditions, a value greater than the current value of the first multiple is newly set as the first multiple. The computer is made to perform the process, The aforementioned acquisition process is, The information processing program according to Appendix 1, characterized in that it acquires a new first trajectory having a time length of the first multiple set this time, based on each of the first trajectories acquired in the previous session.

[0157] (Note 3) The information processing program according to Note 2, characterized in that the predetermined condition for a first feature which is any of the above features satisfies at least one of the following: a first condition indicating that the first feature is less than or equal to a first threshold, and a second condition indicating that the change between the second feature corresponding to the second time length which is the next longest time length after the first time length corresponding to the first feature and the first feature is less than or equal to a second threshold.

[0158] (Note 4) The information processing program according to any one of Notes 1 to 3, characterized in that the potential is a setting value that defines the characteristics of the spring in the coarse-grained elastic network model used in the coarse-grained molecular dynamics simulation.

[0159] (Note 5) The information processing program according to any one of Notes 1 to 3, characterized in that the feature quantity is the sum of the coefficients in the dynamic cross-correlation coefficient map, the ratio of positive coefficients to negative coefficients in the dynamic cross-correlation coefficient map, or the sum of the convergence rates of each cell in the dynamic cross-correlation coefficient map.

[0160] (Note 6) The process to be obtained is as follows: The information processing program according to Appendix 2, characterized in that, based on each of the previously acquired first trajectories, each of the previously acquired first trajectories is extended to the time length of the first multiple set this time, thereby acquiring a new first trajectory having the time length of the first multiple set this time.

[0161] (Note 7) For each of the multiple initial conditions, a first trajectory is obtained, which represents the behavior of molecules over time, obtained from molecular dynamics simulations of the target substance performed under the aforementioned initial conditions, and has a time length that is a first multiple of a predetermined time length. For each of the multiple multiples of the predetermined time length that include the first multiple and are less than or equal to the first multiple, a second trajectory is generated by combining all or part of the data in each of the acquired first trajectories that have a time length equal to the multiple. Based on the features of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing method characterized in that the processing is performed by a computer.

[0162] (Note 8) For each of the multiple initial conditions, a first trajectory is obtained, which represents the behavior of molecules over time, obtained from molecular dynamics simulations of the target substance performed under the aforementioned initial conditions, and has a time length that is a first multiple of a predetermined time length. For each of the multiple multiples of the predetermined time length that include the first multiple and are less than or equal to the first multiple, a second trajectory is generated by combining all or part of the data in each of the acquired first trajectories that have a time length equal to the multiple. Based on the features of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing device characterized by having a control unit.

[0163] (Note 9) Obtain multiple trajectories with different time lengths within a range below the upper limit, representing the behavior of molecules over time, obtained from molecular dynamics simulations of the substance in question. Based on the features of the dynamic cross-correlation coefficient maps corresponding to each of the acquired multiple trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing program characterized by having a computer perform the processing.

[0164] (Note 10) If none of the above features satisfy the above predetermined conditions, the above upper limit time length is set to a value greater than the current value of the above upper limit time length. The computer is made to perform the process, The aforementioned acquisition process is, The information processing program according to Appendix 9, characterized by acquiring multiple trajectories, each having a different time length, within a range less than or equal to the upper time length set in this instance, obtained by the molecular dynamics simulation. [Explanation of symbols]

[0165] 100 Information Processing Devices 110-113, 511, 521, 531, 611, 621, 631 First Trajectory 120-123, 501-503, 601-604 Second Trajectory 130-133 DCCM 200 Information Processing Systems 201 Computing equipment 202 Client Devices 210 Network 300 bus 301 CPU 302 memory 303 Network I / F 304 Recording medium interface 305 Recording media 306 displays 307 Input device 400 Storage section 401 Acquisition Department 402 First Setting Section 403 1st generation part 404 Second generation part 405 Third generation part 406 Second Setting Section 407 Implementation Department 408 Output section 500, 600, 700 graph 510, 520, 530, 610, 620, 630 Structure

Claims

1. For each of the multiple initial conditions, a first trajectory is obtained, which represents the behavior of molecules over time, obtained from molecular dynamics simulations of the target substance performed under the said initial conditions, and has a time length that is a first multiple of a predetermined time length. For each of the multiple multiples of the predetermined time length that include the first multiple and are less than or equal to the first multiple, a second trajectory is generated by combining all or part of the data in each of the acquired first trajectories that have a time length equal to the multiple. Based on the features of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing program characterized by having a computer perform the processing.

2. If none of the above features satisfy the predetermined conditions, a value greater than the current value of the first multiple is newly set as the first multiple. The computer is made to perform the process, The aforementioned acquisition process is, The information processing program according to claim 1, characterized in that it acquires a new first trajectory having a time length of the first multiple set this time, based on each of the first trajectories acquired in the previous session.

3. The information processing program according to claim 2, characterized in that the predetermined condition for a first feature which is any of the aforementioned features satisfies at least one of the following: a first condition indicating that the first feature is less than or equal to a first threshold, and a second condition indicating that the change between the first feature, which corresponds to the second time length that is the next longest after the first time length corresponding to the first feature, and the first feature is less than or equal to a second threshold.

4. The information processing program according to any one of claims 1 to 3, characterized in that the potential is a setting value that defines the characteristics of the spring in the coarse-grained elastic network model used in the coarse-grained molecular dynamics simulation.

5. The information processing program according to any one of claims 1 to 3, characterized in that the feature quantity is the sum of the coefficients in the dynamic cross-correlation coefficient map, the ratio of positive coefficients to negative coefficients in the dynamic cross-correlation coefficient map, or the sum of the convergence rates of each cell in the dynamic cross-correlation coefficient map.

6. The aforementioned acquisition process is, The information processing program according to claim 2, characterized in that, based on each of the previously acquired first trajectories, each of the previously acquired first trajectories is extended to a time length of the first multiple set this time, thereby acquiring a new first trajectory having a time length of the first multiple set this time.

7. For each of the multiple initial conditions, a first trajectory is obtained, which represents the behavior of molecules over time, obtained from molecular dynamics simulations of the target substance performed under the said initial conditions, and has a time length that is a first multiple of a predetermined time length. For each of the multiple multiples of the predetermined time length that include the first multiple and are less than or equal to the first multiple, a second trajectory is generated by combining all or part of the data in each of the acquired first trajectories that have a time length equal to the multiple. Based on the features of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing method characterized in that the processing is performed by a computer.

8. For each of the multiple initial conditions, a first trajectory is obtained, which represents the behavior of molecules over time, obtained from molecular dynamics simulations of the target substance performed under the said initial conditions, and has a time length that is a first multiple of a predetermined time length. For each of the multiple multiples of the predetermined time length that include the first multiple and are less than or equal to the first multiple, a second trajectory is generated by combining all or part of the data in each of the acquired first trajectories that have a time length equal to the multiple. Based on the features of the dynamic cross-correlation coefficient map corresponding to each of the generated second trajectories, the system controls the setting of the potential for the coarse-grained molecular dynamics simulation for the target substance according to the time length corresponding to any of the features that satisfy predetermined conditions. An information processing device characterized by having a control unit.

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