New-drug candidate discovery system

Abstract

A new-drug candidate discovery system is provided. The new-drug candidate discovery system is implemented as a platform for providing, to a user in the form of a web service, a function or service required for discovering a new-drug candidate, and may comprise: a project management module for generating a project capable of adding work for discovering a new-drug candidate; a simulation management module for generating, on the generated project, a simulation desired by the user; a simulation configuration module for configuring a simulation flow for the simulation; and a simulation flow management module for managing information about a node capable of preceding or following in the simulation flow configuration.

Description

New drug candidate discovery system

[0001] The disclosure relates to a new drug candidate discovery system, and more specifically, to a new drug candidate discovery system implemented as a cloud platform based on graph computing.

[0002] The discovery of new drug candidates is an early stage in drug development, and protein structure-based in silico screening methods are attracting attention. Protein structure-based in silico screening is a method for identifying potential new drug candidates from compound databases based on the three-dimensional structure of proteins to which the new drug candidate can bind. In silico refers to computer programming in computer simulations or virtual experiments. Protein structure-based in silico screening can predict compound binding affinity by simulating the interaction between proteins and compounds. Protein structure-based in silico screening can identify compounds that precisely fit the active site of a protein, and can be useful for discovering compounds in novel chemical spaces, unlike ligand-based screening, which relies on known ligands and is limited in discovering entirely new chemical structures because it searches for compounds similar to existing ligands.

[0003] In the discovery of new drug candidates, there is a growing need to integrate big data analysis and artificial intelligence technologies with libraries of billions of compounds, utilizing a variety of analytical tools in parallel. To meet this demand, pharmaceutical research institutes and research institutes such as universities have been installing and operating various software. For example, the European Molecular Biology Laboratory (EMBL) has developed and offers downloadable tools for bioinformatics, or provides simple web applications.

[0004] However, these tools are provided without account creation, making them difficult for pharmaceutical companies demanding security to use. Data sharing between the tools is challenging, and they are installed on various operating systems such as Windows, Mac, and Linux, making consistent data management difficult. In this context, data analysis tools such as KNIME (Konstanz Information Miner) have emerged, attempting to visualize data processing in a flowchart format. However, most of these data analysis tools are installable, limiting accessibility. Furthermore, they are not specialized for new drug discovery, requiring significant effort and time to directly utilize them for the discovery of new drug candidates.

[0005] The task to be solved is to provide a new drug candidate discovery system that provides functions and a user interface optimized for the discovery of new drug candidates.

[0006] According to one embodiment, a new drug candidate discovery system is implemented as a platform that provides users with functions or services necessary for discovering new drug candidates in the form of a web service, and may include a project management module that creates a project that can add tasks for discovering new drug candidates; a simulation management module that creates a simulation desired by the user on the created project; a simulation setting module that sets a simulation flow for the simulation; and a simulation flow management module that manages information on nodes that can precede or follow the simulation flow setting.

[0007] In some embodiments, the simulation setting module displays a simulation setting area to a user through a display device of a computing device on which the new drug candidate discovery system is operated, the simulation setting area includes a canvas area, and the simulation setting area may provide a function for the user to arrange and connect a plurality of work modules in the canvas area based on graph computing.

[0008] In some embodiments, the simulation setup area includes a protein structure data input area and a task module selection area, wherein the protein structure data input area and the task module selection area may include one or more possible objects that can be changed into nodes by being dragged and dropped into the canvas area.

[0009] In some embodiments, the protein structure data input area includes one or more objects related to a function of uploading protein structure data, and the one or more objects may include objects that can be converted into one of a first node of a function of receiving protein structure data in the form of a PDB (Protein Data Bank) file from a user, a second node of a function of receiving protein structure data in the form of a PDB code from a user, a third node of a function of receiving protein structure data in the form of a protein sequence file from a user, and a fourth node of a function of receiving protein structure data in the form of a protein sequence from a user by dragging and dropping them onto the canvas area.

[0010] In some embodiments, the work module selection area includes one or more objects related to a function of performing detailed work on discovering new drug candidates based on uploaded protein structure data, and the one or more objects may be converted into one of a fifth node having a function of finding an optimal docking site in a target protein structure, a sixth node having a function of predicting an amino acid sequence into a protein 3-dimensional structure, a seventh node having a function of analyzing and aligning actual binding energy (kcal / mol) and providing it to a user, and an eighth node having a function of converting kcal / mol into kd / Ki (μM) for a selected ligand and performing comparative analysis.

[0011] In some embodiments, the nodes that can precede the fifth node may include the first node, the second node, the third node, the fourth node, and the sixth node, and the nodes that can follow the fifth node may include the seventh node.

[0012] In some embodiments, the nodes that can precede the fifth node may include the first node, the second node, the third node, the fourth node, and the sixth node, and the nodes that can follow the fifth node may include the seventh node.

[0013] In some embodiments, a node that can precede the seventh node may include the fifth node, and a node that can succeed the seventh node may include the eighth node.

[0014] In some embodiments, when a first result is derived according to a first simulation flow generated from the first node to the eighth node, and a second result is derived according to a second simulation flow generated differently from the first simulation flow from the first node to the eighth node, a transfer node that receives the first result and the second result and transfers them to a subsequent node may be placed in the canvas area.

[0015] In some embodiments, the simulation settings area includes an external module provision area, and the external module provision area may include one or more objects that can be dragged and dropped into the canvas area to be changed into nodes having any externally provided functionality.

[0016] In some embodiments, the nodes placed in the canvas area are displayed with node connection shapes, and while the user clicks and holds the node connection shape displayed on the right side of a node, the color or shape of the node connection shape displayed on the left side of a node that can be connected among other nodes may be changed and displayed.

[0017] In some embodiments, the simulation flow management module may manage information for determining whether a connection is possible between the nodes through metadata.

[0018] In some embodiments, the new drug candidate discovery system may further include a token management module that deducts tokens that a user must pay for detailed tasks provided by the new drug candidate discovery system.

[0019] In some embodiments, the token management module determines the amount of tokens to be deducted for each detailed task for discovering new drug candidates, and the amount of tokens to be deducted may be determined by considering a plurality of factors including the type of detailed task, the workload of the detailed task, and the difficulty of the detailed task.

[0020] In some embodiments, the token management module may adjust the amount of tokens deducted to decrease linearly or non-linearly depending on the progress of the detailed task.

[0021] According to embodiments, a new drug candidate discovery system can be provided that provides functions and a user interface optimized for the discovery of new drug candidates.

[0022] Figure 1 is a block diagram illustrating a new drug candidate discovery system according to one embodiment.

[0023] Figures 2 to 6 are drawings showing exemplary screens of a new drug candidate substance discovery system according to one embodiment.

[0024] Figure 7 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0025] Figure 8 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0026] Figure 9 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0027] Figure 10 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0028] Figure 11 is a block diagram showing a new drug candidate discovery system according to one embodiment.

[0029] FIG. 12 is a block diagram illustrating a computing device according to one embodiment.

[0030] Below, with reference to the attached drawings, embodiments of the present invention are described in detail so that those skilled in the art can easily practice them. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In addition, in the drawings, parts irrelevant to the description are omitted for clarity of description, and similar parts are designated with similar reference numerals throughout the specification.

[0031] Throughout the specification and claims, whenever a part is said to "include" a certain component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise stated.

[0032] Additionally, terms such as “part,” “unit,” and “module” described in the specification may mean a unit capable of processing at least one function or operation described in the specification, which may be implemented by hardware, software, or a combination of hardware and software.

[0033] Figure 1 is a block diagram illustrating a new drug candidate discovery system according to one embodiment.

[0034] A new drug candidate discovery system (1) according to one embodiment may be implemented as a platform that provides users with functions or services necessary for discovering new drug candidates in the form of a web service. Specifically, the new drug candidate discovery system (1) may provide users with various functions or services, such as detecting and removing errors (or mistakes) in protein structure files without knowledge of other fields, enabling biologists with specific ideas for new drug development to utilize in silico screening methods, efficiently detecting and providing users with Enzymatically Active Pockets for Docking Calculation (EAPDC) from protein structures for docking, providing users with real-time rankings of candidate substances based on docking binding energy during a long-time docking simulation, increasing reliability by predicting docking binding energy in two stages, or enabling verification of discovered candidate substances through linkage with a verification company.

[0035] The new drug candidate discovery system (1) can provide the same functions or services to users in various environments using a web interface. Specifically, for example, some users may receive services from the new drug candidate discovery system (1) using a mobile device such as a smart phone or tablet computer running a mobile operating system, other users may receive services from the new drug candidate discovery system (1) using a laptop computer running a Windows operating system, and still other users may receive services from the new drug candidate discovery system (1) using a desktop computer running a Linux operating system. In other words, the new drug candidate discovery system (1) is implemented as a platform as a web service, allowing users in different environments to use the same functions or services required for calculating in silico candidate compounds using an artificial intelligence neural network and conducting preclinical experiments on new drug candidates, thereby enhancing compatibility and user convenience, and resolving various issues that required improvement in the in silico calculations previously performed through a terminal on Linux.

[0036] Referring to FIG. 1, a new drug candidate discovery system (1) according to one embodiment may include a project management module (10), a simulation management module (12), a simulation setting module (14), and a simulation flow management module (16).

[0037] The project management module (10) can create a project to which a series of tasks for discovering new drug candidates can be added. Furthermore, the project management module (10) can display the created project to a user via the display device of the computing device on which the new drug candidate discovery system (1) operates.

[0038] In some embodiments, the project management module (10) can perform project name encryption. For a project in which project name encryption is enabled, the project management module (10) can display the original, unencrypted project name to the user who created the project and users authorized to use the project, and display the encrypted project name to users who do not have the authorization to use the project. There is a concern that the project name may contain keywords related to the discovery of new drug candidates that must be kept secure. By preventing the project name from being exposed as is to users who are not participating in the same project, the security of the new drug candidate discovery system (1) used by multiple users can be enhanced. Enabling or disabling project name encryption can be done by changing the option settings not only when creating a project, but also after the project has been created.

[0039] In some embodiments, the project management module (10) may support project membership via invitation codes. For example, if a user creates a project, the user can send the invitation code to another user, and the other user who receives the invitation code can join the project by entering the invitation code. In other words, users can participate in projects created by others via invitation codes. Accordingly, users with diverse knowledge in various fields can participate in a single project to discover new drug candidates. In addition, the project management module (10) may support setting permissions for project members. For example, the project management module (10) may grant administrator permissions to specific members. Of course, the project management module (10) may also support users who are participating in a project to leave the project.

[0040] The simulation management module (12) can create a user-defined simulation on a project created by the project management module (10). A single simulation can include multiple task modules with detailed functions for performing new drug candidate discovery, and a single project can include multiple simulations. After creating a simulation, the user can upload protein structure data for performing the multiple task modules.

[0041] The simulation setting module (14) can display a simulation setting area (140) to the user via the display device of the computing device on which the new drug candidate discovery system (1) is driven when the user selects one or more simulations managed by the simulation management module (12). In the simulation setting area (140), the user can set the simulation flow so that the simulation desired by the user can be performed by arranging and connecting multiple work modules based on graph computing. In particular, the simulation setting area (140) can be laid out so that the user can see at a glance the function of uploading protein structure data and the work modules that perform detailed work related to new drug candidate discovery based on the uploaded protein structure data. This configuration can improve the problem that, in the past, detailed work related to new drug candidate discovery was provided as separate tools with low compatibility, making data sharing and consistent data management difficult. The simulation setting area (140) can include a protein structure data input area (142), a work module selection area (144), and a canvas area (146).

[0042] The protein structure data input area (142) may include one or more objects that can be dragged and dropped onto the canvas area (146). For example, the protein structure data input area (142) may include first to fourth objects. The first object may be converted into a first node by being dragged and dropped onto the canvas area (146), and the first node may receive protein structure data in the form of a PDB (Protein Data Bank) file from the user. The second object may be converted into a second node by being dragged and dropped onto the canvas area (146), and the second node may receive protein structure data in the form of a PDB code from the user. The third object may be converted into a third node by being dragged and dropped onto the canvas area (146), and the third node may receive protein structure data in the form of a protein sequence file from the user. The fourth object can be converted into a fourth node by dragging and dropping it into the canvas area (146), and the fourth node can receive protein structure data in the form of a protein sequence from the user.

[0043] The task module selection area (144) may also include one or more objects that can be dragged and dropped onto the canvas area (146). For example, the task module selection area (144) may include a fifth object to an eighth object. The fifth object may be converted into a fifth node by being dragged and dropped onto the canvas area (146), and the fifth node may perform a task of finding an optimal docking site in a target protein structure. In particular, the fifth node may use an artificial intelligence language model based on Natural Language Processing (NLP) to automatically find an active site in a target protein and generate an optimal docking grid box. In addition, the fifth node may automatically correct various errors that may exist in a protein structure file (i.e., a PDB file). That is, the fifth node can automatically detect and remove anisotropic B-factors from PDB files, detect alternative conformations in the amino acid residue field and correct them to non-alternative conformations, and detect unusual amino acids in the amino acid residue field and correct them to 20 types of non-specific amino acids. The sixth entity can be converted into the sixth node by dragging and dropping it into the canvas area (146), and the sixth node can predict amino acid sequences into protein tertiary structures. The seventh entity can be converted into the seventh node by dragging and dropping it into the canvas area (146), and the seventh node can analyze the actual binding energies (kcal / mol) and sort them in order of best to provide them to the user.That is, the seventh node can perform grid-based in silico docking based on the input protein structure, determine the chemical pose using LGA (Lamarckian Genetic Algorithm), and calculate the binding energy using the empirical scoring function. The eighth object can be converted into the eighth node by dragging and dropping it into the canvas area (146), and the eighth node can perform a comparative analysis by selecting the desired ligand and converting kcal / mol to kd / Ki (μM). That is, the eighth node can predict the Kd / Ki value in μM using a CNN artificial intelligence model that has learned the protein-ligand structure and Kd / Ki / IC50 values.

[0044] The first to eighth objects can be converted into the first to eighth nodes by dragging and dropping them onto the canvas area (146), respectively. The user can freely arrange the first to eighth nodes onto the canvas area (146) in the desired execution order according to the purpose and environment of the simulation. In addition, the user can set the connection relationship between the first to eighth nodes arranged on the canvas area (146) through edges. The user can create a new drug candidate discovery simulation by arranging nodes and connecting edges between nodes. In addition, the user can clearly and easily manage information on protein structure data input by creating, changing, and deleting nodes in a simulation flow whose complexity increases as the number of nodes placed on the canvas area (146) increases and the number of edges between nodes increases.

[0045] In some embodiments, the simulation setting area (140) may further include an external module provision area (148). The external module provision area (148) may include a ninth entity that can be converted into a ninth node having any function provided externally to the new drug candidate discovery system (1) by dragging and dropping it into the canvas area (146). Accordingly, functions provided by other systems operating in conjunction with the new drug candidate discovery system (1) can be easily included in the simulation by setting a node in the canvas area (146).

[0046] As described above, the user can place and freely move nodes by clicking and dragging a desired object in the protein structure data input area (142), the work module selection area (144), and the external module provision area (148) and dropping it on the canvas area (146). In some embodiments, a node connection shape may be displayed on a node placed on the canvas area (146). While the user clicks and holds a node connection shape displayed on the right side of a node, the color or shape of the node connection shape displayed on the left side of a node that can be connected among other nodes may change. The user cannot establish a connection for a node whose color of the node connection shape displayed on the left side has not changed, and can establish a connection only for a node whose color of the node connection shape displayed on the left side has changed. Accordingly, the user can prevent the creation of an incorrect simulation flow by changing the color of the node connection shape without having to be aware of whether a causal relationship can be established between each node.

[0047] Information for determining whether a connection between nodes is possible can be managed by the simulation flow management module (16). The simulation flow management module (16) can manage information on nodes that can precede or follow in the simulation setup, and can use a separate data structure such as metadata as needed. In addition, the simulation flow management module (16) can also update information, such as by reflecting changes in information on nodes that can precede or follow in the metadata.

[0048] When a user wants to make a connection to another node whose color has changed in the node connection shape displayed on the left side while clicking and holding the node connection shape displayed on the right side of a node, the user can make a connection by clicking the node connection shape of a node and then clicking the node connection shape of another node, or by clicking and holding the node connection shape of a node and dragging it to the node connection shape of another node to make a connection. When the connection is completed, a connection line will appear between the nodes, and the connection between the nodes can be disconnected by clicking the X displayed on the connection line.

[0049] Objects included in the task module selection area (144) can be converted into nodes on the canvas area (146), and a run button can be created within the node. The user can click the run button to perform the task of the corresponding node. Before the task begins, the number of tokens required to perform the task is displayed, and upon user confirmation, the tokens are deducted and the task can be executed. When the task is completed, the run button changes to a result button, and a download button can be additionally created. The user can view the task execution results by clicking the result button, and can download the task execution results by clicking the download button.

[0050] In this way, by establishing the connection between nodes with individual functions in the discovery of new drug candidates, a simulation flow suitable for optimal compound development can be created. Furthermore, when functions are added internally or externally to the new drug candidate discovery system (1), nodes corresponding to the added functions can be created, facilitating the establishment of connections with existing nodes.

[0051] Figures 2 to 6 are drawings showing exemplary screens of a new drug candidate substance discovery system according to one embodiment.

[0052] Referring to FIG. 2, the simulation setting area (140) may include a protein structure data input area (142), a work module selection area (144), a canvas area (146), and an external module provision area (148). As illustrated, the protein structure data input area (142) may include one or more objects related to a function of uploading protein structure data, the work module selection area (144) may include one or more objects related to detailed work related to new drug candidate discovery performed based on the uploaded protein structure data, and the external module provision area (148) may include one or more objects related to any function provided externally. Such objects may be converted into nodes by being dragged and dropped by a user into the canvas area (146), and the nodes may be connected by edges to form a graph, which may represent the flow of the simulation. Of course, the number or types of objects included in the work module selection area (144), canvas area (146), and external module provision area (148) shown in the drawings are examples for the purpose of explaining the embodiments, and the scope of the present invention is not limited to those shown.

[0053] Referring to FIG. 3, the protein structure data input area (142) may include a first entity (1420), a second entity (1421), a third entity (1422), and a fourth entity (1423). The first entity (1420), indicated as “Upload PDB File,” is related to a function of receiving protein structure data in the form of a PDB file, and the second entity (1421), indicated as “Enter PDB Code,” is related to a function of receiving protein structure data in the form of a PDB code. The third entity (1422), indicated as “Protein Sequence File (Fasta),” is related to a function of receiving protein structure data in the form of a protein sequence file, and the fourth entity (1423), indicated as “Protein Sequence (Text),” is related to a function of receiving protein structure data in the form of a protein sequence.

[0054] Meanwhile, the work module selection area (144) may include a fifth entity (1440), a sixth entity (1441), a seventh entity (1442), and an eighth entity (1443). The fifth entity (1440), indicated as "PocketFinder," is related to a function of automatically finding an optimal docking site, and the sixth entity (1441), indicated as "CaliciFold," is related to a function of predicting an amino acid sequence into a protein tertiary structure. The seventh entity (1442), indicated as "AI-Dock," is related to a function of automatically sorting the amino acid sequences in order of the best by analyzing the actual binding energy (kcal / mol), and the eighth entity (1443), indicated as "DeepCalici," is related to a function of converting kcal / mol into kd / Ki (μM) and performing a comparative analysis.

[0055] In some embodiments, the fifth entity (1440) may detect and remove anisotropic B-factors from PDB files among protein structure data files input by the user, detect alternative conformations in the amino acid residue field and correct them to non-alternative conformations, and detect unusual amino acids in the amino acid residue field and correct them to 20 types of non-specific amino acids. For example, if a docking simulation is performed without removing anisotropic B-factors from the PDB file, an error may occur due to failure to recognize the PDB file format or failure to read the PDB file, and if a docking simulation is performed with alternative conformations or unusual amino acids in the amino acid residue field, an error may occur due to an unknown amino acid, which may result in a decrease in accuracy in performing the in silico screening method or an increase in the failure rate in discovering candidate substances. By automatically handling such error-causing factors, the user can prevent inefficiencies and inaccuracies that may arise from manual modification of PDB files, and can omit collaboration with structural biologists. In addition, the PDB file preprocessing process can be automatically handled internally without the user being aware of it, providing an environment in which the user can focus solely on discovering new drug candidates. In addition, in some embodiments, the fifth entity (1440) can perform corrections for missing residues in the protein structure of a PDB file. Specifically, the missing residues can be detected by examining the gaps between residues in the protein structure of the PDB file, and when the missing residues are found, a protein amino acid sequence suitable for completing the missing residues can be obtained through a sequence database search, and the missing residues can be automatically completed in the protein amino acid sequence thus obtained.Accordingly, subsequent work can be performed based on an error-free protein structure file from which errors that may occur during simulation have been removed.

[0056] The fifth entity (1440) can detect an Enzymatically Active Pocket for Docking Calculation (EAPDC) from a protein structure file to determine a docking calculation site. Specifically, the fifth entity (1440) can predict a docking site (i.e., EAPDC) in a target protein structure using an artificial intelligence language model. Specifically, the fifth entity (1440) calculates depth values ​​of a pocket based on a Solvent Accessible Surface (SAS) of a target protein surface, generates a Gradient Class Activation Map for amino acids that contributed in the process of predicting the activity of the target protein, and, considering the depth values ​​of the pocket and the values ​​for amino acids with high contributions in the Gradient Class Activation Map, can determine a site that has a high influence on the activity of the target protein as a docking calculation site. Here, the gradient class activation map may be extracted from a GCN (Graph Convolutional Network) trained using EC numbers (Enzyme Commission numbers) or GO numbers (Gene Ontology numbers), which are implemented as a natural language processing model embedding layer. Furthermore, the natural language processing model implemented as an embedding layer may be a Transformer natural language processing model.

[0057] Meanwhile, the external module provision area (148) includes a ninth entity (1480), and the ninth entity (1480) indicated as “CRO-Order” is related to the function of transmitting a verification request for a candidate substance desired by the user to the verification company server.

[0058] As illustrated in FIG. 3, for example, a user can drag and drop a first object (1420) indicated as “PDB File Upload” into the canvas area (146), whereby it can be converted into a node (N31). The node (N31) can include a button that enables the function of inputting protein structure data in the form of a PDB file. In addition, the node (N31) can display related information such as the identifier of the corresponding node, the task execution status, etc. In addition, the node (N31) can include a button that can delete itself. As illustrated with the node (N31), the second object (1421) to the ninth object (1480) can be dragged and dropped into the canvas area (146) to be converted into nodes that display their own buttons, information, etc.

[0059] As illustrated in FIG. 4, a user can drag and drop a second object (1421) indicated as “Enter PDB Code” into the canvas area (146), whereby it can be converted into a node (N41). The node (N41) can include a button that enables a function of inputting protein structure data in the form of a PDB code. Subsequently, the user can drag and drop a fifth object (1440) indicated as “PocketFinder” into the canvas area (146), whereby it can be converted into a node (N42). The node (N42) can include a button that enables a function of automatically finding an optimal docking site.

[0060] As illustrated in FIG. 5, a node connection shape may be displayed on a node placed in the canvas area (146). Specifically, a node connection shape (CS1) may be displayed on the right side of a node (N51), and a node connection shape (CS2) may be displayed on the left side of a node (N52). While the user is clicking on the node connection shape (CS1) displayed on the right side of a node (N51), the color or shape of the node connection shape (CS1) displayed on the left side of a node (N52) that can be connected among other nodes may be changed. The user cannot establish a connection for a node whose color of the node connection shape displayed on the left side has not changed, and can establish a connection only for a node whose color of the node connection shape displayed on the left side has changed. While the user is clicking on the node connection shape (CS1) displayed on the right side of a node (N51), the color or shape of the node connection shape (CS1) displayed on the left side of a node (N52) that can be connected among other nodes can be changed based on information about preceding or succeeding nodes provided from the simulation flow management module (16).

[0061] As illustrated in FIG. 6, when you want to make a connection to another node (N62) whose color has changed in the node connection shape (CS2) displayed on the left side while clicking on the node connection shape (CS1) displayed on the right side of a node (N61), you can make a connection by clicking on the node connection shape (CS1) of a node (N61) and then clicking on the node connection shape (CS2) of another node (N62), or you can make a connection by clicking and dragging the node connection shape (CS1) of a node (N61) to the node connection shape (CS2) of another node (N62). When the connection is completed, a connection line is displayed between the nodes, and you can release the connection between the nodes by clicking on the X displayed on the connection line.

[0062] Figure 7 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0063] Referring to Fig. 7, in an example of generating a simulation flow, nodes (N71) to (N74) are illustrated. The node connection shape on the right side of the node (N71) that receives protein structure data in the form of PDB code and the node connection shape on the left side of the node (N72) that automatically finds the optimal docking site are connected by an edge, the node connection shape on the right side of the node (N72) and the node connection shape on the left side of the node (N73) that automatically aligns by analyzing the actual binding energy (kcal / mol) are connected by an edge, and the node connection shape on the right side of the node (N73) and the node connection shape on the left side of the node (N74) that converts kcal / mol to kd / Ki (μM) and performs comparative analysis are connected by an edge. As explained above, the user cannot establish a connection for a node whose color of the node connection shape displayed on the left side has not changed among other nodes, and can establish a connection only for a node whose color of the node connection shape displayed on the left side has changed. Therefore, the user does not have to consider whether nodes can precede or follow each other in the discovery of new drug candidates, which increases convenience.

[0064] In some embodiments, information about possible preceding or succeeding nodes may be predetermined as follows.

[0065] Preceding NodesNodesFollowing NodesPDB File UploadPocketFinderAI-DockPDB Code InputProtein Sequence File (Fasta)Protein Sequence (Text)CaliciFoldProtein Sequence File (Fasta)CaliciFoldPocketFinderProtein Sequence (Text)PocketFinderAI-DockDeepCaliciAI-DockDeepCalici-

[0066] Nodes that can precede a node that automatically finds an optimal docking site ("PocketFinder") may include a node that inputs protein structure data in the form of a PDB file ("Upload PDB File"), a node that inputs protein structure data in the form of a PDB code ("Enter PDB Code"), a node that inputs protein structure data in the form of a protein sequence file ("Protein Sequence File (Fasta)"), a node that inputs protein structure data in the form of a protein sequence ("Protein Sequence (Text)"), and a node that predicts an amino acid sequence as a protein 3-dimensional structure ("CaliciFold"), and nodes that can follow it may include a node that automatically aligns amino acid sequences by analyzing actual binding energy (kcal / mol) ("AI-Dock"). Nodes that can precede a node that automatically finds an optimal docking site ("CaliciFold") may include a node that inputs protein structure data in the form of a protein sequence file ("Protein Sequence File (Fasta)"), a node that inputs protein structure data in the form of a protein sequence ("Protein Sequence (Text)"), and nodes that can follow it may include a node that automatically finds an optimal docking site. May contain a node ("PocketFinder").

[0067] A node that can precede a node that automatically sorts by analyzing actual binding energy (kcal / mol) (“AI-Dock”) can include a node that automatically finds the optimal docking site (“PocketFinder”), and a node that can follow a node that converts kcal / mol to kd / Ki (μM) and performs comparative analysis (“DeepCalici”).

[0068] A node that can precede the node that performs comparative analysis by converting kcal / mol to kd / Ki(μM) (“DeepCalici”) may include a node that automatically sorts by analyzing the actual binding energy (kcal / mol) (“AI-Dock”).

[0069] Information about such preceding or succeeding nodes can be managed by the simulation flow management module (16), and a separate data structure such as metadata can be used as needed. Furthermore, the simulation flow management module (16) can also update information, such as by reflecting changes in the information about preceding or succeeding nodes in the metadata. In this way, by establishing the connection relationship between nodes with each function in the discovery of new drug candidates, a simulation flow suitable for optimal compound development can be created.

[0070] Figure 8 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0071] Referring to Fig. 8, in an example of generating a simulation flow, nodes (N81) to (N85) are illustrated. A node (N82) that automatically finds the optimal docking site follows a node (N81) that receives protein structure data in the form of a PDB file, and multiple nodes (N83, N84, N85) that analyze the actual binding energy (kcal / mol) and automatically align it can be connected in parallel to node (N82) as nodes following node (N82). In the discovery of new drug candidates, there are many cases where the results of a certain preceding task are reused to perform the next task multiple times. If the duplicated task is performed again each time, the cost required to perform the task (costs to be paid by the user and platform, computing resources, network resources, etc.) may be unnecessarily wasted. According to this embodiment, after detecting EAPDC from a protein structure file through node (N82) to determine a docking calculation site, the user can assign a desired drug library group to the active site of the corresponding protein using multiple nodes (N83, N84, N85). For example, by using multiple nodes (N83, N84, N85), various compound structure files such as a Zinc library, a natural product library, an FDA-approved drug library, and a compound structure file directly acquired by the customer can be assigned in parallel, enabling selective and efficient development of new drug candidates. Therefore, an efficient simulation flow can be created that repeats the work of analyzing and automatically aligning the actual binding energy (kcal / mol) under different environmental settings using the results of automatically finding the optimal docking site.

[0072] Figure 9 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0073] Referring to Fig. 9, in an example of generating a simulation flow, nodes (N91) to (N96) are illustrated. A node (N92) that automatically finds an optimal docking site follows a node (N91) that receives protein structure data in the form of a PDB file, and a node (N93) that automatically aligns the actual binding energy (kcal / mol) by analyzing it follows node (N92), and as nodes following node (N93), multiple nodes (N94, N95, N96) that perform comparative analysis by converting kcal / mol to kd / Ki (μM) can be connected in parallel to node (N93). According to this embodiment, a docking calculation site is determined by detecting EAPDC from a protein structure file through node (N92), and a user-desired drug library is input through node (N93) to obtain a compound result optimized for the active site. Then, a plurality of nodes (N94, N95, N96) can be used to process comparative analysis in parallel through various criteria based on the difference in binding affinity obtained up to node (N93). Therefore, an efficient simulation flow can be created that repeats the task of inputting a user-desired drug library to obtain a compound result optimized for the active site and converting kcal / mol to kd / Ki (μM) with different environmental settings to perform comparative analysis.

[0074] Figure 10 is a diagram showing an example of a simulation setting of a new drug candidate substance discovery system according to one embodiment.

[0075] Referring to Fig. 10, in an example of generating a simulation flow, nodes (N101) to (N111) are illustrated. Here, nodes (N101, N108) are nodes that input protein structure data in the form of a PDB file, and nodes (N102, N109) may be nodes that automatically find the optimal docking site. In addition, nodes (N103, N105, N110) may be nodes that analyze and automatically align actual binding energies (kcal / mol), and nodes (N104, N106, N111) may be nodes that convert kcal / mol to kd / Ki (μM) and perform comparative analysis. Node (N107) may be a node that compiles the results of new drug candidate discovery and transmits the compiled results to other nodes for further analysis.

[0076] According to the present embodiment, a first result of new drug candidate discovery according to a first simulation flow may be derived through nodes (N101, N102, N103, N104), and a second result of new drug candidate discovery according to a second simulation flow may be derived through nodes (N101, N102, N103, N104). Meanwhile, a third result of new drug candidate discovery may be derived through nodes (N108, N109, N110, N111), wherein node (N110) is connected to a plurality of nodes (N107, N109), and node (N107) is connected to a plurality of nodes (N104, N106). Here, node (N107) can receive first and second results as inputs from multiple nodes (N104, N106) and transmit them to node (N110), and node (N110) can automatically sort the results received from multiple nodes (N107, N109) by analyzing the actual binding energy (kcal / mol). Here, node (N107) can be named a "transfer node."

[0077] Conventional simulation methods require significant effort, time, and cost to perform complex simulations, as exemplified by the discovery of new drug candidates, which requires multiple trials using various methods. Furthermore, satisfactory simulation setup has been difficult. The graph computing-based simulation setup method described in the examples improves upon conventional methods. It allows for the intuitive and easy creation and management of complex simulation flows, as exemplified, while offering flexibility and convenience, allowing for easy modification. Furthermore, it enables the practical execution of highly complex and intricate simulation flows.

[0078] Figure 11 is a block diagram showing a new drug candidate discovery system according to one embodiment.

[0079] Referring to FIG. 11, a new drug candidate discovery system (2) according to one embodiment may include a project management module (10), a simulation management module (12), a simulation setting module (14), a simulation flow management module (16), and a token management module (18). With respect to the project management module (10), the simulation management module (12), the simulation setting module (14), and the simulation flow management module (16), unless otherwise described in this embodiment, reference may be made to or application of the descriptions of the previously described embodiments, and therefore, the token management module (18) will be primarily described herein.

[0080] The token management module (18) can deduct tokens that users must pay for detailed tasks provided by the new drug candidate discovery system (2), or recharge tokens when users pay using coupons or various payment methods. As previously explained, new drug candidate discovery is performed through several detailed tasks, including the fifth to ninth entities. However, in the past, there were no clear standards established for the costs that users had to pay for new drug candidate discovery by detailed task. Users paid the entire cost of new drug candidate discovery, not by detailed task. However, since the flow of detailed tasks in new drug candidate discovery varies depending on the discovery purpose and environment, as described above, excessive costs were sometimes incurred regardless of the number and workload of detailed tasks actually performed. Furthermore, it was difficult to reflect exceptional circumstances, such as when a detailed task was interrupted during execution, in the costs paid by the user.

[0081] The token management module (18) can determine the amount of tokens deducted for each detailed task for discovering new drug candidates. Specifically, the amount of tokens deducted can be determined by considering various factors related to the detailed task, such as the type of detailed task, the workload of the detailed task, and the difficulty of the detailed task. The token amount can be stored in a form readable by the computing device on a storage medium or cloud that the computing device can access. The user can use the tokens based on the criteria set according to the detailed task to be performed. In particular, as described above, by setting the connection relationship between nodes with each function in the discovery of new drug candidates, a simulation flow suitable for the development of an optimal compound can be created. The user can use the corresponding node by paying a token that is differentially applied to each node, and can pay only the amount of tokens needed for the node that is used.

[0082] In some embodiments, the token management module (18) may adjust the amount of tokens deducted to decrease linearly or non-linearly depending on the progress of the detailed task. Specifically, if a specific amount of tokens is defined for a specific detailed task, the token management module (18) may deduct tokens corresponding to that amount when the detailed task is completed, or deduct tokens corresponding to only a portion of the detailed task when only a portion of the detailed task is completed. For example, tokens may be deducted only as many as the number of ligands the user wishes to analyze, or tokens may be deducted only for the amount of work up to the point of completion if the user interrupts or an error occurs during analysis. Accordingly, while the discovery of new drug candidates generally requires a significant cost, the user may divide the cost by detailed task and bear it, or may bear only a portion of the cost when the detailed task is not completed.

[0083] In some embodiments, the token management module (18) may adjust the amount of tokens deducted to vary depending on the number of GPUs (Graphics Processing Units) the user wishes to use. Specifically, the token management module (18) may adjust the amount of tokens deducted to be greater when the user configures four GPUs to be used to run a node than when the user configures one GPU to be used. Accordingly, the user is guaranteed a choice between performance and cost, and the computing resources of the new drug candidate discovery system (2) can be efficiently operated.

[0084] FIG. 12 is a block diagram illustrating a computing device according to one embodiment.

[0085] Referring to FIG. 12, a new drug candidate substance discovery system according to embodiments can be implemented using a computing device (50).

[0086] The computing device (50) may include at least one of a processor (501), a memory (502), a storage device (503), a display device (504) communicating via a bus (509), a network interface device (505) providing a connection to a network (40) for communication with other entities, and an input / output interface device (506) providing a user input interface or a user output interface. Of course, the computing device (50) may additionally include any electronic device necessary to implement the technical ideas described herein, although not illustrated in FIG. 12.

[0087] The processor (501) may be implemented in various types such as an Application Processor (AP), a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), a Neural Processing Unit (NPU), etc., and may be any electronic device that executes a program or command stored in a memory (502) or a storage device (503). In particular, the processor (501) may be configured to implement the functions or methods described above with respect to FIGS. 1 to 11, and in relation to the new drug candidate substance discovery system according to embodiments of the present invention, artificial intelligence-specific calculations may be processed on a GPU or NPU.

[0088] The memory (502) and the storage device (503) may include various types of volatile or non-volatile storage media. For example, the memory (502) may include a read-only memory (ROM) or a random access memory (RAM), and the memory (502) may be located inside or outside the processor (501) and may be connected to the processor (501) through various known means. Meanwhile, examples of the storage device (503) include a hard disk drive (HDD) or a solid state drive (SSD), and the scope of the present invention is not limited to the elements listed above for explanation.

[0089] The new drug candidate substance discovery system according to embodiments of the present invention may be implemented as a program or software running on a computing device (50), and such a program or software may be stored on a computer-readable medium.

[0090] Meanwhile, the new drug candidate substance discovery system according to embodiments of the present invention may be implemented using hardware of a computing device (50), or may be implemented as separate hardware that can be electrically connected to the computing device (50).

[0091] According to the embodiments of the present invention described so far, functions and a user interface optimized for the discovery of new drug candidates are provided, and the problem of difficulties in data sharing and consistent data management due to the detailed tasks related to the discovery of new drug candidates being provided as separate tools with high incompatibility with each other in the past is improved, and the simulation flow can be easily managed by creating, changing, and deleting nodes.

[0092] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by those skilled in the art to which the present invention pertains using the basic concept of the present invention defined in the following claims also fall within the scope of the present invention.

Claims

1. A new drug candidate discovery system implemented as a platform that provides users with functions or services necessary for discovering new drug candidates in the form of a web service. A project management module that creates projects where you can add tasks to carry out the discovery of new drug candidates; A simulation management module that creates a simulation desired by the user on the generated project; A simulation setting module that sets the simulation flow for the above simulation; and In the above simulation flow setting, a simulation flow management module is included that manages information about nodes that can precede or follow. New drug candidate discovery system.

2. In paragraph 1, The above simulation setting module displays a simulation setting area to the user through a display device of a computing device on which the new drug candidate substance discovery system is operated. The above simulation settings area includes a canvas area, The above simulation setting area is a new drug candidate discovery system that provides a user with the ability to place and connect multiple work modules in the canvas area based on graph computing.

3. In paragraph 2, The above simulation setting area includes a protein structure data input area and a task module selection area, A new drug candidate discovery system, wherein the protein structure data input area and the work module selection area include one or more possible objects that can be changed into nodes by dragging and dropping them into the canvas area.

4. In paragraph 3, The above protein structure data input area includes one or more entities related to the function of uploading protein structure data, A new drug candidate discovery system, wherein the one or more objects can be converted into one of a first node having a function of receiving protein structure data in the form of a PDB (Protein Data Bank) file from a user, a second node having a function of receiving protein structure data in the form of a PDB code from a user, a third node having a function of receiving protein structure data in the form of a protein sequence file from a user, and a fourth node having a function of receiving protein structure data in the form of a protein sequence from a user by dragging and dropping them onto the canvas area.

5. In paragraph 4, The above task module selection area includes one or more entities related to the function of performing detailed tasks related to the discovery of new drug candidates based on the uploaded protein structure data. A new drug candidate discovery system, wherein the one or more objects can be converted into one of a fifth node having a function of finding an optimal docking site in a target protein structure by dragging and dropping them onto the canvas area, a sixth node having a function of predicting an amino acid sequence into a protein 3-dimensional structure, a seventh node having a function of analyzing and aligning actual binding energy (kcal / mol) and providing it to a user, and an eighth node having a function of converting kcal / mol into kd / Ki (μM) for a selected ligand and performing comparative analysis.

6. In paragraph 5, Nodes that can precede the fifth node include the first node, the second node, the third node, the fourth node, and the sixth node, A new drug candidate discovery system, wherein a node that can follow the fifth node includes the seventh node.

7. In paragraph 5, Nodes that can precede the sixth node include the third node, the fourth node, and the sixth node, A new drug candidate discovery system, wherein the node that can follow the sixth node includes the fifth node.

8. In paragraph 5, The nodes preceding the seventh node include the fifth node, A new drug candidate discovery system, wherein a node that can follow the seventh node includes the eighth node.

9. In paragraph 5, A new drug candidate discovery system, wherein, when a first result is derived according to a first simulation flow generated from the first node to the eighth node, and a second result is derived according to a second simulation flow generated differently from the first simulation flow from the first node to the eighth node, a transfer node is arranged in the canvas area to receive the first result and the second result and transfer them to a subsequent node.

10. In paragraph 2, The above simulation settings area includes an external module provision area, A new drug candidate discovery system, wherein the external module provision area includes one or more objects that are changed into nodes having arbitrary functions provided externally by dragging and dropping them onto the canvas area.

11. In paragraph 3, The node placed in the above canvas area displays a node connection shape, A new drug candidate discovery system in which, while a user clicks on a node connection shape displayed on the right side of a node, the color or shape of a node connection shape displayed on the left side of a node that can be connected among other nodes changes and is displayed.

12. In paragraph 11, The above simulation flow management module is a new drug candidate discovery system that manages information for determining whether a connection between the nodes is possible through metadata.

13. In paragraph 1, A new drug candidate discovery system further comprising a token management module for deducting tokens that a user must pay for detailed tasks provided by the new drug candidate discovery system.

14. In paragraph 13, The above token management module determines the amount of tokens to be deducted for each detailed task for discovering new drug candidates. A new drug candidate discovery system in which the amount of tokens to be deducted is determined by considering multiple factors including the type of detailed task, the workload of the detailed task, and the difficulty of the detailed task.

15. In paragraph 13, The above token management module is a new drug candidate discovery system that adjusts the amount of tokens deducted to decrease linearly or non-linearly according to the progress of detailed work.

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