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

Abstract

Predict the pharmacokinetics of peptides. In response to a request signal transmitted from a terminal 12, a server 14 generates prediction information regarding the pharmacokinetics of a peptide. Then, the server 14 transmits the prediction information to the terminal 12.

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, an information processing method, an information processing program, and an information processing system. [Background technology]

[0002] Patent Document 1 discloses that in the structural analysis of biopolymers, multiple structures are clustered in a multidimensional space in which each of the index dimensions included in a dimension set has its coordinate axis, and a molecular dynamics simulation is performed using an outlier structure that is not included in any cluster as the initial structure (e.g., claim 4).

[0003] Patent Document 2 discloses a protein 3D structure prediction program that predicts the 3D structure of a protein. A computer that executes this protein 3D structure prediction program reads the amino acid sequence of a protein and predicts secondary structure information. Next, the computer calculates the number of amino acids that form turns based on the secondary structure information, and obtains turn structure information of turns with a high probability of existence based on the calculated number of amino acids and the secondary structure information, predicts and reproduces the turns, and predicts the 3D structure of the protein.

[0004] Patent document 3 also discloses a method for generating, for each patient, a set of numerical presentation likelihoods for a set of neoantigens for the patient by inputting the peptide sequences of each of the set of neoantigens into a machine-learned presentation model (e.g., claim 1).

[0005] Patent document 4 also discloses a method of using a computer processor to input a numerical vector of peptides into a deep learning display model to generate a set of display likelihoods for a set of neoantigens (e.g., claim 1). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2017-37378 A [Patent Document 2] International Publication No. 2003 / 054743 [Patent Document 3] Special Publication No. 2020-523010 [Patent Document 4] Special Publication No. 2020-519246 Summary of the Invention [Problem to be solved by the invention]

[0007] In recent years, peptide drugs have been attracting attention as a type of medium-sized molecule drug, but there are many unknowns regarding the pharmacokinetics of peptides.

[0008] The techniques disclosed in the above Patent Documents 1 to 4 are techniques for performing molecular dynamics simulations of biopolymers, techniques for predicting the three-dimensional structures of proteins by computer, and techniques for predicting peptides effective against neoantigens, but are not techniques for predicting the pharmacokinetics of peptides. Therefore, the techniques disclosed in the above Patent Documents 1 to 4 have the problem that they cannot predict the pharmacokinetics of peptides.

[0009] In consideration of the above, the present disclosure aims to predict the pharmacokinetics of a peptide. [Means for solving the problem]

[0010] A first aspect of the present disclosure is an information processing device including a receiving unit that receives a request signal transmitted from a terminal, a prediction unit that generates predicted information regarding the pharmacokinetics of a peptide in response to the request signal, and a transmitting unit that transmits the predicted information generated by the prediction unit to the terminal.

[0011] A second aspect of the present disclosure is an information processing method in which a computer executes a process of receiving a request signal transmitted from a terminal, generating predictive information regarding the pharmacokinetics of a peptide in response to the request signal, and transmitting the generated predictive information to the terminal.

[0012] A third aspect of the present disclosure is an information processing program for causing a computer to execute a process of receiving a request signal transmitted from a terminal, generating predictive information regarding the pharmacokinetics of a peptide in response to the request signal, and transmitting the generated predictive information to the terminal.

[0013] A fourth aspect of the present disclosure is an information processing system including a terminal and an information processing device, wherein the terminal transmits a request signal to the information processing device, the information processing device receives the request signal transmitted from the terminal, and in response to the request signal, generates prediction information regarding the pharmacokinetics of a peptide and transmits the generated prediction information to the terminal, and the terminal displays the prediction information transmitted from the information processing device on a display unit. Effect of the Invention

[0014] According to the present disclosure, an effect is obtained in that the pharmacokinetics of a peptide can be predicted. [Brief description of the drawings]

[0015] [Figure 1] 1 is a block diagram showing an information processing system according to a first embodiment. [Diagram 2] FIG. 2 is a diagram for explaining an example of data stored in a common database 142. [Diagram 3] FIG. 2 is a diagram for explaining an example of data stored in a user database 144. [Figure 4] FIG. 2 is a diagram for explaining a prediction model of the present embodiment. [Diagram 5] FIG. 2 is a diagram illustrating a computer that realizes each device in the information processing system. [Figure 6]This is a sequence executed in the information processing system. [Figure 7] FIG. 13 is a diagram showing an example of a screen displayed on a display unit of the terminal. [Figure 8] This is a sequence executed in the information processing system. [Figure 9] FIG. 13 is a diagram illustrating an example of design support information. [Figure 10] FIG. 11 is a block diagram showing an information processing system according to a second embodiment. [Figure 11] FIG. 11 is a diagram for explaining a score in the second embodiment. [Figure 12] FIG. 1 is a diagram for explaining candidate peptides. [Figure 13] FIG. 13 is a diagram illustrating an example of design support information. [Figure 14] FIG. 13 is a block diagram showing an information processing system according to a fourth embodiment. [Figure 15] FIG. 13 is a diagram illustrating an example of a table stored in a remuneration storage unit. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

[0017] [First embodiment]

[0018] <Configuration of information processing system>

[0019] FIG. 1 is a block diagram showing an example of the configuration of an information processing system 10 according to the first embodiment. As shown in FIG. 1, the information processing system 10 functionally includes a plurality of terminals 12A, 12B, and 12C, and a server 14, which is an example of an information processing device. The plurality of terminals 12A, 12B, and 12C and the server 14 are connected via a network 16, such as the Internet. In the following, when referring to one terminal, it is simply referred to as a terminal 12. In FIG. 1, a plurality of terminals 12A, 12B, and 12C are illustrated, but the information processing system 10 may include more or fewer terminals.

[0020] (Terminal)

[0021] Each of the multiple terminals 12A, 12B, 12C is operated by multiple different users. The user inputs peptide information representing a peptide that the user wishes to analyze into the terminal 12 that the user operates. The user then operates the terminal 12 to transmit the peptide information to a server 14, which will be described later. The terminal 12 transmits the peptide information input by the user to the server 14 via a network 16. The peptide information includes at least one of the following information: a structural formula of the peptide, a SMILES representation of the peptide, a primary structure of the peptide, a secondary structure of the peptide, a tertiary structure of the peptide, and a quaternary structure of the peptide.

[0022] (server)

[0023] As shown in Fig. 1, the server 14 includes a receiving unit 140, a common database 142, a plurality of user databases 144A, 144B, and 144C, a common prediction model storage unit 145, a plurality of user prediction model storage units 146A, 146B, and 146C, a prediction unit 148, a processing unit 150, and a transmission unit 152. In the following, when one user database is indicated, it is simply referred to as a user database 144. In addition, when one user prediction model storage unit is indicated, it is simply referred to as a user prediction model storage unit 146. In addition, although a plurality of user databases 144A, 144B, and 144C are illustrated in Fig. 1, the server 14 may include more or fewer user databases. Similarly, although a plurality of user prediction model storage units 146A, 146B, and 146C are illustrated in FIG. 1, the server 14 may include more or fewer user prediction model storage units.

[0024] The receiving unit 140 receives a request signal transmitted from the terminal 12, the request signal including a user ID for identifying the terminal, peptide information, and selection information for a prediction model, which will be described later.

[0025] The common database 142 stores data of an administrator who manages the server 14 and data of a user who operates the terminal 12. Fig. 2 shows an example of data stored in the common database 142. As shown in Fig. 2, a data ID is assigned to each piece of data. This data ID is identification information for identifying the data. The data ID is also information for identifying whether the data originates from the administrator or the user.

[0026] As shown in FIG. 2, data corresponding to data IDs "00001" to "00004" are data derived from the administrator, and peptide information, characteristic information of the peptide information, an experimental value of the membrane permeability of the peptide, an experimental method for obtaining the experimental value of the membrane permeability of the peptide, an experimental value of the in vivo persistence of the peptide, an experimental method for obtaining the experimental value of the in vivo persistence of the peptide, annotations, and prediction information generated by the prediction unit 148 described later are stored in association with each other. The value of the in vivo persistence of the peptide is, for example, the plasma protein binding rate of the peptide. The annotations are information indicating how the data was obtained. The annotation column may store a score indicating the reliability of the data. Data corresponding to data IDs "00001" to "00004" are stored in advance in the common database 142 by the administrator of the server 14.

[0027] On the other hand, as shown in Fig. 2, data corresponding to data IDs "A_00001", "A_00002", "B_00001", etc. are data derived from users, and are not associated with peptide information. Instead, the peptide information characteristic information, the peptide membrane permeability experimental value, the experimental method used to obtain the peptide membrane permeability experimental value, the peptide in vivo persistence experimental value, the experimental method used to obtain the peptide in vivo persistence experimental value, annotations, and prediction information generated by the prediction unit 148 described later are stored in association with each other. Data corresponding to data IDs "A_00001", "A_00002", "B_00001", etc. are stored in the common database 142 after approval by the user.

[0028] Each of the multiple user databases 144A, 144B, and 144C stores data derived from a user. FIG. 3 shows an example of data stored in the user database 144. As shown in FIG. 3, the user database 144 stores peptide information, peptide characteristic information, an experimental value of the membrane permeability of the peptide, an experimental method when the experimental value of the membrane permeability of the peptide was obtained, an experimental value of the in vivo persistence of the peptide, an experimental method when the experimental value of the in vivo persistence of the peptide was obtained, annotations, and prediction information generated by a prediction unit 148 described later, in association with each other. Note that each data stored in the user database 144 may be partially or entirely missing.

[0029] As described above, the common database 142 does not store peptide information among the data derived from users, but the user database 144 stores peptide information. Peptide information is highly confidential. For this reason, users are considered to be resistant to their own peptide information being stored in the common database 142, which also stores data of other users and administrators.

[0030] Therefore, in this embodiment, a user database 144 different from the common database 142 is provided, and the peptide information of the user is stored only in the user database 144. On the other hand, the common database 142 does not store the peptide information of the user.

[0031] The feature information obtained from the peptide information is in the form of a vector, which is composed of a large number of values ​​calculated by reversible or irreversible arithmetic processing, focusing on various local or overall structures of the peptide, and is less confidential than the peptide information. For this reason, it is considered that some users may consider storing the feature information in the common database 142.

[0032] Therefore, in this embodiment, the characteristic information is stored in the common database 142. The information stored in this common database 142 is used for generating a prediction model for predicting the membrane permeability or in vivo persistence of a peptide. The prediction model will be described later. The information stored in the common database 142 is treated as information available to an administrator who manages the server 14. The information stored in the common database 142 is also treated as information available to all users, not just the administrator. A part of the information stored in the common database 142 may be set as information that cannot be used by users.

[0033] The common prediction model storage unit 145 stores prediction models.

[0034] Each of the plurality of user prediction model storage units 146A, 146B, and 146C stores a user prediction model.

[0035] FIG. 4 shows an example of the prediction model of this embodiment. FIG. 4 shows a trained model M1 for predicting membrane permeability, a trained model M2 for predicting in vivo persistence, and a simulation model M3 for performing molecular dynamics simulation on the in vivo kinetics of a peptide, which are examples of prediction models. As shown in FIG. 4, when feature information obtained from peptide information is input to the trained model M1 for predicting membrane permeability, a predicted value of membrane permeability corresponding to the peptide is output from the model. When feature information obtained from peptide information is input to the trained model M2 for predicting in vivo persistence, a predicted value of in vivo persistence corresponding to the peptide is output from the model. When peptide information is input to the simulation model M3, trajectory data of the molecular dynamics simulation on the in vivo kinetics of the peptide is output from the simulation model M3. Note that the data output from the simulation model M3 is trajectory data, but by performing statistical analysis or the like on the trajectory data, a predicted value of the membrane permeability or in vivo persistence of the peptide is calculated, and these values ​​may be output from the simulation model M3.

[0036] These trained models are generated using all or part of the data stored in the common database 142 or the user database 144 as training data. Specifically, experimental values ​​associated with feature information are used as teacher data, and trained models are generated by supervised machine learning. These trained models are realized, for example, by neural networks (including, for example, deep neural networks trained by deep learning), support vector machines, etc. The trained models are not limited to these machine learning models, and may be realized by a method such as logistic regression.

[0037] The common prediction model storage unit 145 stores, as prediction models, a trained model generated using data stored in the common database 142 as learning data and a simulation model. Meanwhile, each of the multiple user prediction model storage units 146A, 146B, and 146C stores, as prediction models, a trained model generated using data stored in the user database 144 as learning data and a simulation model prepared for each user.

[0038] When predicting the behavior of a peptide, it is assumed that a certain user A wants to analyze the behavior of a certain type of peptide, and another user B wants to analyze the behavior of a different type of peptide. In addition, since the data stored in the user database 144 differs for each user, it is considered that the optimal method of constructing a prediction model will differ. Therefore, in this embodiment, a prediction model for predicting the pharmacokinetics of a peptide is prepared for each user.

[0039] Specifically, a prediction model for user A is stored in user prediction model storage unit 146A. A prediction model for user B is stored in user prediction model storage unit 146B. A prediction model for user C is stored in user prediction model storage unit 146C. In this way, a prediction model is prepared according to a peptide that the user wishes to analyze, and the user can obtain prediction information for the peptide by using the prediction model.

[0040] On the other hand, it is possible that there may be users who wish to predict the pharmacokinetics of a peptide using a general prediction model, rather than a unique prediction model generated from their own data. For this reason, in this embodiment, the common prediction model storage unit 145 stores a trained model generated using the data stored in the common database 142 as training data, and a standard simulation model.

[0041] Therefore, when the prediction model selection information included in the request signal transmitted from the terminal 12 represents a prediction model stored in the user prediction model storage unit 146, the prediction unit 148 reads out a prediction model from the user prediction model storage unit 146 corresponding to the user ID based on the user ID received by the receiving unit 140.

[0042] On the other hand, when the prediction model selection information included in the request signal transmitted from the terminal 12 represents a prediction model stored in the common prediction model storage unit 145, the prediction unit 148 reads out the prediction model from the common prediction model storage unit 145.

[0043] Then, the prediction unit 148 converts the peptide information received by the receiving unit 140 into feature information by a known method. Next, the prediction unit 148 inputs at least one of the peptide information and the feature information into the read prediction model, thereby generating prediction information corresponding to the peptide information.

[0044] For example, when the prediction model is a trained model M1 for predicting membrane permeability, a predicted value of membrane permeability is generated as prediction information. When the prediction model is a trained model M2 for predicting in vivo persistence, a predicted value of in vivo persistence is generated as prediction information. When the prediction model is a simulation model M3, the prediction unit 148 inputs the peptide information received by the receiving unit 140 into the simulation model M3, thereby simulating the behavior of the peptide in the body by a known molecular dynamics simulation method. As a result, prediction information regarding the pharmacokinetics of the peptide is generated.

[0045] In addition, the common prediction model storage unit 145 and the user prediction model storage unit 146 may store a plurality of prediction models that generate the same type of prediction information. For example, the user prediction model storage unit 146 may store a plurality of trained models for predicting membrane permeability. In addition, the user prediction model storage unit 146 may store a plurality of simulation models.

[0046] For example, it is assumed that the user wants to generate prediction information for a certain peptide information by using his / her own trained model X for predicting membrane permeability and trained model Y for predicting membrane permeability stored in the user prediction model storage unit 146, and trained model Z for predicting membrane permeability and trained model W for predicting membrane permeability stored in the common prediction model storage unit 145. The same is assumed for the trained model for predicting in vivo persistence and the simulation model.

[0047] In such a case, the user can generate multiple pieces of prediction information for one piece of peptide information using the above-mentioned multiple prediction models of the same type. In this case, for example, the user can check each piece of prediction information generated by the multiple prediction models of the same type and consider which prediction information is valid. Alternatively, the user can obtain valid prediction information by averaging each piece of prediction information generated by the multiple prediction models of the same type. In this case, the process of generating feature information from the peptide information to be predicted only once is sufficient, and multiple pieces of prediction information are generated by inputting the generated feature information into multiple prediction models.

[0048] In addition, the common prediction model storage unit 145 also stores various parameters when generating prediction information. These various parameters are used when generating prediction information using a prediction model. In addition, each of the multiple user prediction model storage units 146A, 146B, and 146C also stores various parameters when generating prediction information, and these various parameters are different for each user. Since the various parameters when generating prediction information are different for each user, it is possible to generate appropriate prediction information according to the user's request. In addition, the common prediction model storage unit 145 or the user prediction model storage unit 146 may store multiple parameters of the same type. When obtaining prediction information of a peptide, the user appropriately selects a parameter that is considered appropriate from these various parameters and causes the server 14 to generate prediction information of the peptide.

[0049] The processing unit 150 associates the peptide information received by the receiving unit 140 with the characteristic information and prediction information obtained by the prediction unit 148, and stores them in the user database 144 corresponding to the user ID.

[0050] The transmitting unit 152 transmits the prediction information generated by the predicting unit 148 to the terminal 12 corresponding to the user ID received by the receiving unit 140.

[0051] The terminal 12 receives the prediction information transmitted from the server 14, and displays the prediction information on a display unit (not shown).

[0052] The terminal 12 and the server 14 can be realized by, for example, a computer 50 as shown in Fig. 5. The computer 50 realizing the terminal 12 and the server 14 includes a CPU 51, a memory 52 as a temporary storage area, and a non-volatile storage unit 53. The computer also includes an input / output interface (I / F) 54 to which an input / output device or the like (not shown) is connected, and a read / write (R / W) unit 55 that controls reading and writing of data from and to a recording medium 59. The computer also includes a network I / F 56 that is connected to a network such as the Internet. The CPU 51, the memory 52, the storage unit 53, the input / output I / F 54, the R / W unit 55, and the network I / F 56 are connected to one another via a bus 57.

[0053] The storage unit 53 can be realized by a hard disk drive (HDD), a solid state drive (SSD), a flash memory, etc. The storage unit 53 as a storage medium stores a program for making the computer function. The CPU 51 reads the program from the storage unit 53, expands it in the memory 52, and sequentially executes the processes contained in the program.

[0054] Next, the operation of the information processing system 10 according to the embodiment will be described.

[0055] When a user who operates the terminal 12 inputs peptide information to be predicted into the terminal 12 and executes an operation to transmit the peptide information to the server 14, a sequence as shown in FIG. 6 is executed. Specifically, when a user who operates the terminal 12 inputs peptide information to be predicted into the terminal 12, transmits the peptide information to the server 14, and transmits an instruction signal to the server 14 to predict the behavior of the peptide by a trained model, a sequence as shown in FIG. 6 is executed. Note that, in the sequence of FIG. 6, a case will be described as an example in which one prediction model is selected and prediction information is generated by the prediction model. Note that, as described above, prediction information may be generated by each of a plurality of prediction models. In this case, information specifying a plurality of prediction models is included in the selection information of the prediction model described later.

[0056] In step S100, the terminal 12 transmits a request signal including the peptide information input by the user and its own user ID to the server 14. The request signal also includes prediction model selection information indicating whether prediction information is to be generated using the user's own prediction model stored in the user prediction model storage unit 146 or the prediction model stored in the common prediction model storage unit 145.

[0057] In step S102, the receiving unit 140 of the server 14 receives the request signal transmitted from the terminal 12 in step S100.

[0058] In step S104, the prediction unit 148 of the server 14 identifies the user of the terminal 12 from the user ID included in the request signal received in step S102 above.

[0059] In step S106, the prediction unit 148 of the server 14 determines which prediction model is to be used to generate the prediction information, based on the selection information of the prediction model included in the request signal received in step S102. If the selection information indicates that the user's own prediction model is to be used, the prediction unit 148 of the server 14 reads out the prediction model from the user prediction model storage unit 146 corresponding to the user ID. On the other hand, if the selection information indicates that the prediction model in the common prediction model storage unit 145 is to be used, the prediction unit 148 of the server 14 reads out the prediction model from the common prediction model storage unit 145.

[0060] In step S108, the prediction unit 148 of the server 14 converts the peptide information included in the request signal received in step S102 above into feature information.

[0061] In step S110, the prediction unit 148 of the server 14 generates prediction information for the peptide by inputting the feature information obtained in step S108 above into the prediction model read out in step S106 above.

[0062] In step S112, the transmission unit 152 of the server 14 transmits the prediction information obtained in the above step S110 to the terminal 12. Note that the transmission unit 152 may transmit the prediction information obtained in the above step S110 and peptide information (e.g., a structural formula of a peptide) to be predicted to the terminal 12 in association with each other.

[0063] In step S114, the terminal 12 receives the prediction information transmitted in step S112.

[0064] In step S116, the terminal 12 displays the prediction information received in step S114 on a display unit (not shown).

[0065] In this case, as shown in Fig. 7, information showing the structural formula of the peptide, the SMILES notation of the peptide, and the prediction information are displayed on the display unit (not shown) of the terminal 12. Fig. 7 shows an example of information in which the prediction information of the peptide is associated with the structural formula of the peptide to be predicted.

[0066] Note that there may be more than one peptide to be predicted. In this case, for each of the multiple peptides, peptide information (for example, information representing the structural formula of the peptide), the SMILES notation of the peptide, and prediction information are displayed on the display unit (not shown) of the terminal 12. Note that in this case, the processes of steps S106 to S110 are repeated for each of the multiple peptide information, and prediction information for each of the multiple peptide information is displayed on the display unit (not shown) of the terminal 12. Also, when multiple prediction models are read out in step S106, prediction information is generated for one peptide information by the multiple prediction models.

[0067] This allows the user to obtain prediction information regarding the pharmacokinetics of a peptide simply by operating their own terminal 12.

[0068] In step S118, the processing unit 150 of the server 14 associates the peptide information included in the request signal received in step S102, the feature information obtained in step S108, and the prediction information generated in step S110, and stores them in the user database 144 corresponding to the user ID.

[0069] In step S120, the processing unit 150 of the server 14 stores in the common database 142 the feature information obtained in step S108 and the prediction information generated in step S110.

[0070] In addition, when a user operating the terminal 12 inputs peptide information to be predicted into the terminal 12, transmits the peptide information to the server 14, and transmits an instruction signal to the server 14 to predict the behavior of the peptide by molecular dynamics simulation, a sequence such as that shown in Figure 8 is executed.

[0071] Steps S100 to S106 shown in FIG. 8 are executed in the same manner as in FIG.

[0072] In step S210, the prediction unit 148 of the server 14 inputs the peptide information received in step S102 into a simulation model as a prediction model, and simulates the behavior of the peptide in the body by a known molecular dynamics simulation method, thereby generating prediction information regarding the pharmacokinetics of the peptide.

[0073] In step S212, the prediction unit 148 of the server 14 transmits to the terminal 12 the prediction information generated in step S210 above.

[0074] In step S214, the terminal 12 receives the prediction information transmitted from the server 14 in step S212 above.

[0075] In step S216, the terminal 12 displays the prediction information received in step S214 on a display unit (not shown).

[0076] In step S218, the processing unit 150 of the server 14 associates the peptide information included in the request signal received in step S102 with the prediction information generated in step S210, and stores them in the user database 144 corresponding to the user ID.

[0077] In this case, the predicted information of the peptide is, for example, at least one piece of information such as trajectory data simulating the motion of the peptide molecule in a time series and a predicted value of membrane permeability or in vivo persistence obtained by performing a statistical analysis on the trajectory data, etc. Based on this trajectory data, behavior related to the membrane permeability or in vivo persistence of the peptide may be visualized by a moving image.

[0078] As described above in detail, the server of the information processing system generates prediction information regarding the pharmacokinetics of a peptide in response to a request signal transmitted from a terminal. The server then transmits the prediction information to the terminal. This makes it possible to predict the pharmacokinetics of a peptide.

[0079] The present disclosure is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present disclosure.

[0080] For example, the server 14 in the above embodiment has been described with reference to an example in which membrane permeability and persistence in the body are predicted as predicted information on the pharmacokinetics of a peptide, but the present invention is not limited thereto. Any pharmacokinetics of a peptide may be predicted. For example, physical property parameters such as blood-brain barrier permeability or solubility of a peptide in the body environment may be predicted as the pharmacokinetics of a peptide.

[0081] In addition, the server 14 in the above embodiment has been described as generating only predicted information of the peptide, but is not limited thereto. For example, the server 14 may further include a design support unit that generates design support information representing candidates for change portions among the elements that constitute the peptide, for the purpose of improving the pharmacokinetics of the peptide. For example, the elements that constitute the peptide include peptide residues. In this case, the server 14 generates design support information S including candidates R for change portions, for example. Then, the display unit of the terminal 12 displays the design support information S as shown in FIG. 9. This supports the user in designing the peptide. The server 14 may generate only the design support information of the peptide instead of the predicted information of the peptide, and transmit the design support information to the terminal 12.

[0082] In addition, in the above embodiment, an example was described in which characteristic information and prediction information are stored in the common database 142, and peptide information, characteristic information, and prediction information are stored in the user database 144, but various modifications to these are envisioned.

[0083] For example, in the above embodiment, the characteristic information and prediction information of peptide information sent by a user to the server 14 as a prediction target are all stored in the common database 142, but this is not limited to the above.

[0084] For example, the server 14 may store in the common database 142 only the characteristic information and prediction information of the peptide information for which the user's consent has been obtained, among the characteristic information and prediction information of the peptide information. In this case, when the server 14 stores the characteristic information and prediction information in the common database 142 in step S120 of FIG. 6, for example, the server 14 outputs a confirmation signal to the terminal 12 indicating whether or not the characteristic information may be stored in the common database 142. When the terminal 12 receives the confirmation signal transmitted from the server 14, the terminal 12 displays on its own display unit (not shown) a screen for asking whether or not the characteristic information of the peptide information to be predicted may be stored in the common database 142. Note that, at this time, for example, if the user consents to storing the characteristic information in the common database 142, the characteristic information or prediction information is also used when generating prediction information of peptides of other users, and a display indicating that points or the like will be given to the user as a reward for providing the characteristic information or prediction information is displayed on the display unit (not shown) of the terminal 12. Then, for example, when the user inputs an operation to the terminal 12 to the effect that the feature information or the prediction information may be stored in the common database 142, the terminal 12 transmits an instruction signal to that effect to the server 14. On the other hand, when the user inputs an operation to the terminal 12 to the effect that he / she does not agree to the feature information or the feature information being stored in the common database 142, the terminal 12 transmits an instruction signal to that effect to the server 14.

[0085] The server 14 judges whether to store the characteristic information or the prediction information in the common database 142 in response to the instruction signal transmitted from the terminal 12. Then, when the instruction signal transmitted from the terminal 12 indicates consent to the storage of the characteristic information or the prediction information in the common database 142, the server 14 stores the characteristic information or the prediction information in the common database 142. On the other hand, when the instruction signal transmitted from the terminal 12 indicates not consent to the storage of the characteristic information or the prediction information in the common database 142, the server 14 ends the process without storing the characteristic information or the prediction information in the common database 142. In this way, by registering data according to the user's intention, the user can easily use the server 14. As described above, when the user provides the characteristic information or the prediction information and the characteristic information or the prediction information is stored in the common database 142, points are given to the user. The points given to the user are used, for example, for discounts on the usage fee when using the server 14.

[0086] In addition, after obtaining prediction information of a peptide, a user may conduct an experiment to verify how the peptide actually behaves in the body. The user may confirm the experimental value and store it in the user database 144 and the common database 142 of the server 14. In this case, similar to the case of the characteristic information and the prediction information, the user agrees to store the experimental value and the information of the experimental method in the common database 142, and when various information obtained by the user's experiment is stored in the common database 142, the user is given points or the like as a reward for providing various information obtained by the experiment. As a result, the experimental value obtained by the user's experiment becomes information that can be used by other users or the administrator of the server 14, and can be used as teacher data when the user or the administrator trains a prediction model by machine learning. For example, c2_A, d2_A, e2_A, and f2_A shown in FIG. 2 are examples of experimental information provided by the user, and when the experimental information is stored in the common database 142 in this way, points are given to the user.

[0087] When storing data in various storage units of server 14, the user can choose to store no data at all in server 14, store data only in user database 144, or store data also in common database 142.

[0088] Furthermore, when points are awarded to a user, the administrator of the server 14 may award points to the user based on the reliability stored in the user's behavior history or the annotations in Fig. 2. The reliability stored in the annotations may be either the reliability of the data itself provided by the user or the reliability of the user. In this case, for example, a large number of points are awarded to a user who provides high-quality data or a user who is highly reliable.

[0089] In the above embodiment, the trained model stored in each of the multiple user prediction model storage units 146A, 146B, and 146C is a trained model generated using only the data stored in the user database 144 as training data, but the present invention is not limited to this. For example, the trained model stored in each of the multiple user prediction model storage units 146A, 146B, and 146C may be generated using the data stored in the common database 142 as training data in addition to the data stored in the user database 144. Specifically, a trained model may be generated using part or all of the data stored in the common database 142 as training data in addition to the data stored in the user database 144. This generates a trained model with high prediction accuracy. In this case, various data as training data is registered in advance in the user database 144 or the common database 142.

[0090] Furthermore, the user may not only provide his / her own data by storing the above-mentioned various data in the common database 142 of the server 14, but also provide a prediction model that he / she has created to the server 14. In this case, similar to the above, points are awarded to the user when the prediction model is provided.

[0091] Furthermore, the data stored in the common database 142 or the user database 144 may be downloadable to the terminal 12. Furthermore, the data stored in the common database 142 or the user database 144 may be editable by the terminal 12. In this case, the data editable from the terminal 12 may be restricted. For example, the data stored in the common database 142 may be configured not to be editable from the terminal 12. Furthermore, the terminal 12 or the server 14 may generate various information regarding the pharmacokinetics of the peptide using the data stored in the common database 142 or the user database 144. For example, the terminal 12 or the server 14 may generate various information regarding the pharmacokinetics of the peptide by generating a graph with the feature amount of the peptide (e.g., lipophilicity) on the horizontal axis and the membrane permeability on the vertical axis.

[0092] In the above embodiment, the prediction unit 148 of the server 14 converts peptide information contained in a request signal transmitted from the terminal 12 into feature information, but the present invention is not limited to this. For example, the request signal transmitted from the terminal 12 may include feature information.

[0093] It is assumed that some users who use the service provided by the server 14 are hesitant to transmit peptide information such as the structural formula of a peptide to the server 14. For this reason, for example, the conversion from peptide information to feature information may be performed in advance by a computer other than the terminal 12 operated by the user or the server 14, and the feature information may be included in the request signal. In this case, the terminal 12 transmits a request signal including the feature information converted from the peptide information to the server 14. The receiving unit 140 of the server 14 receives the request signal transmitted from the terminal 12. The prediction unit 148 of the server 14 generates prediction information of the peptide by inputting the feature information contained in the request signal to the prediction model. Then, the transmitting unit 152 of the server 14 transmits the obtained prediction information to the terminal 12. This allows the user to obtain prediction information of the peptide without transmitting peptide information such as the structural formula of the peptide to the server 14. However, in this case, it is not possible to select a simulation model as the prediction model. This is because peptide information such as the structural formula of the peptide is required for prediction by the simulation model. In this case, for example, a program for converting peptide information into characteristic information, which is to be used by the server 14 or a computer other than the server 14, may be provided in advance to the terminal 12 or a computer other than the server 14.

[0094] [Second embodiment]

[0095] Next, a second embodiment will be described. The information processing system of the second embodiment supports a user in designing a peptide. In the second embodiment, a more specific content of the first embodiment will be described. In addition, the same parts of the configuration of the information processing system according to the second embodiment as those of the information processing system according to the first embodiment will be denoted by the same reference numerals and will not be described.

[0096] When a user designs a peptide, he or she may want to know the structure of the peptide that has the desired membrane permeability or in vivo persistence. In this regard, information on which part of the peptide structure should be changed to obtain a peptide with more preferable membrane permeability or in vivo persistence is useful information for the user who designs the peptide.

[0097] Therefore, the information processing system of the second embodiment identifies residues that have a particular effect on prediction information among a plurality of residues constituting a peptide, and presents the residues to the user as replacement candidate residues. This makes it possible to assist in the search for peptides with better membrane permeability or in vivo persistence. In addition, it is possible to provide the user with a navigation service for peptide design.

[0098] Fig. 10 is a block diagram showing an example of a configuration of an information processing system 210 according to the second embodiment. As shown in Fig. 10, the server 214 of the information processing system 210 according to the second embodiment further includes a design support unit 254. The design support unit 254 transmits information for supporting a user in designing a peptide to the terminal 12. A specific description will be given below.

[0099] The prediction unit 148 of the server 214 of the information processing system 210 of the second embodiment generates predicted information of a peptide by a trained model such as a neural network, as in the first embodiment. Also, the prediction unit 148 of the information processing device 210 extracts a feature vector x as feature information from peptide information of a peptide to be predicted, as in the first embodiment, and inputs the feature vector x to the trained model to generate predicted information.

[0100] The feature vector x is obtained from each of the multiple residues contained in the peptide. For example, the feature vector x=[x 11 ,x 12 ,x 21 ,x 22 ,x N1 ,x N2 , , ], [x 11 ,x 12, ] is the vector obtained from the first residue in the peptide, and [x 21 ,x 22 , ] is the vector obtained from the second residue in the peptide, and [x N1 ,x N2 , ···] is a vector obtained from the Nth residue in the peptide.

[0101] First, the design support unit 254 calculates a score (hereinafter simply referred to as "residue influence score") representing the degree to which each of the multiple residues that make up the peptide influences the prediction information by analyzing the parameters of a trained model for generating prediction information for the peptide, for example, using known technology.

[0102] Specifically, first, the design support unit 254 calculates each element x of the feature vector x input to the trained model. ij For the prediction information output from the trained model, the value y is expressed by the element x ij The differential value obtained by partially differentiating with respect to the element x of the value y represented by the prediction information is calculated. ij The partial derivative with respect to is expressed by the following equation.

[0103]

number

[0104] This differential value is obtained by analyzing the parameters of the trained model.

[0105]

number

[0106] The absolute value of is a score that indicates the degree to which each feature of the i-th residue influences the prediction information (hereinafter, also referred to simply as a "feature influence score"). The design support unit 254 calculates this feature influence score for each element x of the feature vector x. ij Calculate:

[0107] Fig. 11 is a diagram for explaining the feature amount influence score of this embodiment. Fig. 11 is also called a saliency map in other fields.

[0108] In the map shown in Figure 11, the horizontal axis "1, 2,..." represents the residue ID, and the vertical axis "x ij " represents the type of feature contained in the feature vector x. The shading of the map represents each element x of the feature vector x. ij This corresponds to the feature influence score, which indicates the degree to which the feature influences the prediction, and the darker the shading on the map, the higher the feature influence score.

[0109] In the example of FIG. 11, for example, the element x 71 has a high feature influence score. Therefore, if the residue with ID=7 is replaced with another residue, it is expected that the prediction information will change significantly. At this time, it is also possible to show the direction of the influence of the presence of this residue by checking the positive or negative differential value corresponding to the feature influence score.

[0110] Next, for each of the multiple residues, the design support unit 254 calculates a residue influence score for that residue, for example, by calculating the sum of the feature influence scores calculated for each element of the feature vector x. Then, the design support unit 254 identifies a residue having a residue influence score equal to or greater than a predetermined threshold value among the residue influence scores calculated for each of the multiple residues.

[0111] Note that the method of calculating the residue influence score from the feature influence score is not limited to the above-mentioned method. For example, the residue influence score may be a weighted average, a maximum value, or a minimum value of the feature influence scores calculated for each element of the feature vector x.

[0112] Next, the design support unit 254 sets the identified residues in the peptide structure as replacement candidate residues, and sets the replacement candidate residues as candidates for the change portion.

[0113] For example, the design support unit 254 sets residues whose residue influence scores are equal to or greater than a predetermined threshold as candidates for change locations, and then generates design support information that suggests replacement candidate residues.

[0114] The transmission unit 152 of the server 214 in the second embodiment transmits the design support information generated by the design support unit 254 to the terminal 12. The transmission unit 152 may transmit a map such as that shown in Fig. 11 to the terminal 12 as the design support information.

[0115] Other configurations and operations of the information processing system 210 of the second embodiment are similar to those of the first embodiment, and therefore description thereof will be omitted.

[0116] As described above, the server of the information processing system of the second embodiment calculates a residue influence score representing the degree to which each of the residues constituting the peptide influences the predicted information by analyzing the parameters of the trained model for generating predicted information. The server identifies residues having a residue influence score equal to or greater than a predetermined threshold among the residue influence scores calculated for each of the residues, sets the identified residues as replacement candidate residues in the peptide structure, and sets the replacement candidate residues as candidates for the change portion. The server also transmits design support information suggesting candidates for the change portion to the terminal. The terminal displays the design support information transmitted from the server on a display unit (not shown). This allows the user to obtain a clue as to which residues constituting the peptide should be changed and how. In addition, the server can support the user in designing the peptide.

[0117] The server of the second embodiment may specify residues having a residue influence score less than a predetermined threshold value among the residue influence scores calculated for each of the residues, and present the information to the user. In this case, residues that do not significantly affect membrane permeability or in vivo persistence even if changed can be specified, so that the user can obtain a clue as to which residues in the peptide should be changed.

[0118] [Third embodiment]

[0119] Next, a third embodiment will be described. The information processing system of the third embodiment generates a plurality of candidate peptides in which at least one of a plurality of residues constituting a peptide is replaced with another predetermined residue, generates prediction information for each of the plurality of candidate peptides, identifies residues that affect the prediction information, and specifically proposes residue exchange, which is different from the first and second embodiments. Note that the configuration of the information processing system according to the third embodiment is the same as that of the information processing system according to the second embodiment, and therefore the same reference numerals are used and the description is omitted.

[0120] The design support unit 254 of the server 214 in the third embodiment generates a candidate peptide in which at least one residue among a plurality of residues constituting a peptide is replaced with another predetermined residue such as alanine.

[0121] FIG. 12 shows a diagram for explaining candidate peptides. FIG. 12 shows an example of replacing a residue Re1 in a peptide S1 with another residue. For example, the design support unit 254 replaces the residue Re1 in the peptide S1 in FIG. 12 with another residue Re2 (e.g., glycine) to generate a candidate peptide S2. The design support unit 254 also replaces the residue Re1 in the peptide S1 in FIG. 12 with another residue Re3 (e.g., alanine) to generate a candidate peptide S3. The other residue may be, for example, a natural amino acid such as valine, leucine, arginine, or aspartic acid, or any artificial amino acid.

[0122] Then, the prediction unit 148 of the third embodiment uses a method similar to that of the first embodiment to generate prediction information of the peptide before the residues are substituted and prediction information of each of the multiple candidate peptides.

[0123] Next, for each of the multiple candidate peptides, the design support unit 254 calculates the difference between the predicted information of the candidate peptide and the predicted information of the peptide before the residue is replaced. Then, the design support unit 254 identifies candidate peptides whose difference is equal to or greater than a predetermined threshold, and identifies the position of the replaced residue in the identified candidate peptide.

[0124] When the difference between the predicted information of the peptide before the residue is replaced and the predicted information of the candidate peptide is large, at least one of the residues before and after the replacement is a residue that has a large effect on the predicted information. Therefore, the design support unit 254 identifies the position of the replaced residue in the candidate peptide with a large difference, and identifies the residue that was present at that position in the peptide before the residue was replaced.

[0125] The design support unit 254 then sets the residues at the identified positions in the peptide structure information that represents the peptide structure as replacement candidate residues, and sets the replacement candidate residues as candidates for the changed positions. The design support unit 254 then generates design support information that suggests replacing the replacement candidate residues with other residues.

[0126] FIG. 13 shows an example of the design support information. As shown in FIG. 13, for example, the design support information may include peptide information (e.g., structural formula of the peptide) before the residue is changed, information representing the residue of the replacement candidate (the dotted line portion in the figure represents the residue of the replacement candidate), and predicted information of the peptide. In addition, for example, the design support information may include peptide information (e.g., structural formula of the peptide) of the candidate peptide after the residue is replaced, predicted information of the candidate peptide, and information of the residue newly incorporated into the candidate peptide (e.g., structural formula of the residue). Note that the design support information may include a change in predicted information (or a weighted average of the change) when the replacement candidate residue is replaced by a plurality of different residues.

[0127] The transmitting unit 152 of the server 214 in the third embodiment transmits the design support information generated by the design support unit 254 to the terminal 12.

[0128] Other configurations and operations of the information processing system 210 of the third embodiment are similar to those of the first or second embodiment, and therefore description thereof will be omitted.

[0129] As described above, the server of the information processing system of the third embodiment generates a plurality of candidate peptides in which at least one of a plurality of residues constituting a peptide is replaced with another predetermined residue, and generates predicted information of the peptide before the residue is replaced and predicted information of each of the plurality of candidate peptides. Next, the server calculates the difference between the predicted information of the peptide before the residue is replaced and the predicted information of the candidate peptide for each of the plurality of candidate peptides, identifies a candidate peptide in which the difference is equal to or greater than a predetermined threshold, and identifies the position of the residue after replacement in the identified candidate peptide. Then, the server sets the residue at the identified position in the peptide structure as a replacement candidate residue, and sets the replacement candidate residue as a candidate for the change position. This allows the user to obtain a clue as to which residue of the residues constituting the peptide should be changed. In addition, it is possible to support the user in designing a peptide.

[0130] [Fourth embodiment]

[0131] Next, a fourth embodiment will be described. The information processing system of the fourth embodiment differs from the first to third embodiments in that, when a user provides predetermined data, the information processing system gives points representing a reward to the user. Note that the configuration of the information processing system of the fourth embodiment is similar to that of the information processing system of the first embodiment, and therefore the same reference numerals are used and the description thereof is omitted.

[0132] A large amount of data is required to generate accurate prediction information on the pharmacokinetics of a peptide. For example, experimental data indicating membrane permeability or in vivo persistence obtained by a given experiment on the pharmacokinetics of a certain peptide can be useful data for predicting the pharmacokinetics of other peptides.

[0133] Therefore, the information processing system of the fourth embodiment gives points representing a reward to a user when the user provides predetermined data. The points are used, for example, for discounts on the usage fees of services provided using the information processing system.

[0134] Fig. 14 is a block diagram showing an example of a configuration of an information processing system 410 according to the fourth embodiment. The server 414 of the information processing system 410 according to the fourth embodiment further includes a reward granting unit 456 and a reward storage unit 458, as shown in Fig. 14.

[0135] The reward granting unit 456 stores data provided by the user in the common database 142 or the common prediction model storage unit 145. For example, the reward granting unit 456 accepts at least one of the following data: experimental data on the pharmacokinetics of peptides, which is teacher data of prediction information of peptides transmitted from the terminal 12, information indicating the experimental method when the experimental data was obtained, a prediction model stored in the user prediction model storage unit 146, a prediction model used by the user on his / her own terminal (for example, a prediction model generated by the user on his / her own terminal or a prediction model brought in from outside by the user, etc.), and prediction information on the pharmacokinetics of peptides generated by the user. Then, the reward granting unit 456 stores the accepted data in the common database 142 or the common prediction model storage unit 145, which is an example of a storage unit managed by the information processing device 414.

[0136] The experimental data is data on the pharmacokinetics of the peptide to be predicted, obtained by a known experimental method. This experimental data is used, for example, as teacher data when generating a trained model. Information indicating the experimental method used when the experimental data was obtained is useful information because the reliability of the experimental data varies depending on which experimental method was used. If a prediction model such as a trained model generated by a user is provided, it is also useful because it is possible to generate a new prediction model based on the prediction model. Peptide prediction information generated by a user is also useful.

[0137] Therefore, the reward granting unit 456 grants points representing a reward for providing the data to the user ID assigned to the terminal 12 to which the data was transmitted.

[0138] For example, the reward granting unit 456 grants points to a user ID by updating a table stored in the reward storage unit 458 in which the user ID is associated with the points granted to the user ID.

[0139] FIG. 15 is an example of a table stored in the reward storage unit 458. In the example shown in FIG. 15, a user ID "USER_01" and points "P_USER_01" granted to the user ID are shown. When the reward granting unit 456 accepts the provision of data from the terminal 12 corresponding to the user ID "USER_01", the reward granting unit 456 updates the table stored in the reward storage unit 458 by adding a predetermined point to the point "P_USER_01" granted to the user ID "USER_01". Note that a rule regarding how many points are granted for the data provision is set in advance. For example, the reward granting unit 456 grants points to the user according to the type of data provided (e.g., whether the data provided is data representing experimental values, data representing a prediction model, or data representing prediction information), the amount of data, data history information (e.g., if the data provided is data representing experimental values ​​of membrane permeability, history information such as by which method the data was obtained from PAMPA test, Caco2 cells, MDCK cells, LLC-PK1 cells, etc.), data category (e.g., whether the data provided is data related to cyclic peptides or data related to small molecules), and the reliability of the user to whom the data was provided. The reliability of the user can be set manually or by a computer such as the server 414. For example, the reward granting unit 456 of the server 414 determines the reliability of the user according to the frequency of use and the history of use of the service provided by the information processing system 410. For example, the reward granting unit 456 sets the reliability of the user to be higher as the frequency of use increases. Alternatively, the reward granting unit 456 sets the reliability of a user who has used the system for a longer period of time according to the usage history so that the reliability is higher. In addition, as for the points when data is provided from a user, for example, the reward granting unit 456 may calculate an estimated value of the contribution of the provided data to the performance improvement when the data is reflected in the prediction model, and change the points to be granted to the user based on the estimated value.For example, the reward granting unit 456 may calculate the similarity between data provided by a user and data already held by the server 414, and may grant high points to a user who provides data with low similarity. This encourages the provision of data that is not held by the server 414. Also, for example, the reward granting unit 456 may not grant points when data that is thought to have a negative effect on peptide prediction is provided.

[0140] In this way, by giving an incentive to the user for providing data, more data can be stored in the common database 142 or the common prediction model storage unit 145 of the server 414, and the data can be used to predict the pharmacokinetics of peptides. The method of giving points for providing a prediction model is almost the same as that described above. For example, the performance of the provided prediction model may be estimated using test data prepared in advance and the correct answer is known, and high points may be given to a prediction model that is expected to perform well. Also, for example, high points may be given to a prediction model that has improved predictions for a peptide that performed poorly in a conventional prediction model.

[0141] As described above, when at least one of the experimental data, which is teacher data for peptide prediction information, information representing the experimental method used to obtain the experimental data, the prediction model used by the user, and the prediction information on the pharmacokinetics of the peptide generated by the user, transmitted from the terminal, is stored in a storage unit managed by the server, the server of the information processing system of the fourth embodiment gives points representing a reward for providing the data to the user ID given to the terminal. This makes it possible to collect more data useful for predicting the pharmacokinetics of the peptide.

[0142] The present disclosure is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present disclosure.

[0143] In addition, although the above describes an embodiment in which the program according to the present invention is pre-stored (installed) in a memory unit (not shown), the program according to the present invention can also be provided in a form in which it is recorded on a recording medium such as a CD-ROM, a DVD-ROM, or a micro SD card.

[0144] In the above embodiment, the processes executed by the CPU after reading the software (program) may be executed by various processors other than the CPU. Examples of the processor in this case include a PLD (Programmable Logic Device) such as an FPGA (Field-Programmable Gate Array) whose circuit configuration can be changed after manufacture, and a dedicated electric circuit such as an ASIC (Application Specific Integrated Circuit) which is a processor having a circuit configuration designed exclusively for executing a specific process. Each process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (for example, a plurality of FPGAs, a combination of a CPU and an FPGA, etc.). The hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0145] Furthermore, each process of the present embodiment may be implemented by a computer or server equipped with a general-purpose processor and a storage device, and each process may be executed by a program. This program is stored in a storage device, and may be recorded on a recording medium such as a magnetic disk, an optical disk, or a semiconductor memory, or may be provided through a network. Of course, any other components do not have to be implemented by a single computer or server, and may be distributed and implemented on multiple computers connected by a network. [Explanation of symbols]

[0146] 10. Information Processing Systems 12A, 12B, 12C terminals 14 Server 16 Network 140 Receiving unit 142 Common Database 144A, 144B, 144C User database 145 Common Prediction Model Memory Unit 146A, 146B, 146C User prediction model storage unit 148 Prediction Department 150 Processing section 152 Transmitter 254 Design Support Department 456 Reward Department 458 Reward storage section

Claims

1. A receiving unit that receives a request signal transmitted from a terminal; A prediction unit that generates predicted information regarding the pharmacokinetics of the peptide in response to the request signal; a transmission unit that transmits the prediction information generated by the prediction unit to the terminal; a design support unit that generates design support information including candidates for modification sites of elements that constitute the peptide; Including, the design support unit calculates, for each of a plurality of residues constituting the peptide, a residue influence score representing a degree to which the residue influences the prediction information by analyzing parameters of a trained model for generating the prediction information; identifying a residue having a residue influence score equal to or greater than a predetermined threshold among the residue influence scores calculated for each of the plurality of residues; The identified residue in the peptide structure is set as a replacement candidate residue, and the replacement candidate residue is set as a candidate for a change portion; The transmission unit transmits the design support information to the terminal. Information processing device.

2. the request signal includes selection information of a prediction model for generating prediction information; The prediction unit selects a prediction model according to the selection information from the plurality of prediction models, and inputs peptide information representing the peptide to the selected prediction model, thereby generating the prediction information of the peptide. The information processing device according to claim 1 .

3. The request signal includes a user ID, Each of the plurality of prediction models is a prediction model prepared in advance for each user, The prediction unit generates the prediction information of the peptide by inputting peptide information representing the peptide to a prediction model corresponding to the user ID among the plurality of prediction models. The information processing device according to claim 2 .

4. Each of the plurality of prediction models is a trained model that has been trained in advance using training data prepared in advance for each user. The information processing device according to claim 3 .

5. The request signal includes a user ID, a processing unit that stores the prediction information in a database corresponding to the user ID among a plurality of databases prepared in advance for each user according to the user ID, The information processing device according to any one of claims 1 to 4.

6. The request signal includes a user ID, and a processing unit for storing the data transmitted from the terminal in a database corresponding to the user ID among a plurality of databases prepared in advance for each user according to the user ID. The information processing device according to any one of claims 1 to 5.

7. The processing unit stores data regarding the pharmacokinetics of the peptide transmitted from the terminal in the database. The information processing device according to claim 6.

8. The transmission unit transmits the prediction information predicted by the prediction unit in association with the peptide to be predicted to the terminal. The information processing device according to any one of claims 1 to 7.

9. the design support unit generates a plurality of candidate peptides in which at least one residue among a plurality of residues constituting the peptide is replaced with another predetermined residue; the prediction unit generates the prediction information of the peptide before residue substitution and the prediction information of each of the plurality of candidate peptides; the design support unit calculates, for each of the plurality of candidate peptides, a difference between the predicted information of the peptide before substitution of a residue and the predicted information of the candidate peptide, identifies the candidate peptide in which the difference is equal to or greater than a predetermined threshold, and identifies the position of the residue after substitution in the identified candidate peptide; setting the residue at the specified site in the peptide structure as a replacement candidate residue, and setting the replacement candidate residue as a candidate for a change site; The information processing device according to any one of claims 1 to 8.

10. a reward granting unit that grants points representing a reward for providing the data to a user ID assigned to the terminal when at least one of the following data, which are teacher data for the prediction information of the peptide and which are transmitted from the terminal, is stored in a storage unit managed by the information processing device: experimental data on the pharmacokinetics of the peptide, information representing an experimental method used to obtain the experimental data, a prediction model used by a user, and prediction information on the pharmacokinetics of the peptide generated by the user; The information processing device according to any one of claims 1 to 9.

11. Receive a request signal sent from a terminal; generating predictive information regarding the pharmacokinetics of the peptide in response to the demand signal; Transmitting the generated prediction information to the terminal; An information processing method in which a process is executed by a computer, comprising the steps of: generating design support information including candidates for modification of elements constituting the peptide; For each of a plurality of residues constituting the peptide, a residue influence score is calculated that indicates the degree to which the residue influences the prediction information by analyzing parameters of a trained model for generating the prediction information; identifying a residue having a residue influence score equal to or greater than a predetermined threshold among the residue influence scores calculated for each of the plurality of residues; The identified residue in the peptide structure is set as a replacement candidate residue, and the replacement candidate residue is set as a candidate for a change portion; Transmitting the design support information to the terminal; Information processing methods.

12. Receive a request signal sent from a terminal; generating predictive information regarding the pharmacokinetics of the peptide in response to the demand signal; Transmitting the generated prediction information to the terminal; An information processing program for causing a computer to execute a process, generating design support information including candidates for modification of elements constituting the peptide; For each of a plurality of residues constituting the peptide, a residue influence score is calculated that indicates the degree to which the residue influences the prediction information by analyzing parameters of a trained model for generating the prediction information; identifying a residue having a residue influence score equal to or greater than a predetermined threshold among the residue influence scores calculated for each of the plurality of residues; The identified residue in the peptide structure is set as a replacement candidate residue, and the replacement candidate residue is set as a candidate for a change portion; Transmitting the design support information to the terminal; Information processing program.

13. An information processing system including a terminal and the information processing device according to any one of claims 1 to 10, The terminal transmits a request signal to the information processing device; the information processing device receives the request signal transmitted from the terminal, generates prediction information regarding the pharmacokinetics of the peptide in response to the request signal, and transmits the generated prediction information to the terminal; The terminal causes a display unit to display the prediction information transmitted from the information processing device; the information processing device generates design support information including candidates for change locations of elements constituting the peptide, and transmits the design support information to the terminal; The terminal further displays the design support information on the display unit. Information processing system.

Images