Answer generation method and system

Abstract

This invention relates to a method and system for generating answers. More specifically, this invention relates to a method and system for generating answers using a super-large-scale infrastructure model. This invention relates to an answer generation platform based on a super-large-scale infrastructure model. Furthermore, this invention relates to a chemical reaction prediction system and a method for controlling the same, and a method for learning a chemical reaction prediction system. More specifically, this invention relates to a chemical reaction prediction system that performs forward reaction prediction based on electron flow, a method for controlling the same, and a method for learning a chemical reaction prediction system.

Description

Technical Field

[0001] The present invention relates to an answer generation method and system, and provides an answer generation method and system using a generation model or a base model. The present invention relates to an answer generation platform based on an ultra-large-scale base model. Further, the present invention relates to a chemical reaction prediction system and its control method, and a learning method of the chemical reaction prediction system. More specifically, the present invention relates to a chemical reaction prediction system that performs forward reaction prediction based on an electron flow and its control method, and a learning method of the chemical reaction prediction system.

Background Art

[0002] Recently, with the development of artificial intelligence, especially deep learning that extracts data characteristics using a deep neural network structure, the number of cases achieving excellent results in various fields such as speech recognition, image recognition, natural language processing, and autonomous driving has been rapidly increasing.

[0003] With the development of such deep learning technology, recently, generative artificial intelligence (Generative AI) technology has been attracting attention. More specifically, a generative artificial intelligence model can generate new data in various forms such as text, images, and voices from given data, which provides a different level of application potential from simply classifying or predicting existing data. That is, it has become possible to automatically generate articles, images, voices, etc., which were previously generated by humans, by utilizing a generative artificial intelligence model, and services using generative artificial intelligence (e.g., ChatGPT) show initiative and accuracy differentiated from existing chatbot services and have attracted great attention worldwide.

[0004] Meanwhile, efforts to solve various scientific problems in the fields of natural science (e.g., physics, chemistry, biology, etc.) continue. For example, research to design new materials or develop new drugs is actively being conducted, and such research plays an important role in future technological development and industrial innovation. In this context, organic synthesis is one of the important challenges in new drug development and materials science, and predicting the results of chemical reactions is crucial in designing new molecules. However, it is time-consuming and costly for researchers to directly perform chemical synthesis, such as molecular synthesis.

[0005] Therefore, research is actively being conducted on methods to improve the efficiency of natural science research based on generative artificial intelligence. [Overview of the project] [Problems that the invention aims to solve]

[0006] This invention aims to provide a method and system for generating answers that can propose optimal research methods for researchers in the field of natural science.

[0007] More specifically, the present invention aims to provide a method and system for generating answers that minimize the risk of failure in natural science research and enhance the efficiency of natural science research. Furthermore, the present invention aims to solve the problems of time and cost associated with the research and development of materials, and to provide a method and system for generating answers that improve the efficiency of material research and development.

[0008] Furthermore, the present invention aims to provide a chemical reaction prediction model that can understand chemical reaction mechanisms and predict the results of various types of chemical reactions. More specifically, the present invention provides a chemical reaction prediction model based on electron flow using graph diffusion, in which both the input and output structures are formed in graph space.

[0009] Furthermore, the present invention provides a method for learning a chemical reaction prediction model that enables a deeper understanding of chemical reaction mechanisms and the generation of more accurate and interpretable chemical reaction prediction results. [Means for solving the problem]

[0010] A method according to the present invention, in which a memory and at least one processor cooperate to perform a prediction, may include the steps of: identifying a plurality of molecular structures to be predicted based on user input received via a service page; storing information about the plurality of molecular structures in the memory; obtaining a molecular graph of the plurality of molecular structures by converting atoms into nodes and interatomic bonds into edges based on the plurality of molecular structures stored in the memory; receiving a user query via the service page for a prediction of a chemical reaction related to the plurality of molecular structures; processing the molecular graph of the plurality of molecular structures as input to a chemical reaction prediction model so that a chemical reaction corresponding to the user query is predicted; performing a chemical reaction prediction for the plurality of molecular structures using the chemical reaction prediction model; obtaining a chemical reaction prediction result for the plurality of molecular structures from the chemical reaction prediction model; and generating an answer to the user query using the chemical reaction prediction result.

[0011] In one embodiment, the chemical reaction prediction model includes a first module that takes molecular structures as input and predicts chemical reactions based on a graph, and a second module that analyzes information relating to the chemical reaction predictions of the plurality of molecular structures from text data, wherein the step of performing the chemical reaction prediction is characterized in that the prediction results of the first module and the analysis results of the second module are used to generate the result product of the chemical reaction prediction. In this embodiment, the step of predicting the chemical reaction is characterized by verifying the predicted result obtained through the first module using the output data analyzed by the second module.

[0012] In this embodiment, the plurality of molecular structures include a first molecular structure and a second molecular structure, and the step of obtaining a molecular graph relating to the plurality of molecular structures is characterized in that a first molecular graph including nodes and edges corresponding to the first molecular structure is obtained by converting the atoms constituting the first molecular structure into nodes and the bonding relationships between the atoms constituting the first molecular structure into edges, and a second molecular graph including nodes and edges corresponding to the second molecular structure is obtained by converting the atoms constituting the second molecular structure into nodes and the bonding relationships between the atoms constituting the second molecular structure into edges. In this embodiment, the method further includes the steps of: labeling each of the plurality of molecular structures so that each of them is assigned a different label; and storing in the memory the first molecular graph obtained through the process of obtaining the molecular graph and information relating to the labels assigned to the first molecular graph, and the second molecular graph obtained through the process of obtaining the molecular graph and information relating to the labels assigned to the second molecular graph.

[0013] In one embodiment, when a user query is received that includes the plurality of molecular structures each having different labels, the step of generating the response involves processing the plurality of molecular structures each having different labels as input to the chemical reaction prediction model, generating a response to the user query using the chemical reaction prediction results obtained from the chemical reaction prediction model, and if the response to the user query includes a specific molecular structure generated by the chemical reaction prediction model, a label is assigned to that specific molecular structure. In one embodiment, the method further includes the step of providing a plurality of graphic objects corresponding to each of the plurality of molecular structures, each of which is assigned a different label, to a region of the service page where the user query is received, wherein each of the plurality of graphic objects includes the first molecular graph and the second molecular graph.

[0014] In one embodiment, the service page is provided with detailed information corresponding to the graphic object selected by the user input, based on the reception of user input selecting one of the plurality of graphic objects. In the embodiment, the step of predicting chemical reactions relating to the plurality of molecular structures is characterized by obtaining an embedding vector corresponding to the molecular graph using information about the plurality of molecular structures, performing an attention calculation related to the interaction between atoms of the plurality of molecular structures, updating the embedding vector based on the calculation, performing bond prediction and atom prediction predicted as chemical reactions of the plurality of molecular structures using the updated embedding vector, and obtaining the chemical reaction prediction result predicted from the chemical reactions of the plurality of molecular structures using the bond prediction result and the atom prediction result.

[0015] In one embodiment, the updated embedding vector is characterized in that the attention score calculated based on the result of the attention calculation is updated by adding different biases to it, depending on the type of connection between the nodes constituting the first molecular graph and the second molecular graph. In the embodiment, the bond prediction is performed by performing a dot-product operation using the updated embedding vector, the atom prediction is performed by predicting the atomic properties of the atom corresponding to the updated embedding vector using the atomic property probability distribution of each atom corresponding to the updated embedding vector, and the atomic properties include the charge state of the atom which can change during the chemical reaction process of the plurality of molecular structures.

[0016] In this embodiment, the predicted chemical reaction result corresponds to the final result stabilized through a diffusion feedback process. In the embodiment, the service page includes at least one of the following: a first area where a plurality of graphic objects corresponding to each of the plurality of molecular structures, each of which is assigned a different label, and detailed information relating to the plurality of molecular structures; a second area where answers to user queries are provided; and a third area where user queries are received. The detailed information relating to the plurality of molecular structures includes at least one of the molecular structure image, name, physical properties, and string in SMILES notation for each of the plurality of molecular structures. The detailed information is extracted from user input or obtained from at least one pre-trained predictive model, the pre-trained predictive model including at least one of a chemical reaction prediction model that predicts chemical reactions between molecular structures and a molecular property prediction model that predicts the physical properties of molecular structures.

[0017] In this embodiment, the method further includes the steps of receiving editing requests for the plurality of molecular structures via a service page that provides answers to user queries, and providing the service page with an editing interface that provides editing functionality for the plurality of molecular structures. In one embodiment, the editing interface is provided with a molecular graph in an editable state that corresponds to at least one of the first molecular structure and the second molecular structure obtained by the molecular graph conversion.

[0018] In this embodiment, the editing of the plurality of molecular structures is characterized by the deletion or repositioning of at least one of the nodes corresponding to each atom constituting each of the plurality of molecular structures and the edges representing the bonding relationships of the atoms, or the addition of a new node corresponding to a new atom, or the addition of a new edge that generates a new bonding relationship to the atom. In this embodiment, the edited molecular structure among the plurality of molecular structures is stored in the memory, the edited molecular structure is assigned a new label to identify the edited molecular structure, and when a user query including the new label is input to the ultra-large-scale infrastructure model, the ultra-large-scale infrastructure model generates a response using the edited molecular structure corresponding to the new label.

[0019] The answer generation system according to the present invention includes a memory and at least one processor, wherein the memory and the processor cooperate to identify a plurality of molecular structures to be predicted based on user input received via a service page, store information about the plurality of molecular structures in the memory, obtain a molecular graph of the plurality of molecular structures by converting atoms into nodes and interatomic bonds into edges based on the plurality of molecular structures stored in the memory, receive a user query for chemical reaction prediction related to the plurality of molecular structures via the service page, process the molecular graph of the plurality of molecular structures as input to a chemical reaction prediction model so that a chemical reaction corresponding to the user query is predicted, perform a chemical reaction prediction for the plurality of molecular structures using the chemical reaction prediction model, obtain a chemical reaction prediction result for the plurality of molecular structures from the chemical reaction prediction model, and generate an answer to the user query using the chemical reaction prediction result. A program according to the present invention, which is executed by one or more processes in an electronic device and stored on a computer-readable medium, may include instructions that cause the program to perform the following steps based on user input received via a service page: identify a plurality of molecular structures to be predicted; store information about the plurality of molecular structures in memory; obtain a molecular graph of the plurality of molecular structures by converting atoms into nodes and interatomic bonds into edges based on the plurality of molecular structures stored in memory; receive a user query for chemical reaction prediction related to the plurality of molecular structures via the service page; process the molecular graph of the plurality of molecular structures as input to a chemical reaction prediction model so that a chemical reaction corresponding to the user query is predicted; perform a chemical reaction prediction for the plurality of molecular structures using the chemical reaction prediction model; obtain a chemical reaction prediction result for the plurality of molecular structures from the chemical reaction prediction model; and generate an answer to the user query using the chemical reaction prediction result.

[0020] A chemical reaction prediction method performed cooperatively by a memory and processor according to the present invention may include the steps of: receiving information on a plurality of molecular structures as input to an encoder; obtaining an embedding vector corresponding to the plurality of molecular structures using the information on the plurality of molecular structures in the embedding layer of the encoder; performing an attention calculation related to the interaction between atoms of the plurality of molecular structures in a multi-head self-attention layer and updating the embedding vector based on the calculation; storing the updated embedding vector in the update step in memory and inputting the updated embedding vector stored in memory to a decoder; performing bond prediction and atom prediction using the updated embedding vector in the decoder, which are predicted as chemical reactions of the plurality of molecular structures; and obtaining a final chemical reaction result predicted from the chemical reactions of the plurality of molecular structures using the results of the bond prediction and atom prediction. In an embodiment, the step of obtaining the chemical reaction result comprises the steps of: sampling the initialization reaction result using the bond prediction result and the atom prediction result; stabilizing the sampled initialization reaction result through a diffusion feedback process; and obtaining the final chemical reaction result stabilized through the diffusion feedback process.

[0021] In an embodiment, based on the plurality of molecular structures, the method further includes obtaining a molecular graph related to the plurality of molecular structures by converting atoms into nodes and bonds between atoms into edges. The plurality of molecular structures includes a first molecular structure and the second molecular structure. The step of obtaining the molecular graph includes using a pre-specified graph conversion algorithm to convert the atoms constituting the first molecular structure into nodes and convert the bond relationships between the atoms constituting the first molecular structure into edges, thereby obtaining a first molecular graph including nodes and edges corresponding to the first molecular structure; and using the pre-specified graph conversion algorithm to convert the atoms constituting the second molecular structure into nodes and convert the bond relationships between the atoms constituting the second molecular structure into edges, thereby obtaining the second molecular graph including nodes and edges corresponding to the second molecular structure. In an embodiment, the information related to the plurality of molecular structures includes information related to the nodes and edges corresponding to the first molecular structure and information related to the nodes and edges corresponding to the second molecular structure. The embedding vector includes at least one of information related to the atom type (Type), atomic charge (Charge), number of hydrogens (Hydrogen), number of radical electrons (Radical), and degree of the nodes corresponding to the first molecular structure and the nodes corresponding to the second molecular structure.

[0022] In an embodiment, in the step of updating the embedding vector, different biases are added to the attention scores calculated in the multi-head self-attention layer according to the bond types between the nodes constituting the first molecular graph and the second molecular graph. In an embodiment, the bond types include a single bond type, a double bond type, a triple bond type, and an aromatic bond type.

[0023] In an embodiment, at least one of an adjacency matrix, a bond type matrix, a shortest paths matrix, and K-hop neighbors corresponding to each of the first molecular graph and the second molecular graph is extracted by using nodes and edges corresponding to the first molecular graph and nodes and edges corresponding to the second molecular graph, wherein the adjacency matrix includes information on direct connections between nodes constituting the first molecular graph and the second molecular graph, the bond type matrix includes information on bond types between nodes constituting the first molecular graph and the second molecular graph, the shortest paths matrix includes information on shortest path lengths between nodes constituting the first molecular graph and the second molecular graph, and the K-hop neighbors include information on neighboring nodes within K hops for each node constituting the first molecular graph and the second molecular graph. In an embodiment, in the step of updating the embedding vector, an output vector of the multi-head self-attention layer is input into a feed-forward neural network layer, and in the feed-forward neural network layer, at least one of the adjacency matrix, the bond type matrix, the shortest paths matrix, and the K-hop neighbors is used to update the output vector of the multi-head self-attention layer, and an output vector of the feed-forward neural network layer is specified as the updated embedding vector.

[0024] In this embodiment, the bond prediction is performed by executing a dot product using the updated embedding vectors, and the dot product is performed for each pair of atoms corresponding to the updated embedding vectors. In the embodiment, the step of performing bond prediction includes obtaining dot product values ​​for each of the plurality of bond types for each of the atom pairs based on the dot product calculation, wherein the plurality of bond types are associated with at least one of single bond, double bond, bond formation, bond collapse, and no change.

[0025] In this embodiment, the step of performing bond prediction further includes the steps of generating a probability distribution for each of the plurality of bond types for each of the atom pairs using the dot product value obtained by the dot product calculation, and obtaining a transformation matrix that predicts the change in the bond state of each of the atom pairs using the probability distribution. In the embodiment, the step of generating the probability distribution is characterized by applying a softmax function to the dot product value obtained for each of the plurality of bond types for each of the atom pairs, thereby generating the probability distribution of the plurality of bond types for each of the atom pairs.

[0026] In the embodiment, the step of performing atomic prediction further includes the steps of generating an atomic property probability distribution for each atom corresponding to the updated embedding vector using a softmax output layer, and predicting the atomic property of the atom corresponding to the updated embedding vector using the probability distribution, wherein the atomic property includes the charge state of the atom which can change during the chemical reaction process of the plurality of molecular structures. In the embodiment, the diffusion feedback process is characterized by repeatedly evaluating each bond transformation of the initialization reaction result and removing or modifying unstable bonds.

[0027] In the embodiment, the diffusion feedback process is characterized by using a transformation probability matrix and a target transformation matrix to predict changes in the binding state of the initialization reaction result, evaluating the predicted binding transformation at each of the repeatedly performed steps, and generating the final chemical reaction result using an interpolation factor. A method for predicting a chemical reaction, in which a memory and a processor cooperate, according to the present invention, comprising the steps of: receiving information on a plurality of molecular structures as input to an encoder; obtaining a molecular graph using atoms as nodes and bonds as edges based on the plurality of molecular structures; obtaining an embedding vector corresponding to the molecular graph using the information on the plurality of molecular structures in the embedding layer of the encoder; performing an attention calculation related to the interaction between atoms of the plurality of molecular structures in a multi-head self-attention layer and updating the embedding vector based on the calculation; storing the updated embedding vector in the update step in memory and inputting the updated embedding vector stored in memory to a decoder; and using the updated embedding vector in the decoder, predicting bond prediction and atom prediction as chemical reactions of the plurality of molecular structures. The steps may include: performing prediction; obtaining a final chemical reaction product predicted from the chemical reactions of the plurality of molecular structures using the results of the bond prediction and the results of the atom prediction; calculating a loss function between the final chemical reaction product and label data including the actual bond states and atomic states corresponding to the plurality of molecular structures; and optimizing the parameters of at least one of the encoder and the decoder to minimize the loss function.

[0028] A chemical reaction prediction system according to the present invention, comprising a memory, an encoder, a decoder, and at least one processor, wherein the encoder receives information on a plurality of molecular structures, obtains a molecular graph based on the plurality of molecular structures using atoms as nodes and bonds as edges, obtains an embedding vector corresponding to the molecular graph in the embedding layer of the encoder, performs an attention calculation related to the interaction between atoms of the plurality of molecular structures in the multi-head self-attention layer of the encoder, updates the embedding vector based on the calculation, the processor stores the updated embedding vector in the update step in memory, inputs the updated embedding vector stored in memory to the decoder, the decoder uses the updated embedding vector to perform bond prediction and atom prediction, which are predicted as chemical reactions of the plurality of molecular structures, and obtains the final chemical reaction result predicted from the chemical reactions of the plurality of molecular structures using the results of the bond prediction and atom prediction. A program, which is executed by one or more processes in an electronic device according to the present invention and stored on a computer-readable medium, may include instructions to cause an encoder to perform the following steps: receiving information about a plurality of molecular structures as input; obtaining a molecular graph based on the plurality of molecular structures using atoms as nodes and bonds as edges; obtaining an embedding vector corresponding to the molecular graph in the embedding layer of the encoder; performing an attention calculation related to the interaction between atoms of the plurality of molecular structures in a multi-head self-attention layer and updating the embedding vector based on the calculation; storing the updated embedding vector in memory and inputting the updated embedding vector stored in memory to a decoder; performing bond prediction and atom prediction, which are predicted as chemical reactions of the plurality of molecular structures, using the updated embedding vector in the decoder; and obtaining the final chemical reaction result predicted from the chemical reactions of the plurality of molecular structures using the results of the bond prediction and atom prediction. [Effects of the Invention]

[0029] As described above, the response generation method and system according to the present invention can generate and provide responses suitable for user queries based on data extracted from documents, thereby proposing the optimal research method to the user and minimizing the risk of research failure. Furthermore, the response generation method and system according to the present invention can provide answers to user queries using data extracted from documents or generated from a pre-trained predictive model. This allows for the rapid and accurate provision of necessary information to users, thereby reducing the time and cost associated with research and / or development.

[0030] Furthermore, according to the answer generation method and system of the present invention, it is possible to generate answers to user queries using results predicted from a pre-trained predictive model and provide the generated answers to the user. This allows the user to reduce the time spent on research and / or development and to reduce the number of trial-and-error iterations in research and / or development. Furthermore, according to the answer generation method and system of the present invention, by visualizing and providing the extracted molecular structure and related data through a user interface, users can intuitively recognize and understand the necessary information more quickly, thereby improving the accuracy and efficiency of their research.

[0031] On the other hand, according to the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system according to the present invention, by modeling electron transfer through the graph structure of molecules, it is possible to deeply understand the chemical reaction mechanism and accurately predict the chemical reaction results. Furthermore, the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system according to the present invention provide a chemical reaction prediction model that can deeply understand the chemical reaction mechanism and accurately predict the chemical reaction results, thereby reducing the time and cost of experiments for users. This reduces research and development costs and shortens the time to market for new products.

[0032] On the other hand, according to the chemical reaction prediction system, its control method, and learning method for the chemical reaction prediction system of the present invention, by performing both the input of molecular structures and the output for predicted results in graph space, forward reaction prediction can be performed regardless of the permutation and order of SMILES. Furthermore, according to the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system of the present invention, it is possible to ensure consistency between transformations by enabling simultaneous sampling of multiple interdependent transformations that occur in parallel within the molecular graph.

[0033] Furthermore, according to the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system of the present invention, in order to solve problems that may arise due to symmetrical structures, it is possible to break the symmetry by including noise or a sampling mechanism and form an effective output structure, thereby preventing the occurrence of ineffective configurations. [Brief explanation of the drawing]

[0034] [Figure 1] This is a conceptual diagram illustrating a response generation system to which the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system according to the present invention are applied. [Figure 2] [a] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [b] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [c] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [d] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [e] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [f] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [Figure 3] This is a flowchart illustrating the learning method for the chemical reaction prediction model according to the present invention. [Figure 4] [a] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [b] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [c] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [d] A conceptual diagram illustrating the chemical reaction prediction model according to the present invention. [Figure 5][a] A conceptual diagram illustrating an example of application in a response system to which the chemical reaction prediction model according to the present invention is applied. [b] A conceptual diagram illustrating an example of application in a response system to which the chemical reaction prediction model according to the present invention is applied. [c] A conceptual diagram illustrating an example of application in a response system to which the chemical reaction prediction model according to the present invention is applied. [d] A conceptual diagram illustrating an example of application in a response system to which the chemical reaction prediction model according to the present invention is applied. [e] A conceptual diagram illustrating an example of application in a response system to which the chemical reaction prediction model according to the present invention is applied. [Figure 6] This is a conceptual diagram illustrating an example of the application of the chemical reaction prediction model according to the present invention in a response system. [Figure 7] This is a conceptual diagram illustrating an example of the application of the chemical reaction prediction model according to the present invention in a response system. [Figure 8] This is a conceptual diagram illustrating the ultra-large-scale infrastructure model according to the present invention. [Figure 9] This is a conceptual diagram illustrating the ultra-large-scale infrastructure model according to the present invention. [Figure 10] This is a flowchart illustrating the answer generation method according to the present invention. [Figure 11] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 12] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 13] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 14] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 15] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 16] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 17] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 18]This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 19] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 20] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 21] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 22] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 23] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 24] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 25] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 26] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 27] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 28] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 29] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 30] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 31] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Figure 32] This is a conceptual diagram illustrating the answer generation method according to the present invention. [Modes for carrying out the invention]

[0035] The embodiments disclosed herein will be described in detail below with reference to the accompanying drawings, but regardless of the reference numerals used in the drawings, identical or similar components will be given the same reference numerals, and redundant descriptions thereof will be omitted. The suffixes “module” and “part” used for components in the following description are added or mixed for the sake of ease of writing the specification and do not have any distinguishing meaning or role in themselves. Furthermore, when describing the embodiments disclosed herein, if it is determined that a detailed description of the relevant prior art would obscure the gist of the embodiments disclosed herein, such detailed description will be omitted. In addition, the accompanying drawings are intended solely to facilitate the understanding of the embodiments disclosed herein, and the technical ideas disclosed herein should not be limited by the accompanying drawings and should be understood to include all modifications, equivalents and substitutions that fall within the concept and technical scope of the present invention. Terms including ordinal numbers such as "1st," "2nd," etc., may be used to describe various components, but the components are not limited to those defined by these terms. These terms are used solely to distinguish one component from another.

[0036] When it is stated that one component is “connected” or “linked” to another component, it should be understood that it may be directly connected or linked to the other component, but there may also be another component between them. On the other hand, when it is stated that one component is “directly connected” or “directly linked” to another component, it should be understood that there is no other component between them. A singular expression includes plural forms unless otherwise clearly indicated in the context.

[0037] In this application, terms such as “includes” or “having” are intended to specify the presence of features, figures, steps, actions, components, parts, or combinations thereof as described in the specification, and should be understood not to preemptively exclude the possibility of the presence or addition of one or more other features, figures, steps, actions, components, parts, or combinations thereof. The present invention relates to a method and system for generating answers. The answer generation system according to the present invention generates answers based on generative artificial intelligence (Generative AI) or a foundation model, and is also called an answer generation platform based on a super-large-scale foundation model. However, "super-large-scale foundation model" is also called a generative model, foundation model, or Large Language Model (LLM). The answer generation system according to the present invention may be a system that generates prediction results for physical properties of molecular structures or a system that designs molecules having properties desired by the user. Furthermore, the answer generation system according to the present invention may be a system that generates prediction results for chemical reactions between new types of molecules and / or multiple molecules. In addition, the answer generation system according to the present invention may be a system that generates prediction results for the deformation of existing materials and the synthesis of various materials (e.g., new materials, polymer materials, nanomaterials, composite materials, organic materials, pharmaceutical materials, etc.).

[0038] The answer generation system according to the present invention includes an ultra-large-scale foundational model (or ultra-large-scale foundational artificial intelligence model), and the present invention aims to improve the efficiency of natural science research by minimizing the risk of failure in research. The following will be discussed in more detail with the attached drawings. Figure 1 is a conceptual diagram illustrating the chemical reaction prediction system and its control method, as well as the learning method for the chemical reaction prediction system, according to the present invention, and the answer generation system to which these are applied. Figures 2a, 2b, 2c, 2d, 2e, and 2f are conceptual diagrams illustrating the chemical reaction prediction model according to the present invention. Figure 3 is a flowchart illustrating the learning method for the chemical reaction prediction model according to the present invention. Figures 4a, 4b, 4c, and 4d are conceptual diagrams illustrating the chemical reaction prediction model according to the present invention. Figures 5a, 5b, 5c, 5d, 5e, 6, and 7 are conceptual diagrams illustrating examples of applications in an answer system to which the chemical reaction prediction model according to the present invention is applied. Figures 8 and 9 are conceptual diagrams illustrating the ultra-large-scale infrastructure model according to the present invention, and Figure 10 is a flowchart illustrating the answer generation method according to the present invention. Figures 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, and 32 are conceptual diagrams illustrating the answer generation method according to the present invention.

[0039] On the other hand, as shown in Figure 1, the response generation system 100 may include at least one of the following: an input unit 110, an output unit 120, a communication unit 130, a storage unit 140, and a super-large-scale foundational model 200. Here, the super-large-scale foundational model 200 is also called a foundational model, and the foundational model may mean a super-large-scale AI core foundational model trained on a massive dataset. Although not shown in the figures, the answer generation system 100 may include one or more processors, such processors may include one or more general-purpose processors and / or one or more special-purpose processors (e.g., digital signal processors, tensor processing units (TPUs), graphics processing units (GPUs), neural network processing units (NPUs), application-specific integrated circuits, application-specific semiconductors (ASICs), etc.). One or more processors may be configured to execute instruction words, computer-readable instructions, and / or other instructions described herein that are stored (or included) in the memory unit 140. Such an answer generation system and method can perform data processing described later in cooperation with the memory and at least one processor. The processor can perform a series of operations and data processing using the data and information stored in the memory. In this case, the memory may be configured as the memory unit 140.

[0040] On the other hand, the input unit 110 is a means of data input and may be configured in various ways. For example, the input unit 110 may be configured to receive user input. The input unit 110 may be configured to receive user input from the user terminal 10. Here, "receiving input" may mean receiving an input signal (or selection signal) corresponding to the user input, based on the user input being made through the configuration of the input unit provided in the user terminal 10. The input unit 110 is also called a user interface module. The input unit 110 may include a touchscreen, computer mouse, keyboard, keypad, touchpad, trackball, joystick, voice recognition module, or other similar device. However, the present invention does not limit the type of input unit 110. Furthermore, in the present invention, the input unit 110 does not necessarily mean hardware means, but can be understood as a channel for receiving input from the user.

[0041] Here, user input may include documents, text, images (or videos), audio, etc. In this case, the response generation system 100 may further include a module that converts audio to text. Next, the output unit 120 can output information through the configuration of an output unit (e.g., display unit, touchscreen, speaker, etc.) provided on the user terminal 10, which is linked to the answer generation system 100. For example, the output unit 120 can output a page 1000 (or service page) linked to the answer generation system 100 to the display unit of the user terminal 10. Furthermore, the output unit 120 does not necessarily refer to hardware means, but can be understood as a channel for outputting results to the user.

[0042] The communication unit 130 may then be connected wirelessly or via a wired network to the user terminal 10, servers (e.g., a central server, an external server, etc.), devices, and at least one network, and may be configured to receive or transmit overall data and information necessary for the operation of the response generation system 100. Here, the user terminal 10 may include at least one of the following: mobile phone, smartphone, notebook computer, laptop computer, slate PC, tablet PC, ultrabook, desktop computer, digital broadcasting terminal, PDA (personal digital assistant), PMP (portable multimedia player), navigation system, and wearable device (e.g., smartwatch, smart glass, HMD (head mounted display)).

[0043] Furthermore, the communication unit 130 can support various communication methods depending on the communication standard of the device it communicates with. For example, the communication unit 130 supports WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G (5th Generation Mobile Telecommunication), and Bluetooth (registered trademark). TM It may be configured to communicate with a target using at least one of the following technologies: RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi Direct, or Wireless USB (Wireless Universal Serial Bus).

[0044] On the other hand, the storage unit 140 plays a role in storing various data related to the present invention and may include one or more non-temporary computer-readable storage media that can be read and / or accessed by at least one of one or more processors. One or more computer-readable storage media may include volatile and / or non-volatile storage components such as optical, magnetic, organic, or other memory or disk storage devices. In some examples, the storage unit 140 can be embodied using a single physical device (e.g., one optical, magnetic, organic, or other memory or disk storage device), while in other examples, the storage unit 140 can be embodied using two or more physical devices.

[0045] The storage unit 140 may include computer-readable instructions and additional data. The storage unit 140 may include storage necessary to perform at least some of the methods, scenarios and techniques described herein and / or at least some of the functions of the apparatus and network. Furthermore, at least a portion of the storage unit 140 may be cloud storage or a cloud server. The storage unit 140 may store at least a portion of the data corresponding to user input received from the input unit 110 and the training data.

[0046] In other words, the memory unit 140 only needs to be a space where the information necessary for the operation of the answer generation system 100 is stored, and it can be understood that there are no physical space constraints. On the other hand, the ultra-large-scale base model 200 may be configured to generate prediction results for physical properties of molecular structures or to design molecules with properties desired by the user. Furthermore, the ultra-large-scale base model 200 can generate prediction results for new types of molecules and / or chemical reactions between multiple molecules, or generate prediction results for the deformation of existing materials and the synthesis of various materials (e.g., new materials, polymer materials, nanomaterials, composite materials, organic materials, pharmaceutical materials, etc.).

[0047] In this regard, the ultra-large-scale foundational model 200 may include at least one of the following: a document understanding model 300, a chemical reaction prediction model 400, and a molecular property prediction model 500. The Document Understanding model 300 can extract various forms of content from documents (e.g., papers, books, patent documents, reports, etc.) that meet predefined content criteria. More specifically, the Document Understanding model 300 may be a model trained to understand structured data, unstructured data, linguistic data (or linguistic elements), and non-linguistic data (or non-linguistic elements) contained in a document, and to extract various content (or data) and knowledge based on its understanding.

[0048] Here, the pre-set content criteria can be set in various ways and may be determined according to the intended use and purpose of the response generation system 100 according to the present invention. For example, if the purpose of using the response generation system 100 is chemistry, biotechnology, new materials, new substances, and new drug development, the document understanding model 300 may be trained to understand and extract content related to chemistry, biotechnology, new materials, new substances, and new drug development from the documents being analyzed.

[0049] In this case, the pre-defined content criteria may include content related to molecular structure concerning at least one of the following: chemistry, biotechnology, new materials, new substances, and new drug development. Here, the document comprehension model 300 can extract content related to chemistry, biotechnology, new materials, new substances, and new drug development from the document under analysis based on the pre-defined content criteria. For the sake of clarity, this specification describes the predefined content criteria as relating to at least one of the following: chemistry, biotechnology, new materials, new substances, and new drug development, but is not necessarily limited to these.

[0050] The document comprehension model 300 can extract at least one of the following from the document under analysis that satisfies the pre-defined content criteria: text, molecular structure, mathematical formula, chart, table, and image. For example, the document understanding model 300 can understand the chemical structure of the molecular structure contained in the document 20 to be analyzed, and based on the understanding, it can convert the molecular structure into SMILES string notation and extract it. Furthermore, the document understanding model 300 can understand the chemical structure of the molecular structure and, based on the understanding, perform a graph transformation corresponding to the molecular structure.

[0051] On the other hand, in this invention, inputting "molecular structure" can be understood as inputting information that can identify a molecule. In this case, the information that can identify a molecular structure may take various forms, such as molecular structural formulas, molecular graphs, chemical formulas, molecular structural formulas according to SMILES notation, and molecular structural images. Furthermore, the document understanding model 300 can understand the text 23 related to molecular structure 21 from among the texts contained in the document 20 to be analyzed, and extract this as text data 23 related to molecular structure 21.

[0052] Furthermore, the document understanding model 300 can recognize the rows and columns that make up a table 24 related to the molecular structure 21 from the document 20 to be analyzed, and convert and extract this into structured data 25 in a format such as HTML or Excel. Furthermore, the document understanding model 300 can extract relationship information (or relationships) between molecular structures contained in the document.

[0053] As described above, the document understanding model 300 can convert and extract various forms of data contained in a document into data in a form that the ultra-large-scale infrastructure model 200 can understand (for example, machine-readable data). The data extracted using the document understanding model 300 may be sorted by page or document and stored in the storage unit 140 (or memory). In the present invention, the document understanding model 300 is also called a deep document understanding model. Next, the chemical reaction prediction model 400 may be configured to predict the chemical reaction between the input molecular structures (or compounds or reactants) and output (or generate) the predicted chemical reaction result (or product). For example, as shown in Figure 2a, when information about the molecular structures 401a and 401b of a particular compound is input, the chemical reaction prediction model 400 can predict the product 402 that will be formed by the chemical reaction between the molecular structures 401a and 401b of that particular compound.

[0054] The chemical reaction prediction model 400 according to the present invention can model electron transfer through the graph structure of molecules. The chemical reaction prediction model 400 according to the present invention may also be a prediction model based on electron flow using graph diffusion that can deeply understand the chemical reaction mechanism and accurately predict the chemical reaction results. The chemical reaction prediction model 400 can represent both input and output in graph space. By learning the graph transformation of chemical reactions, the chemical reaction prediction model 400 can output predicted results of reaction products and / or by-products based on the graph structure of the input compound. In the present invention, the chemical reaction prediction model 400 is also called an electron flow model, or an electron flow-based forward reaction prediction model, or an electron-flow inspired graph diffusion model for interpretable forward reaction prediction. On the other hand, the chemical reaction prediction model 400 described above may include at least one of the encoder 410 and the decoder 420.

[0055] The encoder 410 according to the present invention may be configured to extract the characteristics of each atom and bond through a graph representation of the molecular structure and generate an embedding vector based on this. Alternatively, it may be configured to better understand the properties of the molecular structure by performing attention calculations related to the interactions between atoms of the plurality of molecular structures via a neural network. The decoder 420 can perform bond prediction and atom prediction, which are predicted as chemical reactions of the plurality of molecular structures, using the embedding vectors obtained by the encoder 410. The chemical reaction prediction model 400 can stabilize the chemical reaction results obtained by bond prediction and atom prediction by performing a diffusion feedback process and generate the final chemical reaction result. The configuration and detailed process of the chemical reaction prediction model 400 according to the present invention will be discussed in more detail later. On the other hand, the chemical reaction prediction model 400 according to the present invention can utilize various chemical information such as reaction conditions, catalysts, and temperature, in addition to molecular structure information, for more accurate prediction of chemical reactions. Such chemical information may include molecular structure data (e.g., molecular structure graphs) and descriptive data (e.g., text data). Chemical reactions are influenced by various factors such as molecular structure changes, reaction mechanisms, reaction conditions, and reaction environments, so these factors must be considered comprehensively for more accurate predictions. Such chemical information is contained in various literature, textbooks, papers, patent documents, articles, and academic journals, and the chemical reaction prediction model 400 according to the present invention can make more accurate chemical reaction predictions by integrating such chemical information.

[0056] For this purpose, as shown in Figure 2b, the chemical reaction prediction model 400 may include at least one of the first module 400a and the second module 400b. Here, the first module 400a is also called the "ChemExpert-Graph" module, graph module, graph processing module, graph model, graph processing model, etc., and the second module 400b is also called the "ChemExpert-Text" module, text module, text processing module, text model, text processing model, etc. The first module 400a can take a molecular (or chemical) structure as input and predict a chemical reaction based on a graph. For example, referring to Figure 2d(a), one embodiment of a chemical reaction can be seen. Also, as shown in Figure 2d(b), the first module 400a may include multiple layers 400a-1 to predict a chemical reaction based on a graph. More specific details about the multiple layers 400a-1 will be described later.

[0057] The molecular structure (or molecular structural formula) 403a input to the first module 400a is converted into a molecular graph, in which atoms may be represented as nodes and interatomic bonds as edges. The molecular structure 403a may be data extracted from a document containing the molecular structure 403a using the document understanding model 300, or it may correspond to at least one piece of information extracted through the storage unit 140 (or memory).

[0058] Module 400a can analyze changes in the structural properties of molecules based on the input molecular graph, predict chemical reaction pathways and the products generated as a result of the chemical reactions, and output the predicted chemical reaction pathways and products. In one embodiment, the first module 400a can analyze structural changes of a molecule based on a molecular graph and predict the process by which specific bonds are separated and new bonds are formed.

[0059] In other embodiments, the first module 400a can analyze the interactions between atoms within a molecule based on a molecular graph and predict the formation of radicals and binding changes that may occur at each step. In other words, the first module 400a may be configured to receive a molecular graph as input and output a predicted chemical reaction pathway and products based on the molecular graph.

[0060] Next, the second module 400b may be configured to process text data 403b and understand and predict the reaction mechanism. In this case, the text data 403b may be data extracted from a document containing molecular structure 403a using the document understanding model 300, or it may correspond to at least one piece of information extracted through the storage unit 140 (or memory) related to molecular structure 403a. The second module 400b may be a model that has been pre-trained on a vast amount of data related to chemical reactions. In one embodiment, the text data 403b input to the second module 400b is data containing a description of the molecular structure 403a, and may include at least one of the following: chemical reaction conditions, chemical reaction mechanism (or reaction pathway), and chemical properties of the molecular structure 403a.

[0061] The second module 400b can analyze the input text data 403b to understand and predict the chemical reaction mechanism. More specifically, the second module 400b can analyze the input text data 403b and output at least one of the following: chemical reaction conditions, chemical reaction mechanism, or chemical properties, based on the text data 403b. In one embodiment, the second module 400b can analyze the text data 403b using natural language processing (NLP) techniques and extract at least one text from among the chemical reaction conditions, chemical reaction mechanism (or reaction pathway), chemical properties, and experimental data contained in the text data 403b.

[0062] In another embodiment, the second module 400b can predict the chemical reaction mechanism (e.g., how a particular catalyst or condition affects the reaction) based on the text extracted through the analysis of the text data 403b, and output the predicted chemical reaction mechanism and chemical properties. The second module 400b can analyze information from text data regarding the prediction of chemical reactions related to the multiple molecular structures.

[0063] The chemical reaction prediction model 400 can combine the output data 404a from the first module 400a and the output data 404b from the second module 400b to output the final chemical reaction prediction result (e.g., products, chemical reaction pathway, chemical reaction mechanism, etc.). In one embodiment, as shown in Figures 2c(a) to (c), the chemical reaction prediction model 400 can generate electron flow, reaction conditions, and structural effects of a molecular structure (or chemical structure) using output data output from the first module 400a and the second module 400b. In this case, electron flow, reaction conditions, and structural effects can be represented together as graphs and text, and a molecular structure that reflects the position before and after electron movement can be generated, or the molecular structure of the product generated according to the reaction conditions can be generated.

[0064] In other words, the chemical reaction prediction model 400, by combining output data 404a and 404b output from the first module 400a and the second module 400b, respectively, can make more accurate predictions than by using a single data source alone, and allows users to intuitively recognize various factors related to chemical reactions. Furthermore, the chemical reaction prediction model 400 can verify the chemical reaction results predicted through the first module 400a using the output data analyzed in the second module 400b. Specifically, the second module 400b can obtain at least one of the chemical reaction conditions, chemical reaction mechanism, or chemical properties analyzed based on the text data 403b. Based on the data analyzed in the second module 400b, the chemical reaction prediction model 400 can verify whether the chemical reaction results predicted and obtained in the first module 400a are consistent with experimental data or with theoretical expectations.

[0065] On the other hand, the chemical reaction prediction model 400 according to the present invention is characterized by performing chemical transformations based on a molecular graph, and for understanding this, we will briefly examine the configuration of the molecular structure graph. Figure 2e(a) shows nodes and edges. In a molecular graph, atoms may be represented as nodes and interatomic bonds as edges. For example, as shown in Figure 2e(b), a water molecule has the molecular formula "H2O," and in this case, the atoms are H, H, and O. In this case, there are three nodes, and as shown in Figure 2e(c), the atoms H, H, and O may be represented as nodes n1, n2, and n3, respectively. There are also two interatomic bonds, OH and OH, and these bonds may be represented as edges e1 and e2, as shown in Figure 2e(c). When converting a molecular structure to a molecular graph in this way, the unique positional and phase relationships of the molecular structure can be preserved, enabling more accurate predictions. In this invention, the molecular structure to be predicted is converted to a molecular graph and input to the encoder 410 to predict a chemical reaction.

[0066] On the other hand, in the present invention, the encoder 410 can embed molecular structures into vectors using molecular graphs. Briefly considering the vectors, as shown in Figure 2f(a), the ethanol (C2H5OH) molecule can be represented by vectors having specific dimensions, as shown in Figure 2f(b). For example, vectors corresponding to each atom of ethanol can be represented as shown. The dimensions of the vectors and the information they contain can be set in various ways. For example, the diagram shows five-dimensional vectors, and each vector may include atomic type, bonding information, charge information, hybridization information, directional information, etc. Although not shown, the encoder 410 can be used for analysis by embedding vectors corresponding to the bonding information of molecules. Below, we will more specifically examine the method for predicting chemical reactions based on molecular graphs and obtaining the resulting chemical reaction products in the chemical reaction prediction model 400 according to the present invention. The following explanation can be performed using the encoder 410 examined in Figure 2b.

[0067] Chemical reaction prediction and organic synthesis are important challenges in new drug development and / or materials science. Predicting the outcome of chemical reactions is crucial for designing new molecules and can significantly contribute to shortening product development cycles in various industrial fields. Traditionally, sequence-based models using SMILES strings have been utilized to predict chemical reactions and / or retrosynthetic pathways. SMILES strings encode chemical structures (or molecules or molecular structures) into ASCII strings, allowing them to be processed as text data using natural language processing techniques. However, because SMILES strings do not match the natural graph representation of molecules, they have low learning efficiency and can produce chemically invalid transformations (or deformations). In particular, in the case of chemical reactions, transformations in SMILES space may not directly correspond to valid molecular graphs, potentially resulting in chemically unfeasible outputs. In other words, there is a limitation in that such transformations do not always lead to valid deformations in graph space, which is a more natural representation of molecules.

[0068] To address these problems, various graph-based (or graph-centered) approaches have been proposed to represent molecules as graphs. Graph representations can accurately depict molecular structures, enhance the interpretability of chemical reaction mechanisms, and facilitate more accurate chemical reaction predictions based on electron flow theory. Therefore, the present invention proposes a chemical reaction prediction model based on electron flow using graph diffusion, which allows for a deep understanding of chemical reaction mechanisms and accurate prediction of chemical reaction results by modeling electron transfer through the graph structure of molecules. The prediction method according to the present invention is also called the Electron-Flow Inspired Graph Diffusion Model for Interpretable Forward Reaction Prediction method.

[0069] The chemical reaction prediction model described in this invention utilizes graph diffusion, so that both inputs and outputs operate in graph space, which can provide the user with a more interpretable transformation while maintaining invariance with respect to the permutations and orders of SMILES strings. The chemical reaction prediction model according to this invention can learn the transformation process from an initial set of graphs to the final product, rather than starting with random graph sampling. The present invention will be discussed in more detail below, along with the attached drawings.

[0070] As shown in Figure 4a, the chemical reaction prediction model 400 according to the present invention may include an encoder 410, a decoder 420, a graph diffusion module 430, and a learning unit 440. As shown in the figure, the chemical reaction prediction model 400 can work with memory and at least one processor to perform a series of data processing according to the present invention. The memory can store various information. For example, the memory may store various information such as information input to the chemical reaction prediction model 400, information about results, intermediate products, etc., generated by a series of data processing in the chemical reaction prediction model 400, and information about the final result. The processor can work with each component of the chemical reaction prediction model to perform the chemical reaction prediction process according to the present invention in each component and neural network layer. In the chemical reaction prediction model 400 according to the present invention, a learning unit 440 and a ground-truth product are present during the learning step, as shown in Figures 4a and 4b. However, after learning is completed, the learning unit and the ground-truth product may be removed, as shown in Figures 4c and 4d.

[0071] The encoder 410 according to the present invention may be configured as a backbone encoder. The encoder 410 according to the present invention is configured as an attention-based graph neural network (GNN) and can perform relative position encoding based on the shortest path between nodes (atoms). On the other hand, the diffusion process described in the present invention does not depend on a backbone network and can be realized by various encoders. The encoder 410 according to the present invention may include an embedding layer 411 and a graph neural network (GNN) 412, as shown in the figure. The graph neural network may be an attention-based graph neural network or a backbone neural network.

[0072] On the other hand, the graph neural network 412 may include a multi-head self-attention layer 413 and a feed-forward neural network layer. The multi-head self-attention layer is also called a topologically weighted multi-head self-attention layer. The embedding layer 411 is configured to receive information about multiple molecular structures and convert it into embedding vectors. The embedding layer 411 may also be configured to generate vectors that include atoms of the molecular structures and the bonding relationships between atoms. The graph neural network 412 can receive the embedding vectors embedded in the embedding layer 411, perform attention operations related to the interactions between atoms of the multiple molecular structures, and update the embedding vectors based on the operations. At this time, the embedding layer can receive a molecular graph and obtain embedding vectors based on it.

[0073] On the other hand, the node features included in the embedding vector output by the embedding layer 411 may include the atomic type, atomic charge, implicit number of hydrogen atoms, and number of radical electrons. The embedding layer 411 can encode nodes following the molecular graph and generate an embedding vector containing information that can be used within the encoder. This allows the molecular graph to be completely reconstructed for each molecule, and the node order is also tokenized as an input feature that can later be used to measure the consistency between bond predictions and predicted atomic orders. The characteristics of atom v may also be represented as (f), (g), and (h) in Figure 5a, and may be tokenized and embedded via the embedding layer 411.

[0074] As shown in Figure 5a(g), the features of atom v are tokenized and embedded via the embedding layer 411, and the tokenized features of type i shown in Figure 5a(f) may have features as shown in Figure 5a(g). i is an index representing the type of atomic feature and can belong to the set of type, charge, hydrogen, radical, or order. i means at least one of the features of type, charge, hydrogen, radical, or order, where type may mean atomic type (e.g., carbon, oxygen, etc.), charge may mean the formal charge of the atom, hydrogen may mean the number of implicit hydrogens, radical may mean the number of radical electrons of the atom, and order may mean the order of the node (the number of other atoms bonded to that atom). Such features are embedded via the embedding layer 411 as shown in Figure 5a(h), forming the initial embedding of the atom. The initial embedding of atom v may be the average of all embeddings. The embedding layer 411 can perform embedding for multiple molecules based on a molecular graph. The molecular graph relating to molecular structure may exist stored in memory, a dataset, a database, etc. Alternatively, the molecular graph relating to molecular structure may be extracted by the molecular graph extraction module 450. The chemical reaction prediction model 400 may further include the molecular graph extraction module 450 and cooperate with the molecular graph extraction module 450. Furthermore, it goes without saying that the molecular graph extraction module 450 may be included as a component of the encoder 410.

[0075] Molecular graphs can maintain multiple molecular structures by representing atoms as nodes and the bonds between atoms as edges. Multiple molecular structures may include a first molecular structure and a second molecular structure, and such first and second molecular structures are also called reactants. These correspond to reference numerals 401a and 401b in Figure 4a.

[0076] The molecular graph extraction module 450 can obtain molecular graphs relating to the multiple molecular structures by using a pre-identified graph transformation algorithm to transform atoms into nodes and interatomic bonds into edges, based on the multiple molecular structures. The molecular graph extraction module 450 can obtain a first molecular graph including nodes and edges corresponding to the first molecular structure by converting the atoms constituting the first molecular structure into nodes and the bonding relationships between the atoms constituting the first molecular structure into edges. Similarly, the molecular graph extraction module 450 can obtain a second molecular graph including nodes and edges corresponding to the second molecular structure by converting the atoms constituting the second molecular structure into nodes and the bonding relationships between the atoms constituting the second molecular structure into edges using the pre-specified graph transformation algorithm.

[0077] In the embedding layer 411 of the encoder 410, embedding vectors corresponding to the plurality of molecular structures can be obtained using the information about the plurality of molecular structures. In this case, the information about the plurality of molecular structures may include information about nodes and edges corresponding to the first molecular structure and information about nodes and edges corresponding to the second molecular structure. The information about molecular structures may be a molecular graph extracted by the molecular graph extraction module 450, or information extracted from such a molecular graph. The embedding vector may include at least one piece of information regarding the atomic type, atomic charge, number of hydrogen atoms, and the number and degree of radical electrons of the nodes corresponding to the first molecular structure and the nodes corresponding to the second molecular structure. Furthermore, the embedding vector may further include information regarding the bonding relationships of the atoms in the first and second molecular structures.

[0078] On the other hand, once the reactants to be predicted, i.e., multiple molecular structures (for example, a first molecular structure 401a and a second molecular structure 401b), are identified, the chemical reaction prediction model 400 can use the nodes and edges corresponding to the first molecular graph and the nodes and edges corresponding to the second molecular graph to extract at least one of the following: an adjacency matrix, a bond type matrix, shortest paths, and K-hop neighbors, respectively, for the first and second molecular graphs. The timing at which such information 480 is extracted is not limited and may vary. The adjacency matrix may also include information regarding direct connections between nodes constituting the first molecular graph and the second molecular graph.

[0079] The bond type matrix may include information regarding the bond types between nodes constituting the first molecular graph and the second molecular graph. The shortest path matrix may include information regarding the shortest path lengths between nodes constituting the first molecular graph and the second molecular graph.

[0080] The K-hop neighborhood information may include information about neighboring nodes within K hops for each node constituting the first molecular graph and the second molecular graph. At least one of the extracted information 480, consisting of an adjacency matrix, bond type matrix, shortest paths, and K-hop neighbors, may be stored in memory. At least one of the adjacency matrix, bond type matrix, shortest paths, and K-hop neighbors stored in memory may be input to at least one of the embedding layer 411, graph neural network 412, multi-head self-attention layer 413, and feedforward neural network layer 414.

[0081] In the embedding layer 411, when embedding vectors v1 corresponding to multiple molecular structures (or reactants, e.g., first molecular structure 401a, second molecular structure 401b) are obtained, the obtained embedding vectors v1 may be input to the graph neural network 412. In particular, the embedding vectors v1 may be input to the multi-head self-attention layer 413 of the graph neural network. The Multi-Head Self-Attention layer 413 can perform attention calculations related to the interactions between atoms of multiple molecular structures using an embedding vector v1 embedded based on a molecular graph, and update the embedding vector based on the calculation. In this case, the embedding vector v1 to be updated may be a vector output from the embedding layer.

[0082] The multi-head self-attention layer 413 can perform global attention-based pooling. The multi-head self-attention layer 413 can add a bias to the number of attention weight points derived from the shortest paths between node pairs to account for molecular graph topology. This can act as a relative position encoding between atoms. Consequently, this is unaffected by spectrum-based encoding problems (e.g., lack of sign invariance for Laplace matrix eigenvectors) because the distance measurements between nodes are not absolute. Such encoding is computationally efficient and allows for the consideration of the entire graph topology while allowing for the transmission of global messages, where the timing of the shortest path calculations may vary. Global attention-based pooling considers the entire topology of the graph, and the multi-head self-attention layer 413 can globally consider the relationships between each node in the graph by performing global attention-based pooling.

[0083] For example, assuming the input vector from the embedding layer has a dimension of 128, and the number of heads in the multi-head attention system is 8, each head may be input a 16-dimensional sub-vector, which is the 128-dimensional input vector divided by the number of heads, 8. Each head of the multi-head self-attention layer 413 is configured to independently calculate attention values ​​for 16-dimensional subvectors by applying a pre-configured attention mechanism. For example, the first head may primarily reflect information about adjacent nodes in the molecular graph, and the second head may primarily reflect information about a specific bond type in the molecular graph. The 16-dimensional attention vectors calculated by each of the multiple heads of the multi-head self-attention layer 413, for example, eight heads, may be combined again to form a 128-dimensional vector. This combined vector has the same dimensions as the embedding vector output from the embedding layer 411, but can contain richer information by integrating various information extracted from each head. In this invention, this can be expressed as an updated embedding vector.

[0084] On the other hand, in order to reflect the shortest path between nodes in the weights, the present invention can calculate the shortest path between all pairs of nodes in the molecular graph, as described above. The shortest path can be calculated by a pre-set algorithm. The multi-head self-attention layer 413 can encode the relative positions between nodes using the calculated shortest path information. This encoding can be done based on the shortest path distance between nodes. When the multi-head self-attention layer 413 calculates the number of attention weight points between nodes, it can use the shortest path information as a bias. For example, the multi-head self-attention layer 413 can adjust the attention weights by increasing the attention weight when the shortest path distance between two nodes is close, and decreasing it when the shortest path distance between two nodes is farther. The multi-head self-attention layer 413 can add the bias derived from the shortest path between pairs of nodes to the number of attention weight points. The multi-head self-attention layer 413 thus encodes each node of the molecular graph using attention weights adjusted based on the shortest path, which enables encoding that takes into account the entire topology of the molecular graph.

[0085] Furthermore, the multi-head self-attention layer 413 can add a bias to the attention score based on the bond type (e.g., single, double, triple, aromatic bond). For example, in the multi-head self-attention layer 413, different biases can be applied to the attention score calculated in the multi-head self-attention layer, depending on the type of binding between the nodes constituting the first molecular graph and the second molecular graph.

[0086] Here, the bond type may include single bond type, double bond type, triple bond type, and aromatic bond type. This may mean that instead of a single bias matrix for all heads, each head has its own unique bias matrix. For example, the first head may have a shortest path bias added, and the second head may have a single bond bias added. The remaining heads can use masks that focus on interatomic or intermolecular global attention. In the present invention, in the multi-head self-attention layer 413, such various bias matrices can be added to each layer with multiple heads. In the present invention, by adding bias matrices to each layer corresponding to multiple heads, the attention score of each head can highlight information masked by the bias matrix. For example, if a bias matrix corresponding to the bond type is added, the head can receive information only from neighbors connected by a specific bond type, masked according to the bond type. For example, a particular head can receive information only from nodes connected by single bonds.

[0087] Thus, the weights for the attention score according to the present invention, which add biases that reflect various properties of the molecular structure, can be calculated according to the formulas and meanings shown in Figure 5. Here, A(Qk, Kk, Vk) may represent the attention weights calculated by applying the softmax function to the attention score. In this way, the multi-head self-attention layer 413 performs attention calculations related to the interactions between atoms of the plurality of molecular structures, and updates the embedding vector based on the calculations. The updated embedding vector can then be stored in memory. The updated embedding vector stored in memory can then be input to the feed-forward neural network layer 414. The feed-forward neural network layer 414 receives the output of the multi-head self-attention layer 413 and can further update the updated embedding vector. The feed-forward neural network layer 414 can update the output vector of the multi-head self-attention layer using at least one of the adjacency matrix, bond type matrix, shortest paths, and K-hop neighbors. The feed-forward neural network layer 414 can update the output vector of the multi-head self-attention layer so that it contains more structural information about the molecular structures. The output vector of the feedforward neural network layer can be identified as the final updated embedding vector v2 input to the decoder 420.

[0088] Furthermore, the decoder 420 according to the present invention may include at least one projection layer 421. The decoder 420 can perform bond prediction and atom prediction based on multiple molecular structures (reactants) based on an updated embedding vector v2 input to the projection layer. Bond prediction predicts the bonds between atoms, and atom prediction predicts the state changes of atoms. The decoder 420 may include multiple projection layers 422a, 422b. Bond prediction 423 and atom prediction 424 can be performed in each of the projection layers 422a, 422b. In the decoder 420, coupling prediction can be performed by executing a dot product operation using the updated embedding vectors. The dot product operation can be performed for each vector corresponding to each atom pair of atoms corresponding to the updated embedding vectors.

[0089] In block 423 in the drawing, each box on the x-axis 423a and y-axis 423b conceptually represents an atom of the molecule to be reacted, and such atoms can be represented as the updated embedding vector v2. Based on the information contained in the updated embedding vector, the decoder 420 can predict the bond type between each atom through an inner product operation. The decoder 420 can obtain dot product values ​​for each of the multiple bond types for each of the atom pairs based on the dot product calculation. The multiple bond types can relate to at least one of single bond, double bond, bond formation, bond collapse, and no change.

[0090] The decoder 420 can generate probability distributions for each of the multiple bond types for each of the atomic pairs using the dot product values ​​obtained by the dot product calculation. Furthermore, the decoder 420 can obtain a transformation matrix that predicts the change in the bond state of each of the atomic pairs using the probability distributions. The decoder 420 can generate the probability distribution of the multiple bond types for each of the atomic pairs by applying a softmax function to the dot product value obtained for each of the multiple bond types for each of the atomic pairs.

[0091] The decoder 420 can generate a probability distribution of atomic properties for each atom corresponding to the updated embedding vector v2 using a softmax output layer. The decoder 420 can also predict the atomic properties of the atoms corresponding to the updated embedding vector using the probability distribution. The atomic properties may include the charge state of the atoms, which can change during the chemical reaction process of the plurality of molecular structures. The decoder 420 can perform the dot product between the updated embedding vectors v2 (or atomic embedding vectors, shown as in Figure 5b(a)) for the atoms and calculate the number of potential bond transformation points, as shown in Figure 5(b). Here, the lowercase t can represent a stage in the diffusion process.

[0092] This is based on the standard query-key matrix multiplication of the transformer attention mechanism, but in this invention, the key and query can share the same projection head. The setting that the key and query have the same projection head may require many projections to accommodate various bond types such as none, single, double, triple, and aromatic. This corresponds to multi-head attention in the transformer, which can be expressed mathematically as shown in Figure 5b(c). Thus, bond prediction can be performed in bond prediction heads, which may correspond to the first projection layer 423a shown in the figure. That is, bond prediction can be performed in the first projection layer 423a. The first projection layer 423a may consist of layers that make up the bond prediction head. Each bond type prediction can be considered a head in this multi-head attention scheme, where softmax normalization is applied to a state like that shown in Figure 5b(d) to obtain the distribution of bond transformations for each atom pair, where Figure 5b(d) may represent a bond transformation.

[0093] The join prediction head calculates a score S for each join type k, as shown in Figure 5b(c), where the meaning of each symbol is as shown in Figure 5b(d). On the other hand, the join prediction head can calculate the probability of a join transformation T for a join type k, as shown in Figure 5b(e), as shown in Figure 5b(b). The join prediction head can obtain the probability distribution of the join transformation by applying softmax normalization as shown in Figure 5b(f).

[0094] In this invention, each bond type prediction can be understood as each head in a multi-head attention system. The decoder 420 is applied to the bond transformation-possible states shown in Figure 5b(e) through softmax normalization to obtain a normalized probability distribution of the bond transformation for each atom pair. This can provide an efficient and scalable method for directly predicting bond transformations in the embedding space. Next, atomic prediction can be performed in the second projection layer 423b of the decoder 420. The second projection layer 423b may consist of layers that constitute an atomic prediction head. Since the characteristics of some atoms may change during a chemical reaction, the decoder 420 according to the present invention can not only predict bond transformations between atoms but also make predictions about atoms. Atomic prediction can be performed in the second projection layer 423b. For this purpose, a standard softmax output layer can be used for each atomic characteristic. This includes the order of the atom for each bond type (e.g., the number of single bonds the atom has), and such predictions can contribute to ensuring consistency between independent parallel samples of bond transformations and atomic transformations.

[0095] The atomic prediction head can calculate the probability that the feature Av of atom v becomes a at step t of the diffusion process, as shown in Figure 5c(c). Here, Figure 5c(b) represents the embedding of atom v, and Figure 5c(d) can represent the weight matrix of the prediction head for atom feature a. The atomic prediction head can calculate the probability that the atom feature Av becomes a by multiplying the embedding h of atom v (see Figure 5c(b)) by the weight matrix W (see Figure 5c(d)) and then applying the softmax function. The decoder 420 can obtain a predicted chemical reaction product (probability distribution) 425 from chemical reactions of multiple molecular structures by combining the predicted atomic bonding probabilities and atomic prediction probabilities output from the projection layers 423a and 423b. The chemical reaction product predicted by the projection layer is also called the initialization reaction product.

[0096] In the present invention, the predicted chemical reaction results may be input to a Graph Diffusion Module 430, which can sample the initialization reaction results using the bond prediction results and the atom prediction results. The Graph Diffusion Module 430 can stabilize the sampled initialization reaction results through a diffusion feedback process. The chemical reaction prediction model can obtain the final chemical reaction results stabilized through the diffusion feedback process. In the diffusion feedback process, the graph diffusion module 430 can repeatedly evaluate each bond transformation of the initialization reaction result and remove or modify unstable bonds. Furthermore, in order to predict changes in the bond state of the initialization reaction result, the graph diffusion module 430 can evaluate the predicted bond transformations at each of the repeatedly executed steps using a transformation probability matrix and a target transformation matrix, and generate the final chemical reaction result using an interpolation factor.

[0097] In this invention, since bond prediction is performed independently across the entire molecular graph, a method is needed to obtain a valid molecular structure in the end. For example, the prediction process may result in the prediction of bonds to multiple atoms or violations of bonding rules. To solve such problems, a denoising process is needed to obtain a valid final state. As shown in Figure 5e, the predicted chemical reaction result can be stabilized by performing multiple sampling and denoising processes 432.

[0098] Figure 5e shows the diffusion process from the ground truth data 440 (or target label, ground truth product) to the initial distribution 425, which is based on an initial distribution derived from the dot product matrix of bond transformation predictions between all atomic pairs. Here, the target label corresponding to the ground truth data may include the actual bond states and atomic states corresponding to multiple molecular structures input to the encoder. Here, the initial distribution 425 refers to the initialization chemical reaction result 425 output from the decoder, which may correspond to a probability distribution. On the other hand, the diffusion process to the target label takes place during the learning process in the learning unit 440, and in the actual inference stage, a fully trained graph diffusion module 430 may be used. In this case, the predicted chemical reaction result 425 is input to the fully trained graph diffusion module 430, and after a denoising process, the final chemical reaction result can be obtained. The learning unit 440 calculates a loss function between the final chemical reaction result output from the decoder and label data (hereinafter also called "target label") which includes the actual bonding and atomic states corresponding to the plurality of molecular structures, and can optimize at least one of the parameters of the encoder and the decoder in order to minimize the loss function.

[0099] On the other hand, during the learning process, the target label may be interpolated with the initial distribution and sampled at random points in the interpolated space. Figure 5e shows a noise graph in which such random points are denoised to the target label. During prediction, the results of sampling with the initial distribution are updated at each step to obtain a stable final product molecule, and a similar process can be applied to atomic features. In the Diffusion Step, the Predicted Product, as described above, may correspond to the initial distribution obtained by the decoder 420 via the dot product matrix of bond transformation predictions for all atomic pairs. This initial distribution represents the probability distribution of bond formation between atoms. The Diffused Product is obtained by interpolating the ground-truth product with the initial distribution, and in this invention, a noisy graph can be obtained by sampling random points in this interpolated space. Such random points represent a noise graph that is denoised towards the target label. The Denoised Product is obtained by denoising the sampled noise graph to gradually approach the target label, and this process is carried out in multiple stages, with the graph changing more stably at each stage. The Alternative Product means that, in some cases, an alternative product may be obtained through a different path, which may have a different bond configuration than the target label.

[0100] Thus, in this invention, the decoder 420 acquires an initial distribution, and the spread graph module 430 samples the initial distribution to sample random points. The spread graph module 430 interpolates the target label with the initial distribution through interpolation and denoising processes to obtain an intermediate state. During the learning process, such an intermediate state v3 may be input again to the encoder. The spread graph module 430 samples random points to obtain a graph containing noise and gradually approaches the target label through a denoising process. Such a process may be performed repeatedly, and in such iterative processes, at least one of the encoder, decoder, and spread graph module can be learned. The graph diffusion module 430 may be configured to learn how to denoise the predicted chemical reaction results 425 by first diffusing the target label into the previous output distribution, and then learning how to map the states in the diffused interpolation space to the final target label.

[0101] For this purpose, a transformation probability matrix Mt and a desired target transformation matrix T can be defined, and the transformation matrix T can be derived from the atomic bond prediction results. The atomic bond prediction results are obtained from the decoder and can be derived from the bond prediction head described above. This can be represented as the atom-mapped label of the reaction. The transformation matrix T can identify whether bonds are formed or broken by calculating the difference between the input and output adjacency matrices, and the transformation matrix T can be represented as shown in Figure 5d(a). This defines the difference between the adjacency matrices of the reactants and products, and the adjacency matrix can represent the entire bond type matrix including single / double / triple / aromatic bonds. On the other hand, the reactants change continuously at each diffusion step and are therefore time-dependent. This means that the target label also changes at each step, and in this invention, a method can be learned to predict when and where the difference between the reactants and the target label occurred (reaction center), and if a difference occurred, what changed (final combined configuration), as shown in Figure 5d(b). The parameter at is a randomly sampled interpolation factor that determines the weights of the current transformation matrix and the target transformation matrix. During learning, the diffusion process may include obtaining the transformation probability Mt from the initial reactant set {Gr} and sampling {Gr(t+1)} at Mt+1 to use as input for the next step. Either step may aim to directly predict the target transformation matrix T. Therefore, the learning loss can be defined as shown in Figure 5d(c). During prediction, the input {Gr} can simply be encoded, the probability of the combined transformation Mt can be obtained using the prediction head, and sampling can be performed at each step using standard categorical softmax sampling on this distribution. In the learning process of this invention, this process can be continuously repeated and the results fed back until a stable configuration is obtained in which the combinations no longer change.

[0102] On the other hand, in this invention, the final chemical reaction result can be derived by applying Top-K ranking to multiple results stabilized in the diffusion module 430. This may be performed in the Top-K Sampled Product module of the chemical reaction prediction model 400. The Top-K ranking process may be performed in the inference stage, that is, it can be used to extract the results with the highest probability in order to provide the user with the final result. In this invention, at least one of Top-K sampling and Top-K ranking can be performed, which can be used to obtain a wider variety of results in the inference process. In Top-K sampling, multiple samples can be generated based on what the chemical reaction prediction model has learned, and the distribution can be diversified by adjusting the temperature parameter τ. Top-K ranking is used to select the most likely result from the generated samples, the probability of each sample can be calculated, and the result with the highest probability can be selected as the final prediction. In the Top-K sampling process, when sampling the distribution Mt at a time step t, the logits can be readjusted by the temperature parameter τ. This allows for sharper or more uniform adjustment of the softmax distribution shown in Figure 6(a).

[0103] On the other hand, in order to obtain multiple samples, the present invention can use a temperature-mediated parameter (temperature schedule) τ. In the present invention, when attempting to extract N samples, τi can be set as shown in Figure 6(b). Here, f(i) is the temperature sampling schedule, and in the present invention, f(i) can be defined as shown in Figure 6(c). Here, x is the linspace between 0 and 1. Next, we consider the Top-K ranking. When sampled candidates are available, a method is needed to rank them based on probability. This can be done by calculating probabilities using the transformation matrix Mt. If the sampled states are considered as sampled tokens for each atom and bond, the probability of each state can be derived from the sampled softmax distribution Mt. This allows us to assign a total probability to the final overall configuration of the molecule (atomic and bond states) and calculate the sampled probabilities (S). This can be shown as in Figure 6(d). Here, Figures 6(e), (f), and (g) can be derived from the softmax distribution for atomic features, bond features, and atomic order, respectively. Furthermore, term 6(d) can ensure consistency between the predicted atomic order and the actual order calculated from the bonds. In this invention, since bonds are sampled independently, multiple bonds can be formed on the same atom. Therefore, the actual order of each atom in the sampled graph is directly calculated and compared with the predicted probability distribution of the order. In this method, if the actual order of the generated molecule matches the predicted order, a high probability score is assigned.

[0104] Furthermore, since there is no guarantee that all solutions are unique when sampling independently of the distribution, many duplicate samples may occur. Therefore, in this invention, the Top-K products can be selected during prediction by taking a simple, unique graph. On the other hand, the above explanation focused on Figures 4a and 4b, which both include the learning process of the chemical reaction prediction model. However, as mentioned above, in the inference process, the chemical reaction prediction model can have the configurations shown in Figures 4c and 4d. In this case, if the learning process is excluded, everything can be understood similarly, so please refer to the explanation above for a detailed explanation.

[0105] In the inference process, as described above and as shown in Figure 3, the encoder receives information about a plurality of molecular structures, including a first molecular structure and a second molecular structure, as input (S310); the embedding layer of the encoder uses the information about the plurality of molecular structures to obtain embedding vectors corresponding to the first molecular structure and the second molecular structure (S320); the multi-head self-attention layer performs attention calculations related to the interaction between atoms of the first molecular structure and the second molecular structure and updates the embedding vectors based on the calculations (S330); the updated embedding vectors are stored in memory and input to the decoder (S340); and the decoder uses the updated embedding vectors to perform bond prediction and atom prediction, which are predicted as chemical reactions of the first molecular structure and the second molecular structure. The final chemical reaction product can be obtained through a process of performing prediction (S350) and a process of obtaining the final chemical reaction product predicted from the chemical reactions of the first molecular structure and the second molecular structure using the results of the bond prediction and the atom prediction (S360). The process for obtaining such a final chemical reaction result can be carried out through the response generation system 100 discussed in Figure 1. In this system, the response generation system 100 can receive user queries, and these user queries may include information about the molecular structure to be predicted for the chemical reaction.

[0106] In the chemical reaction prediction model 400 according to the present invention, as shown in Figure 7(a), the chemical reaction result can be obtained through bond prediction and atom prediction for multiple molecular structures that are the target of the chemical reaction, as shown in Figure 7(b). As described above, the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system according to the present invention allow for a deep understanding of the chemical reaction mechanism and accurate prediction of the chemical reaction results by modeling electron transfer through the graph structure of molecules.

[0107] Furthermore, the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system according to the present invention provide a chemical reaction prediction model that can deeply understand the chemical reaction mechanism and accurately predict the chemical reaction results, thereby reducing the time and cost of experiments for users. This reduces research and development costs and shortens the time to market for new products. On the other hand, according to the chemical reaction prediction system, its control method, and learning method for the chemical reaction prediction system of the present invention, by performing both the input of molecular structures and the output for predicted results in graph space, forward reaction prediction can be performed regardless of the permutation and order of SMILES.

[0108] Furthermore, according to the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system of the present invention, it is possible to ensure consistency between transformations by enabling simultaneous sampling of multiple interdependent transformations that occur in parallel within the molecular graph. Furthermore, according to the chemical reaction prediction system, its control method, and the learning method for the chemical reaction prediction system of the present invention, in order to solve problems that may arise due to symmetrical structures, it is possible to break the symmetry by including noise or a sampling mechanism and form an effective output structure, thereby preventing the occurrence of ineffective configurations.

[0109] Next, the Document Understanding model 300 can extract various forms of content from a document (e.g., a paper, book, patent document, report, etc.) that meet pre-defined content criteria. More specifically, the Document Understanding model 300 may be a model trained to understand structured data, unstructured data, linguistic data (or linguistic elements), and non-linguistic data (or non-linguistic elements) contained in a document, and to extract various content (or data) and knowledge based on its understanding. Here, the pre-set content criteria can be set in various ways and may be determined according to the application and purpose of the response generation system 100 according to the present invention.

[0110] For example, if the purpose of using the response generation system 100 is chemistry, biotechnology, new materials, new substances, and new drug development, the document understanding model 300 may be trained to understand and extract content related to chemistry, biotechnology, new materials, new substances, and new drug development from the documents being analyzed. In this case, the pre-defined content criteria may include content related to molecular structure concerning at least one of the following: chemistry, biotechnology, new materials, new substances, and new drug development. Here, the document comprehension model 300 can extract content related to chemistry, biotechnology, new materials, new substances, and new drug development from the document under analysis based on the pre-defined content criteria.

[0111] For the sake of clarity, this specification describes the predefined content criteria as relating to at least one of the following: chemistry, biotechnology, new materials, new substances, and new drug development, but is not necessarily limited to these. The document comprehension model 300 can extract at least one of the following from the document under analysis that satisfies the pre-defined content criteria: text, molecular structure, mathematical formula, chart, table, and image.

[0112] In one embodiment, as shown in Figure 8, the document understanding model 300 can understand the chemical structure of the molecular structural formula 21 contained in the document 20 to be analyzed, and based on the results of this understanding, it can convert the molecular structural formula 21 into a SMILES string representation 22 and extract it. Furthermore, the document understanding model 300 can understand the chemical structure of the molecular structural formula 21 and, based on the results of this understanding, can perform a graph conversion corresponding to the molecular structural formula 21. In another embodiment, the document understanding model 300 can understand the text 23 related to the molecular structural formula 21 from the text contained in the document 20 to be analyzed, and extract this as text data 23 related to the molecular structural formula 21.

[0113] In another embodiment, the document understanding model 300 can recognize rows and columns that constitute a table 24 related to molecular structural formulas 21 from the document 20 to be analyzed, and convert and extract this into structured data 25 in a format such as HTML or Excel. Furthermore, the document understanding model 300 can extract relationship information (or relationships) between molecular structures contained in the document.

[0114] In one embodiment, as shown in Figure 12, among the multiple molecular structures included in the document 600 to be analyzed, the third molecular structure to which the third label M3 is assigned can be understood as a molecular structure (or compound) produced as a result of a chemical reaction between the first molecular structure to which the first label M1 is assigned and the second molecular structure to which the second label M2 is assigned. The document understanding model 300 can understand the relationship between the first molecular structure and the second molecular structure included in the document 600 to be analyzed and extract relationship information that shows how the third molecular structure is produced through a chemical reaction between the first molecular structure and the second molecular structure. In this case, information about the relationships between molecular structures may be extracted by understanding the text contained in the document being analyzed, or by understanding the non-verbal data contained in the document being analyzed.

[0115] In one embodiment, the document understanding model 300 can understand the relationship between the first molecular structure and the second molecular structure through symbols (e.g., plus signs, arrows, etc.) present in one of the multiple regions contained in the document 600 to be analyzed, where the first molecular structure and the second molecular structure are located, and can extract relationship information that the third molecular structure is generated through a chemical reaction between the first molecular structure and the second molecular structure. As described above, the document understanding model 300 can convert and extract various forms of data contained in a document into data in a form that the ultra-large-scale infrastructure model 200 can understand (for example, machine-readable data). The data extracted using the document understanding model 300 may be sorted by page or document and stored in the storage unit 140 (or memory). In the present invention, the document understanding model 300 is also called a deep document understanding model.

[0116] Next, the molecular property prediction model 500 may be a model pre-trained on various training data to predict the properties of a substance (or molecule) or to design a material structure. For example, the training data used to train the molecular property prediction model 500 may include data containing information on the intrinsic properties of materials and information on the physical properties of materials.

[0117] Here, the intrinsic property information of a substance may include the name of the substance, its molecular structure formula, and / or chemical formula. Furthermore, the physical property information of a substance may include physical property (i.e., domain) values ​​such as boiling point, melting point, refractive index, solubility, viscosity, surface tension, density, strength, and / or thermal conductivity. Such molecular property prediction models 500 can predict the physical properties (or information about physical properties) of a substance, or they can design a material with the physical properties desired by the user.

[0118] Specifically, the molecular property prediction model 500 can take information on the intrinsic properties and / or physical properties of a substance as input and output predicted data based on the input information and learned knowledge. In one embodiment, the molecular property prediction model 500 can receive intrinsic property information of a specific substance as input and output predicted property information of the specific substance based on the input information and learned knowledge.

[0119] In another embodiment, the molecular property prediction model 500 can take property information of a specific substance as input and output predicted intrinsic property information of that specific substance based on the input information and learned knowledge. In another embodiment, the molecular property prediction model 500 can receive intrinsic property information and physical property information of a specific substance as input, and output the optimal intrinsic property information and physical property information of the substance predicted based on the input information and learned knowledge.

[0120] As described above, the answer generation system 100 based on the ultra-large-scale infrastructure model 200 aims to improve the efficiency of natural science research by proposing optimal research methods for researchers in the field of natural science and minimizing the risk of failure in natural science research. More specifically, the present invention aims to solve the problems of time and cost associated with the research and development of materials and to improve the efficiency of the research and development of materials. Below, we will examine the answer generation method of the ultra-large-scale infrastructure model and the overall process of the system. The response generation system 100 can identify the target of analysis based on user input received from the user terminal 10. Here, user input may include at least one of documents, images, audio, video, and text. For example, if user input for a document is received, the response generation system 100 can identify the document corresponding to the user input as the target of analysis. The following explanation assumes that user input for a document is received.

[0121] As shown in Figure 9, the response generation system 100 can identify the document 30 to be analyzed. The method (or format or criteria) for identifying the document 30 to be analyzed in this invention may vary.

[0122] In one embodiment, the response generation system 100 can identify the input document as the document to be analyzed 30 based on the fact that at least one document corresponding to the user's selection is input to the document upload page (or interface) provided on the service page 1000 from among the documents stored (or embedded) in the storage (or memory or storage space or database) of the user terminal 10. In another embodiment, the response generation system 100 can receive document link information (e.g., URL) or link information to an external storage service (e.g., Google Drive, Dropbox, etc.) that stores the document from the user terminal 10. The response generation system 100 can also identify the document to be analyzed 30 by directly accessing the document via the document link information or by downloading the document.

[0123] However, the method for identifying the document to be analyzed in the present invention is not necessarily limited to the embodiments described above. For the sake of explanation, the following description will assume that the document received via the user terminal 10 on which the service page 1000 was output was identified as the document to be analyzed 30. Once the document to be analyzed 30 is identified, the response generation system 100 can use the document understanding model 300 to extract various forms of content from the document to be analyzed 30. Here, the various forms of content extracted from the document to be analyzed 30 can be understood as content that satisfies pre-set content criteria.

[0124] As described above, based on the fact that the purpose of using the response generation system 100 is chemistry, biotechnology, new materials, new substances, and new drug development, the pre-set content criteria may be determined to be content related to at least one of chemistry, biotechnology, new materials, new substances, and new drug development. This allows the document understanding model 300 to extract multiple content pieces 31 related to chemistry, biotechnology, new materials, new substances, and new drug development from the document 30 under analysis, based on pre-defined content criteria. For example, the multiple content pieces 31 may include at least one of the following: text, molecular structures, mathematical formulas, charts, tables, and images.

[0125] Furthermore, once multiple contents 31 are extracted from the document comprehension model 300, the processor can store the extracted contents 31 in the storage unit 140 (or memory). On the other hand, the response generation system 100 (or processor) can analyze the relationships between multiple contents 31 stored in the memory unit 140 (or memory). Here, relationships refer to the semantic connections between different contents (e.g., molecular structures, text, mathematical formulas, tables, etc.), and can mean relationships based on semantic, thematic, and / or structural similarities that connect different contents to one another. Such relationships can be analyzed based on the meaning of each content.

[0126] Specifically, the response generation system 100 can perform a relationship analysis 32 between multiple content items 31 based on the meaning each of the multiple content items 31 possesses. For example, suppose the following are extracted as multiple content items 31: first molecular structure, second molecular structure, first text, second text, first formula, second formula, first table, and second table. Through the relationship analysis 32 of the multiple content items 31, the response generation system 100 can identify that there is a relationship between the first molecular structure, first text, first formula, and first table, and that there is a relationship between the second molecular structure, second text, second formula, and second table. Furthermore, the response generation system 100 can group multiple contents 31. The grouping in this invention may be performed among multiple contents 31 that are related to each other.

[0127] The response generation system 100 can group related content 31 from among multiple content 31 based on the relationships between them. More specifically, the response generation system 100 can group together content 33 that is related to the same molecular structure from among the multiple content 31, including at least one text, molecular structure, mathematical formula, chart, table, and image, and generate the grouped content 34.

[0128] In one embodiment, the response generation system 100 can group a first text, a first mathematical formula, and a first table from among a plurality of contents 31 that contain content related to the first molecular structure as content related to the first molecular structure 33, and generate the grouped first content. In another embodiment, the response generation system 100 can group the second text, second formula, and second table from among the multiple contents 31 that contain content related to the second molecular structure as content related to the second molecular structure, and generate the grouped second content.

[0129] Furthermore, the response generation system 100 (or processor) can store the grouped content in the storage unit 140 (or memory) in conjunction with the user account. Through the process described above, the grouped content 34 based on a specific molecular structure may include at least one of the following: a molecular structure image of the specific molecular structure corresponding to the grouped content 34, the name of the molecular structure, a description of the molecular structure, physical properties of the molecular structure (e.g., molecular weight, density, melting point, boiling point, flash point, surface tension, etc.), and a string of characters according to the SMILES notation for the molecular structure.

[0130] On the other hand, at least a portion of the content 34 grouped for a specific molecular structure may include content generated by a pre-trained predictive model. Specifically, at least a portion of the content 34 grouped for a particular molecular structure may include content generated by at least one of the following: a super-large-scale foundational model 200, a pre-trained chemical reaction prediction model 400, and a pre-trained molecular property prediction model 500.

[0131] In one embodiment, it is assumed that multiple contents 31 extracted from the document 30 to be analyzed contain molecular structure images and names of specific molecular structures, but do not contain descriptions of those specific molecular structures. The response generation system 100 can generate descriptions of specific molecular structures using a pre-trained chemical reaction prediction model 400. Furthermore, the response generation system 100 can group the molecular structure images and names of specific molecular structures extracted from the document 30 to be analyzed with the descriptions of those molecular structures generated by the chemical reaction prediction model 400, and generate grouped contents 34. In another embodiment, it is assumed that the multiple contents 31 extracted from the document 30 to be analyzed contain a molecular structure image, name, and description of a specific molecular structure, but do not contain physical properties of that specific molecular structure. The answer generation system 100 can generate the physical properties of the specific molecular structure using a pre-trained molecular property prediction model 500. The answer generation system 100 can also group the molecular structure image, name, and description of the specific molecular structure extracted from the document 30 to be analyzed with the physical properties of the specific molecular structure generated by the molecular property prediction model 500, and generate the grouped contents 34.

[0132] In other words, the response generation system 100 can generate content not included in the document to be analyzed using at least one of the ultra-large-scale infrastructure model 200, the chemical reaction prediction model 400, and the molecular property prediction model 500, and generate grouped content 34 that includes content generated by at least one of the models 200, 400, and 500. On the other hand, the response generation system 100 can assign labels to at least some of the multiple contents 31 by performing labeling 35. Here, at least some of the multiple contents 31 to which labels are assigned may correspond to the contents 34 grouped by grouping 33.

[0133] In this case, the grouped content 34, which includes related content, may be assigned the same label. Specifically, the response generation system 100 can assign the same label to grouped content 34 by performing labeling 35. For example, the response generation system 100 can assign a first label to first content grouped based on a first molecular structure, and a second label to second content grouped based on a second molecular structure, by performing labeling 35.

[0134] In this regard, as described above, in the present invention, if there are multiple grouped contents 34, each of the multiple grouped contents 34 may be assigned a different label from the others (for example, the first grouped content may be assigned a first label, and the second grouped content may be assigned a second label). However, as described above, in the present invention, the objects to which labels are applied are described as grouped content, but the invention is not necessarily limited to this. In addition to grouped content, the present invention may also be configured to apply labels to each of the contents that have independent meanings.

[0135] Furthermore, grouped content with different labels assigned to each other may be stored in the storage unit 140 in conjunction with a user account. On the other hand, the response generation system 100 can provide the grouped content 34 stored in the memory unit 140 to the user terminal 10 on which the service page 1000 is output.

[0136] Specifically, the response generation system 100 can provide a graphic object corresponding to each of the grouped contents 34 that have been labeled by the labeling process 35 to an area of ​​the service page 1000 where the user query is received (see, for example, Figure 13). Furthermore, the response generation system 100 can receive user queries corresponding to user input via a region of the service page 1000.

[0137] Here, the user query 36 may include labels (or label information) assigned to the grouped content 34, or information that can represent the molecular structure (e.g., the name of the molecular structure, the chemical formula of the molecular structure, etc.). For the sake of explanation, the following description assumes that a user query 36 containing label information (for example, "Can you predict the chemical reaction between the first label m1 and the second label m2?") has been received. However, in this invention, the information contained in the user query 36 is not limited to just one of the items; any information that can represent a specific molecule (or compound or material) may be included in the user query.

[0138] The response generation system 100 can process the user query 36 as input to the ultra-large-scale infrastructure model 200, based on the fact that the user query 36 has been received. The ultra-large-scale infrastructure model 200 can receive a user query 36 as input, understand the content contained in the user query 36, and identify specific content related to the user query 36.

[0139] More specifically, the ultra-large-scale infrastructure model 200 can extract labels assigned to grouped content 34 from the user query 36 through analysis of the user query 36, and identify specific grouped content 37 corresponding to the extracted labels. For example, based on the analysis of the user query 36, the ultra-large-scale infrastructure model 200 can identify the first content corresponding to the first label and the second content corresponding to the second label as specific grouped content 37 related to the user query 36, based on the fact that the user query 36 contains content corresponding to a first label and a second label that represent specific grouped content 37. Furthermore, the ultra-large-scale infrastructure model 200 can process specific content (i.e., the molecular structure of a specific group of content) as input to a pre-trained chemical reaction prediction model 400.

[0140] At this time, the ultra-large-scale base model 200 can change the names of molecular structures contained in specific grouped contents 37 into a language that the computer can understand. More specifically, the ultra-large-scale base model 200 can convert (or change) the names of molecular structures contained in a specific grouped content 37 into strings conforming to SMILES notation, a language that computers can understand, and process the converted strings and information about the specific grouped content 37 as input to a chemical reaction prediction model 400 that understands chemical reaction mechanisms. For example, the ultra-large-scale base model 200 can convert the names of the first and second molecular structures contained in each of the specific grouped content 37 into strings conforming to SMILES notation, and input the converted strings and information about the first and second molecular structures into a pre-trained chemical reaction prediction model 400.

[0141] The chemical reaction prediction model 400 can predict the chemical reactions between molecules of specific grouped content 37 and output the predicted results as output data. As described above, the chemical reaction prediction model 400, which has been input with converted strings and information on the first and second molecular structures from the ultra-large scale base model 200, can predict the chemical reactions (or synthesis results) between the first molecule corresponding to the first molecular structure and the second molecule corresponding to the second molecular structure, and can output the predicted chemical reaction results 38 between the first and second molecules. In one embodiment, the predicted chemical reaction may include a third molecular structure produced as a result of a chemical reaction between a first molecular structure and a second molecular structure, and may include at least one of the following: chemical properties of the third molecular structure, reaction conditions, predicted yield, reaction energy, reaction pathway, and predicted reaction time.

[0142] On the other hand, the ultra-large-scale infrastructure model 200 can generate a response 39 to a user query 36 using the output data (chemical reaction prediction results 38) of the chemical reaction prediction model 400 and the contents that make up the grouped content (or specific grouped content). At this point, the ultra-large-scale infrastructure model 200 can determine what procedures and tools to use to generate the answer 39 to the user query 36. More specifically, the ultra-large-scale infrastructure model 200 can determine the answer generation procedure to be performed for the prediction corresponding to the user query 36, and the tools to be used in the answer generation procedure.

[0143] In this case, the response generation system 100 can provide the service page 1000 with information regarding the response generation procedure determined from the ultra-large-scale infrastructure model 200, and the tools used in the response generation procedure (see, for example, Figure 17). The ultra-large-scale infrastructure model 200 can perform actions to generate an answer 39 to user query 36 based on the determined answer generation procedure and tools. The ultra-large-scale infrastructure model 200 can generate an answer 39 to user query 36 using the output data of the chemical reaction prediction model 400 described above, the contents constituting the specific grouped content 37, and the determined answer generation procedure and tools.

[0144] At this time, the response generation system 100 can assign a new label (e.g., third label M3) to the new molecular structure by labeling the new molecular structure, based on the fact that the response 39 generated from the ultra-large-scale base model 200 includes a new molecular structure (e.g., third molecular structure). Based on the fact that a response 39 has been generated for user query 36, the response generation system 100 can provide the response 39 generated from the ultra-large-scale infrastructure model 200 via the user terminal 10 on which the service page 1000 was output.

[0145] Meanwhile, the response generation system 100 can receive new (or additional) user queries via the service page 1000. The response generation system 100 can receive input for a new user query 40 if a new user query 40 is entered from the user terminal 10 after a response 39 for the user query 36 has been provided.

[0146] For example, a new user query 40 may be a query that includes content related to at least one molecular structure to which a label has been assigned, or a query that includes content related to other molecules to which no label has been assigned. For the sake of explanation, the following description will assume that a new user query 40 has been received that includes a specific molecular structure 41 (e.g., a third molecular structure) to which a new label (e.g., a third label M3) has been assigned. Based on the receipt of a new user query 40 that includes the label M3 assigned to the third molecular structure 41, the response generation system 100 can input the new user query 40 into the ultra-large-scale infrastructure model 200.

[0147] The ultra-large-scale base model 200 can leverage at least one predictive model to understand a new user query 40 and generate an answer to the new user query 40. In one embodiment, the ultra-large-scale base model 200 can input information about the third molecular structure 41 into the molecular property prediction model 500 based on the fact that the user query 40 includes the question, "Can you predict the surface tension of m3?". The molecular property prediction model 500 can predict the physical properties (e.g., surface tension) of the third molecular structure 41 corresponding to the new user query 40. The molecular property prediction model 500 can also output the predicted properties 42 of the third molecular structure 41.

[0148] In one embodiment, the predicted physical properties 42 may include at least one of the following: surface tension, boiling point and melting point, density, solubility, viscosity, thermal properties, mechanical properties, and electrical properties of the third molecular structure 41. The ultra-large-scale infrastructure model 200 determines the answer generation procedure to be executed for predictions corresponding to the new user query 40, and the tools used in the answer generation procedure. Using the determined answer generation procedure and tools, along with the output data of the molecular property prediction model 500, it can generate an answer 43 to the user query 40 (for example, "The surface tension of m3 is XX...").

[0149] Furthermore, the response generation system 100 can provide the responses 43 generated from the ultra-large-scale infrastructure model 200 to the user terminal 10. Thus, the present invention generates and provides answers suitable for user queries, thereby proposing the optimal research method to the user and minimizing the risk of research failure. Furthermore, by providing predictive information about user queries using a pre-trained predictive model, it is possible to support user decision-making and provide users with the necessary information, thereby increasing the efficiency of research.

[0150] Below, we will explain in more detail the method for generating answers for the ultra-large-scale infrastructure model, based on the above-mentioned method for generating answers for the ultra-large-scale infrastructure model and the overall process of the system. First, the present invention allows for the identification of multiple molecular structures to be predicted based on user input received via a service page (see S1010, Figure 10).

[0151] The response generation system 100 according to the present invention can be implemented in the form of various platforms such as applications, software, and websites. For the convenience of explanation, this specification does not limit the form in which the response generation system 100 is implemented to any one of these forms. In the present invention, the response generation system 100 is also called a "response generation platform." The user described above may have a user account pre-registered with the response generation system 100 according to the present invention. In this case, the account may be generated through a page (or screen) linked to the response generation system 100. Alternatively, the account may be generated in at least one other system linked to the response generation system 100. However, in this specification, without distinguishing the system in which the user account was issued, any account that can use the various services (or functions) provided by the response generation system 100 according to the present invention is referred to as an "account pre-registered with the response generation system 100".

[0152] On the other hand, in this invention, inputting "molecular structure" can be understood as inputting information that can identify a molecule. In this case, the information that can identify a molecular structure may take various forms, such as molecular structural formulas, molecular graphs, chemical formulas, molecular structural formulas according to SMILES notation, and molecular structural images. As shown in Figure 11, the response generation platform based on the ultra-large-scale infrastructure model 200 can provide a service page 1000 linked to the platform to the user terminal 10.

[0153] The response generation system 100 can identify multiple molecular structures to be predicted based on user input received via the service page 1000. As mentioned above, user input may include at least one of the following: document, image, audio, video, and text. The following explanation assumes that user input for a document is received.

[0154] The response generation system 100 can receive user input for at least one document from the user terminal 10 to which the service page 1000 is provided. At this time, the response generation system 100 can receive one or more documents (i.e., multiple documents) from the user terminal 10, and for the sake of explanation, this specification will be described assuming that one document has been received. The response generation system 100 can provide (or display) a graphic object 601 linked to a document input function in an area of ​​the service page 1000 in order to receive user input for a document. For example, when the graphic object 601 is selected from the user terminal 10, the response generation system 100 can launch (or output) a document upload page (or window) to the user terminal 10. The user can select a document via the document upload page or upload a document using the drag and drop method. The response generation system 100 can receive user input for a document based on the input of a document corresponding to the user's selection.

[0155] However, in the present invention, user input to a document is not necessarily limited to the embodiments described above. For example, a user can input link information to a document (e.g., a URL) or link information to an external storage service (e.g., Google Drive, Dropbox, etc.) that stores the document. In this case, the response generation system 100 can access the document directly via the link information or download the document and receive the user's input to the document. Furthermore, the response generation system 100 can identify documents received from the user terminal 10 as documents to be analyzed. For example, as shown in Figure 12, the response generation system 100 can identify a document 600 corresponding to user input as a target for analysis using the document understanding model 300.

[0156] Once the document to be analyzed is identified, the response generation system 100 can identify at least one molecular structure to be predicted from the document 600. Specifically, the response generation system 100 can use a document understanding model 300 to extract multiple pieces of content (or various pieces of content related to molecular structure or information about molecular structure) from the document 600 to be analyzed, and identify the molecular structure to be predicted based on the extracted content.

[0157] As described above, the document understanding model 300 can extract multiple pieces of content from at least one document that satisfy pre-defined content criteria. Here, the pre-defined content criteria can be understood as content related to molecular structure relating to at least one of the following: chemistry, biotechnology, new materials, new substances, and new drug development. Therefore, the response generation system 100 can use the document understanding model 300 to extract content related to molecular structure relating to at least one of the following: chemistry, biotechnology, new materials, new substances, and new drug development from the document. The document understanding model 300 understands the content contained in the document 600 to be analyzed, and based on the results of this understanding, it can extract various content (or information) related to molecular structure from the document 600 to be analyzed.

[0158] In one embodiment, as shown in Figure 12, if the document to be analyzed 600 contains a first molecular structure 611 and a second molecular structure 621, the document understanding model 300 can understand the first molecular structure 611 and the second molecular structure 621, and based on the results of the understanding, can extract the first molecular structure 611 and the second molecular structure 621 contained in the document to be analyzed 600. In this case, the response generation system 100 can identify the first molecular structure 611 and the second molecular structure 621 extracted using the document understanding model 300 as the molecular structures to be predicted. In another embodiment, if the document 600 to be analyzed includes a first molecular graph for a first molecular structure 611 and a second molecular graph for a second molecular structure 621, the document understanding model 300 can understand the first molecular graph and the second molecular graph and, based on the understanding, extract the first molecular graph and the second molecular graph included in the document 600 to be analyzed. In this case, the response generation system 100 can identify the first molecular structure 611 for the first molecular graph and the second molecular structure 621 for the second molecular graph as the molecular structures to be predicted.

[0159] Furthermore, the response generation system 100 can use the document understanding model 300 to extract at least one of the following content items from the document 600 to be analyzed: text, mathematical formulas, charts, tables, and images. For example, the document understanding model 300 can understand the texts 612, 613, 614, 615, 622, 623, 624, 625, 632, 633, 634, and 635 contained in the document 600 to be analyzed, and based on the results of this understanding, can extract the texts 612, 613, 614, 615, 622, 623, 624, 625, 632, 633, 634, and 635 contained in the document 600 to be analyzed. Furthermore, the present invention allows for the process of storing information on multiple molecular structures in memory (see S1020, Figure 10).

[0160] Multiple contents extracted from the document comprehension model 300 may be stored in the storage unit 140 (or memory). More specifically, the answer generation system 100 can store various information about the extracted molecular structures 611, 621, and 631 in the storage unit 140 (or memory). For example, the answer generation system 100 can store the extracted molecular structures 611, 621, and 631, as well as the texts 612, 613, 614, 615, 622, 623, 624, 625, 632, 633, 634, and 635 extracted along with the molecular structures 611, 621, and 631, in the storage unit 140.

[0161] On the other hand, the response generation system 100 can analyze the relationships between multiple contents based on the meaning of each of the multiple contents stored in the storage unit 140. That is, the response generation system 100 or the processor of the system 100 can perform the series of processes described in the present invention using the contents stored in the storage unit 140 (or memory). Here, relational analysis can be understood as the process of identifying and understanding the interrelationships (or connections) between multiple pieces of content based on the meaning each piece of content possesses. This may include the process of grasping and grouping the similarities, interdependencies, or associated meanings of the information expressed by each piece of content, or deriving specific patterns.

[0162] The answer generation system 100 can analyze the meaning of each of the multiple contents and identify (or extract) the meaning of each of the multiple contents. At this time, the analysis of the meaning of each of the multiple contents can be performed by at least one of the ultra-large scale foundation model 200, the chemical reaction prediction model 400, and the molecular property prediction model 500. For example, the answer generation system 100 analyzes the meaning of each of the texts 612, 613, 614, 615, 622, 623, 624, 625, 632, 633, 634, and 635 extracted from the document understanding model 300, and based on the analysis results, the meaning of each of the texts 612, 613, 614, 615, 622, 623, 624, 625, 632, 633, 634, and 635 is the name of the first molecular structure 612, the It can be identified that it has a description of the first molecular structure 613, SMILES notation for the first molecular structure 614, physical properties of the first molecular structure 615, a name for the second molecular structure 622, a description of the second molecular structure 623, SMILES notation for the second molecular structure 624, physical properties of the second molecular structure 625, a name for the third molecular structure 632, a description of the third molecular structure 633, SMILES notation for the third molecular structure 634, and physical properties of the third molecular structure 635. Furthermore, the response generation system 100 can identify related content based on the meaning of each of the identified content items. More specifically, the response generation system 100 can identify the relationships between multiple molecular structures 611, 621, and 631 extracted from the document understanding model 300 and the names 612, 613, SMILES notation 614, and 615 of the first molecular structure, which have different meanings from each other; the names 622, 623, SMILES notation 624, and 625 of the second molecular structure; the names 632, 633, SMILES notation 634, and 635 of the third molecular structure. For example, the answer generation system 100 can identify that the first molecular structure 611 is related to the name 612 of the first molecular structure, the description 613 of the first molecular structure, the SMILES notation 614 of the first molecular structure, and the physical properties 615 of the first molecular structure, and that the second molecular structure 621 is related to the name 622 of the second molecular structure, the description 623 of the second molecular structure, the SMILES notation 624 of the second molecular structure, and the physical properties 625 of the second molecular structure. Furthermore, the answer generation system 100 can identify that the third molecular structure 631 is related to the name 632 of the third molecular structure, the description 633 of the third molecular structure, the SMILES notation 634 of the third molecular structure, and the physical properties 635 of the third molecular structure.

[0163] On the other hand, the response generation system 100 can group content related to the same molecular structure among multiple contents based on the relationships between them. More specifically, based on the identified relationships, the response generation system 100 can group the name 612 of the first molecular structure, the description 613 of the first molecular structure, the SMILES notation 614 of the first molecular structure, and the physical properties 615 of the first molecular structure, which contain content related to the first molecular structure 611, as content related to the first molecular structure 611. Furthermore, based on the identified relationships, the response generation system 100 can group the name 622 of the second molecular structure, the description 623 of the second molecular structure, the SMILES notation 624 of the second molecular structure, and the physical properties 625 of the second molecular structure, which contain content related to the second molecular structure 621, as content related to the second molecular structure 621. Furthermore, the response generation system 100 can group the name 632 of the third molecular structure, the description 633 of the third molecular structure, the SMILES notation 634 of the third molecular structure, and the physical properties 635 of the third molecular structure, which contain content related to the third molecular structure 631, based on the identified relationships. Through the grouping process described above, at least one grouped content can be generated (or extracted). Referring to Figure 12, the first molecular structure 611, the name of the first molecular structure 612, the description of the first molecular structure 613, the SMILES notation for the first molecular structure 614, and the physical properties of the first molecular structure 615 are grouped together to generate the grouped first content 610. Furthermore, the second molecular structure 621, the name of the second molecular structure 622, the description of the second molecular structure 623, the SMILES notation for the second molecular structure 624, and the physical properties of the second molecular structure 625 are grouped together to generate the grouped second content 620. In addition, the third molecular structure 631, the name of the third molecular structure 632, the description of the third molecular structure 633, the SMILES notation for the third molecular structure 634, and the physical properties of the third molecular structure 635 are grouped together to generate the grouped third content 630.

[0164] The grouped contents 610, 620, and 630, each based on one of the multiple molecular structures 611, 621, and 631, may include at least one of the following: a molecular structure image of the specific molecular structure corresponding to each grouped content 610, 620, and 630; a molecular structure name 612, 622, and 632; a molecular structure description 613, 623, and 633; ​​a string 614, 624, and 634 of the molecular structure according to the SMILES notation; and a molecular structure property 615, 625, and 635. Furthermore, the grouped contents 610, 620, and 630 may be stored in the storage unit 140 in conjunction with the user account.

[0165] On the other hand, in the present invention, a molecular graph relating to multiple molecular structures can be obtained by converting atoms into nodes and interatomic bonds into edges based on multiple molecular structures stored in memory (see S1030, Figure 10). Here, the molecular graph may include nodes and edges corresponding to the atoms and interatomic bonds contained in the molecular structure. That is, in a molecular graph, atoms can be represented as nodes and interatomic bonds as edges.

[0166] The answer generation system 100 can obtain a molecular graph relating to molecular structures by transforming the molecular structures. More specifically, based on the multiple molecular structures stored in the memory unit 140, the answer generation system 100 can obtain a molecular graph relating to each of the multiple molecular structures by transforming the atoms contained in each of the multiple molecular structures into nodes and the bonds between atoms into edges. In one embodiment, as shown in Figure 12, the answer generation system 100 converts the atoms constituting the first molecular structure 611 stored in the memory unit 140 into nodes 611a, 611b, and 611c, and the bonding relationships between the atoms constituting the first molecular structure 611 into edges 611d, 611e, and 611f, thereby obtaining a first molecular graph including nodes 611a, 611b, 611c and edges 611d, 611e, and 611f corresponding to the first molecular structure 611.

[0167] In another embodiment, the answer generation system 100 can obtain a second molecular graph including nodes 621a, 621b, 621c and edges 621d, 621e, 621f corresponding to the second molecular structure 621 by converting the atoms constituting the second molecular structure 621 stored in the memory unit 140 into nodes 621a, 621b, 621c, and the bonding relationships between the atoms constituting the second molecular structure 621 into edges 621d, 621e, 621f. However, in the present invention, the molecular graph may be obtained using the document understanding model 300, or obtained (or extracted) from various information related to molecular structure stored in the memory unit 140.

[0168] In one embodiment, if the document 600 to be analyzed contains a first molecular structure 611 and a second molecular structure 621, the document understanding model 300 understands the first molecular structure 611 and the second molecular structure 621, and based on the results of this understanding, it can obtain molecular graphs for each of the first molecular structure 611 and the second molecular structure 621 through a process of converting the atoms contained in each of the first molecular structure 611 and the second molecular structure 621 into nodes 611a, 611b, 611c, 621a, 621b, and 621c, and the bonds between atoms into edges 611d, 611e, 611f, 621d, 621e, and 621f. In another embodiment, if the text extracted from the document understanding model 300 includes descriptions of the first molecular structure 611 and the second molecular structure 621, the response generation system 100 can extract molecular graphs for the first molecular structure 611 and the second molecular structure 621, respectively, from the storage unit 140 based on the respective descriptions of the first molecular structure 611 and the second molecular structure 621.

[0169] In other words, as described above, there are various methods (or schemes) for obtaining a molecular graph in the present invention, and for the sake of convenience of explanation, the following explanation will assume that the molecular graph was obtained through the conversion process by the answer generation system 100. On the other hand, the response generation system 100 can label each of the grouped contents 610, 620, and 630 stored in the memory unit 140 so that each of the grouped contents 610, 620, and 630 is assigned a label.

[0170] The response generation system 100 can label multiple molecular structures 611, 621, and 631 such that each of the multiple molecular structures 611, 621, and 631 is assigned a different label from the others. In one embodiment, the response generation system 100 can assign a first label M1 to the first molecular structure 611 and a second label M2 to the second molecular structure 621 by labeling the first molecular structure 611 and the second molecular structure 621.

[0171] In another embodiment, the response generation system 100 can assign a first label M1 to a first content 610 grouped based on a first molecular structure 611 by labeling a plurality of molecular structures 611, 621, and 631, and assign a second label M2, different from the first label M1, to a second content 620 grouped based on a second molecular structure 621. Furthermore, the response generation system 100 can assign a third label M3, different from the first and second labels M2, to a third content 630 grouped based on a third molecular structure 631. In this case, labeling of molecular structures can be understood as assigning the same label to content grouped based on a specific molecular structure (i.e., all content included in the grouped content has the same label). The grouped contents 610, 620, and 630, each assigned a different label, may be stored in the storage unit 140 in conjunction with the user account. More specifically, the response generation system 100 can store in the storage unit 140 (or memory) information regarding a first molecular graph obtained through the process of acquiring molecular graphs, and the label M1 assigned to the first molecular graph, and information regarding a second molecular graph obtained through the process of acquiring molecular graphs, and the label M2 assigned to the second molecular graph.

[0172] On the other hand, the response generation system 100 can provide the grouped contents 610, 620, and 630 stored in the memory unit 140 to the user terminal 10 on which the service page 1000 is output. The response generation system 100 can provide a graphic object corresponding to each of the labeled contents to a region of the service page 1000 on which the user query is received. Here, the labeled content may correspond to content related to the molecular structures extracted from the aforementioned analyzed document 600 (e.g., first molecular structure 611, second molecular structure 621, third molecular structure 631) (e.g., images of the molecular structures, names of the molecular structures, descriptions of the molecular structures, SMILES notation of the molecular structures, physical properties of the molecular structures, etc.).

[0173] In this regard, the response generation system 100 can provide multiple graphic objects corresponding to each of the multiple molecular structures 611, 621, and 631, each assigned a different label, in a region of the service page 1000 where the user query is received. As shown in Figure 13, the service page 1000 may include at least one of the following: a first area 710 which provides information on multiple molecular structures identified from the document 600 to be analyzed; a second area 720 which provides at least a portion of the document or answers to user queries; and a third area 730 which receives user queries.

[0174] First, the first area 710 of the service page 1000 may contain at least one grouped content that has been labeled. The first area 710 may display information about identified (or extracted) molecular structures, and such information may be provided in various forms such as molecular graphs, text, and images. Specifically, the first region 710 may include at least one graphic object 711, 712, 713 corresponding to extracted molecular structures that have been assigned different labels by labeling (for example, a first molecular structure 611 with a first label M1, a second molecular structure 621 with a second label M2, and a third molecular structure 631 with a third label M3), and at least one of the details relating to the extracted molecular structures 611, 621, 631.

[0175] The first area 710 of the service page 1000 may include a first sub-area 710a containing graphic objects 711, 712, and 713, and a second sub-area 710b containing detailed information. In this regard, if multiple molecular structures 611, 621, and 631 are identified from document 600, the first sub-region 710a may include multiple graphic objects 711, 712, and 713 corresponding to each of the multiple molecular structures. More specifically, among the multiple graphic objects, the first graphic object 711 may include a graph of the first molecular structure 611 corresponding to the first graphic object 711 among the multiple molecular structures 611, 621, and 631, and the second graphic object 712 may include a graph of the second molecular structure 621 corresponding to the second graphic object 712. In this case, at least some of the graphic objects may include molecular structure images of the molecular structures.

[0176] Furthermore, the second sub-region 710b may be provided with detailed information about the molecular structure corresponding to one of the multiple graphic objects 711, 712, and 713 selected by user input. The response generation system 100 can provide detailed information about the graphic object selected by user input based on the user input receiving user input to select one of the multiple graphic objects 711, 712, and 713. In this regard, each of the multiple graphic objects 711, 712, and 713 contained in the first sub-region 710a may have linked (or linked) detailed information of multiple molecular structures 611, 621, and 631 corresponding to each of the multiple graphic objects 711, 712, and 713. For example, suppose a first graphic object 711 corresponding to a first molecular structure 611 is selected from the user terminal 10. Based on the selection of a first graphic object 711 contained in the first sub-region 710a by the user terminal 10, the response generation system 100 can provide the second sub-region 710b with detailed information 711a, 711b, 711c, 711d, and 711e regarding the first molecular structure 611 linked to the first graphic object 711, as well as information regarding the first label M1 assigned to the first molecular structure.

[0177] Here, detailed information regarding the molecular structure may include at least one of the following: a molecular structure image 711a of the molecular structure, a name of the molecular structure 711b, a description of the molecular structure 711c, a string of characters according to SMILES notation 711d, and physical properties of the molecular structure 711e. In this case, the molecular structure image of the molecular structure may also be provided (or displayed) in molecular graph form obtained through the process of acquiring a molecular graph. On the other hand, detailed information (or at least one piece of content included in the grouped content) may be configured to be extracted from the document or obtained from at least one pre-trained predictive model. As described above, the pre-trained predictive model may include at least one of a chemical reaction prediction model 400 that predicts chemical reactions between molecular structures and a molecular property prediction model 500 that predicts the physical properties of molecular structures.

[0178] For example, if multiple contents extracted from the document 600 to be analyzed contain a molecular structure image, name, description, and a string of characters according to SMILES notation for the first molecular structure 611, but do not contain any physical properties of the first molecular structure 611, the answer generation system 100 can predict the physical properties of the first molecular structure 611 using a pre-trained molecular property prediction model 500. The molecular property prediction model 500 outputs the predicted physical properties of the first molecular structure 611 as output data, and the answer generation system 100 can obtain the predicted physical properties of the first molecular structure 611 and generate detailed information about the first molecular structure 611. In this case, the molecular structure image 711a, name 711b, description 711c, and SMILES notation 711d of the first molecular structure 611 included in the first area 710 of service page 1000 can be understood as having been extracted from document 600, and the physical properties 711e of the first molecular structure 611 can be understood as having been generated by the molecular property prediction model 500. Next, the second area 720, which is separated from the first area 710 of the service page 1000, may be provided with the document 600 received from the user terminal 10.

[0179] In the second area 720, highlighting objects corresponding to each of the molecular structures 611, 621, and 631 may overlap in a region of the document provided to the service page 1000, so that it can be identified that multiple molecular structures 611, 621, and 631 were extracted from document 600. More specifically, in the second area 720, highlighting objects 721 and 722 may be displayed overlapping in the first area 720a (or first sub-area) containing the first molecular structure 611 and the second molecular structure 621 of the document provided to the service page 1000, respectively, so that it can be identified that the first molecular structure 611 and the second molecular structure 621 were extracted from document 600. Here, in the first region 720a containing the first molecular structure 611, a first label M1 corresponding to the first molecular structure 611 may be provided around the first highlight object 721 overlapping the first region 720a. In addition, in the second region 720b containing the second molecular structure 621, a second label M2 corresponding to the second molecular structure 621 may be provided around the second highlight object 722 overlapping the second region 720b.

[0180] Furthermore, the highlight objects 721 and 722 may be displayed in a way that visually emphasizes them in the user interface (e.g., the service page 1000) and distinguishes them from other objects. For example, the response generation system 100 may display the highlight objects 721 and 722 in a way that distinguishes them from other objects on the service page 1000 by changing the color of the highlight objects 721 and 722, adding a border, or changing the background color. On the other hand, when a highlight object is selected by user input, the other areas of the service page 1000, which are separated from the area where the highlight object is displayed, can be provided with information about the specific molecular structure corresponding to the selected highlight object.

[0181] Specifically, the response generation system 100 can provide detailed information about a specific molecular structure linked to the highlighted object selected by the user input to the first area 710 of the service page 1000, based on the user input that selects one of several highlighted objects 721, 722. For example, suppose user input is received for the first highlighted object 721 in the first area 720a, which contains the first molecular structure 611. Based on the user input that selects the first highlighted object 721, the response generation system 100 can provide detailed information 711a, 711b, 711c, 711d, 711e about the first molecular structure 611 linked to the first highlighted object 721 to the first area 710. In this case, depending on the selected highlight object, the display of multiple graphic objects 711, 712, and 713 contained in the first area 710 of the service page 1000 may also be changed. More specifically, when the first highlight object 721 is selected, the first graphic object 711 corresponding to the first highlight object 721 may be highlighted in the first sub-area 710a of the first area 710 so that it can be identified that the first highlight object 721 has been selected by the user.

[0182] In other words, when a highlight object is selected, the first area 710 may display a graphic object corresponding to the selected highlight object as a first visual appearance, so that the user can intuitively recognize it. In contrast, the graphic object corresponding to an unselected highlight object may be displayed as a second visual appearance. In this way, when a user selects a highlight object, the other areas (the first area) that are separated from the area where the highlight object is displayed (the second area) can be provided (or displayed) with information about the specific molecular structure linked to the highlight object.

[0183] In other words, the present invention provides information visually through graphic objects and labels, enabling users to easily understand and utilize data on complex molecular structures. This can enhance user convenience and comprehension, and improve research efficiency. On the other hand, the response generation system 100 can receive editing requests for multiple molecular structures via a service page 1000 that provides answers to user queries.

[0184] The response generation system 100 can provide graphic objects in one area of ​​the service page 1000 that are linked to a function for receiving edit requests for multiple molecular structures. For example, as shown in Figure 13, the response generation system 100 can provide a graphic object 714 in the first area 710 of the service page 1000, where a molecular structure image corresponding to the selected graphic object (first graphic object 711) is displayed, that is linked to a function for receiving edit requests for a molecular structure (first molecular structure 611) corresponding to the molecular structure image (or graph). As another example, although not shown in the figures, the response generation system 100 can provide a graphic object for each of the multiple graphic objects 711, 712, and 713 provided to the first region 710, which is linked to a function for receiving edit requests for multiple molecular structures. Based on the receipt of user input selecting one of the graphic objects provided for each of the multiple graphic objects 711, 712, and 713, the response generation system 100 can receive an edit request for a specific molecular structure corresponding to the selected graphic object.

[0185] As another example, although not shown in the figures, the response generation system 100 can provide each of the multiple highlight objects 721, 722 provided in the second region 720 with a graphic object linked to a function for receiving edit requests for multiple molecular structures. Based on the receipt of user input selecting one of the graphic objects provided for each of the multiple highlight objects 721, 722, the response generation system 100 can receive an edit request for a specific molecular structure corresponding to the selected highlight object. In other words, the present invention does not limit the method for receiving editing requests for molecular structures to just one. For the sake of explanation, the following description will assume that the graphic object 714 has been selected.

[0186] The response generation system 100 can receive editing requests for multiple molecular structures (e.g., a first molecular structure) from the user terminal 10 based on the selection of a graphic object 714 contained in the first region 710. Furthermore, as shown in Figure 14, the response generation system 100 can provide an editing interface 800 that provides editing functions for the first molecular structure 611 based on the receipt of an editing request for the first molecular structure 611 from the user terminal 10. The editing interface 800 may provide an editable molecular graph corresponding to either the first molecular structure or the second molecular structure obtained by molecular graph conversion from among multiple molecular structures. For example, based on receiving an editing request for the first molecular structure 611 from the user terminal 10, the response generation system 100 can provide the molecular structure graph 810 of the first molecular structure 611 on the editing interface 800 launched on the user terminal 10.

[0187] Here, the molecular structure image 810 can also be understood as a molecular graph including nodes 811a, 811b, 811c, 811d, 811e, 811f, 811g corresponding to each atom constituting the first molecular structure 611, and edges 812a, 812b, 812c, 812d, 812e representing the bonding relationships between atoms. The first molecular structure 611 may be configured to be edited based on user input for at least one of the nodes 811a, 811b, 811c, 811d, 811e, 811f, 811g and edges 812a, 812b, 812c, 812d, 812e. Specifically, editing the first molecular structure 611 may involve deleting or repositioning at least one of the nodes 811a, 811b, 811c, 811d, 811e, 811f, 811g corresponding to each atom constituting the first molecular structure 611, or edges 812a, 812b, 812c, 812d, 812e representing the bonding relationships between atoms, or it may involve adding a new node corresponding to a new atom, or adding a new edge that generates a new bonding relationship to an atom.

[0188] For example, the answer generation system 100 can activate a node and edge deletion mode based on the selection of a graphic object 801 that is linked to a node and edge deletion function included in a region of the editing interface 800. Alternatively, the answer generation system 100 may edit the first molecular structure 611 so that specific nodes 811b and 811e are deleted at the positions corresponding to the user input, based on the receipt of user input to select specific nodes 811b and 811e from among a plurality of nodes 811a, 811b, 811c, 811d, 811e, 811f, and 811g included in the image 810 of the first molecular structure. In this case, when editing is performed on the first molecular structure 611 to be edited based on user input, a molecular structure image 820 corresponding to the edited molecular structure, which is different from the molecular structure image 810 of the first molecular structure 611 before editing, may continue to be displayed on the editing interface 800. For example, based on the deletion of specific nodes 811b and 811e corresponding to the user's selection, a molecular structure image 820 with the specific nodes 811b and 811e deleted may be displayed on the editing interface 800.

[0189] Furthermore, the response generation system 100 can store the edited molecular structure 820 (or molecular structure image or molecular graph) in a pre-specified storage (e.g., storage unit 140 or memory) in conjunction with the user account. For example, based on the selection of a graphic object 802 linked to the edit save (or complete) function included on the editing interface 800 from the user terminal 10, the response generation system 100 can generate an edited molecular structure 820 from the first molecular structure 611, and store the edited molecular structure 820 in the storage unit 140 (or memory) in conjunction with the user account. Thus, the present invention provides a user environment that allows users to design their desired molecules through an editing interface.

[0190] On the other hand, the edited molecular structure may be assigned a new label to identify the edited molecular structure. The response generation system 100 can label the edited molecular structure stored in the memory unit 140 so that it is assigned a new label to identify the edited molecular structure. For example, as shown in Figure 15, the response generation system 100 can label the edited molecular structure 941 so that it is assigned a fourth label M4 that is different from the first label M1 assigned to the molecular structure before editing (e.g., the first molecular structure 911). The response generation system 100 can generate a fourth graphic object corresponding to the edited molecular structure 941 and provide the generated fourth graphic object to a region of the service page 1000.

[0191] Specifically, as shown in Figure 16, the first region 1010 (or first sub-region) of the service page 1000 may be provided with a graphic object 1014 (or fourth graphic object) corresponding to the edited molecular structure 941. Here, the graphic object 1014 corresponding to the edited molecular structure 941 may include a molecular structure image of the edited molecular structure 941. Furthermore, the first region 1010 (or first sub-region) of the service page 1000 may include a molecular structure image 1014a of the edited molecular structure 941. In addition, the surrounding region of the molecular structure image 1014a may also be provided with the fourth label M4 assigned to the edited molecular structure 941.

[0192] Furthermore, the graphic object 1014 corresponding to the edited molecular structure 941 may include additional detailed information about the edited molecular structure 941. For example, it may include at least one of the following: the name 1014b of the edited molecular structure 941, a description 1014c of the edited molecular structure 941, the SMILES notation 1014d of the edited molecular structure 941, and the physical properties 1014e of the edited molecular structure 941. In this case, at least one of the following may be generated by at least one of the pre-trained chemical reaction prediction model 400 and molecular property prediction model 500: the molecular structure image 1014a of the edited molecular structure 941, the name 1014b of the edited molecular structure 941, the description 1014c of the edited molecular structure 941, the SMILES notation 1014d of the edited molecular structure 941, and the physical properties 1014e of the edited molecular structure 941. For the sake of explanation, the edited molecular structure 941 will be referred to as "fourth molecular structure 941" below, and the detailed information of the edited molecular structure 941 will be referred to as "grouped fourth content 940" (see Figure 15).

[0193] On the other hand, the present invention allows for the reception of user queries for chemical reaction predictions related to multiple molecular structures via a service page (see S1040, Figure 10). The response generation system 100 can receive user queries via the service page 1000 that include at least one of the labels assigned by labeling. More specifically, the response generation system 100 can receive user queries for chemical reaction predictions related to multiple molecular structures to which labels have been assigned.

[0194] As shown in Figure 16, the third area 1030 of the service page 1000 may be configured to receive user queries. The third area 1030 may include a graphic object 1031 that works in conjunction with the user query receiving function. The response generation system 100 can receive user queries containing labels assigned to specific molecular structures via the third area 1030 of the service page 1000.

[0195] Specifically, the response generation system 100 can receive user queries that include the multiple molecular structures to which different labels have been assigned. For example, the response generation system 100 can receive a user query 1032 from the user terminal 10 (for example, "Can you predict the reaction between m2 and m4?") that includes a second label M2 assigned to the second molecular structure 621 and a fourth label M4 assigned to the edited molecular structure 941, based on the selection of a graphic object 1031 contained in the third region 1030. In this case, the user query 1032 may be a query concerning the prediction of a chemical reaction between the second molecular structure 621 to which the second label M2 has been assigned and the fourth molecular structure 941 to which the fourth label M4 has been assigned. In this way, users can enter queries more intuitively and easily by utilizing labels assigned to specific molecular structures, without having to input complex information about those structures.

[0196] When a user query is received, the response generation system 100 can identify a specific piece of content (or molecular structure) related to the user query from among multiple pieces of content. Based on the fact that the user query 1032 has been received, the response generation system 100 can input the user query 1032 into the ultra-large-scale infrastructure model 200.

[0197] The ultra-large-scale infrastructure model 200 can receive user query 1032 as input, understand the question (or content) contained in user query 1032, and identify specific content (or molecular structure) related to user query 1032. The ultra-large-scale infrastructure model 200 can analyze user query 1032 and extract labels representing grouped content from user query 1032. For example, based on the analysis of user query 1032, the ultra-large-scale infrastructure model 200 can extract the second label M2 and the fourth label M4, based on the fact that user query 1032 contains text corresponding to the second label M2 representing the grouped second content 620 and the fourth label M4 representing the grouped fourth content 940.

[0198] Furthermore, the ultra-large-scale infrastructure model 200 can identify specific grouped content corresponding to the extracted labels. For example, the ultra-large-scale infrastructure model 200 can identify the grouped second content 620 corresponding to the extracted second label M2, and the grouped fourth content 940 corresponding to the extracted fourth label M4, as specific grouped content. Here, the identification of the grouped second content 620 and the grouped fourth content 940 can also be understood as meaning that the second molecular structure 621 corresponding to the grouped second content 620 and the fourth molecular structure 941 corresponding to the grouped fourth content 940 have been identified.

[0199] Thus, in this invention, the time required to identify specific content corresponding to a user query can be reduced by using labels assigned to the extracted molecular structures. Once content related to a user query is identified, the present invention can process molecular graphs relating to multiple molecular structures as input to a chemical reaction prediction model so that the chemical reaction corresponding to the user query is predicted (see S1050, Figure 10).

[0200] The ultra-large-scale foundational model 200 can process the molecular structures of specific grouped content as input to a pre-trained predictive model. More specifically, the ultra-large-scale foundational model 200 can process grouped second content 620 assigned a second label M2, and grouped fourth content 940 assigned a fourth label M4, as input to a pre-trained predictive model. Here, processing specific grouped content as input to a pre-trained predictive model can also be understood as processing the molecular structures corresponding to the grouped content as input.

[0201] In this regard, the response generation system 100 can process a second molecular structure 621 corresponding to a specific grouped second content 620, and a fourth molecular structure 941 corresponding to a specific grouped fourth content 940, as input to a pre-trained predictive model. More specifically, the response generation system 100 can process the second molecular graph for the second molecular structure 621 and the fourth molecular graph for the fourth molecular structure 941 as input to a pre-trained prediction model. The second molecular graph for the second molecular structure 621 can be seen by referring to Figure 12, and the fourth molecular graph for the fourth molecular structure 941 can be seen by referring to Figure 15. In this case, which of the multiple pre-trained prediction models to process the molecular graph (or identified content) as input to may be determined based on the user query 1032. For example, the ultra-large-scale base model 200 can understand the content contained in the user query 1032 and, based on the fact that the user query 1032 contains the content "Can you predict the chemical reaction between m2 and m4?", can determine that the user query 1032 is related to predicting chemical reactions for multiple molecular structures. Based on the determination result, the ultra-large-scale base model 200 can determine the prediction model to which the multiple molecular graphs (e.g., the second molecular graph and the fourth molecular graph) are input as the chemical reaction prediction model 400.

[0202] Furthermore, the ultra-large-scale base model 200 can process the second and fourth molecular graphs as input to the chemical reaction prediction model 400, which understands the chemical reaction mechanism. When a molecular graph relating to molecular structure is input into a chemical reaction prediction model, the model can perform the process of predicting chemical reactions for multiple molecular structures (see S1060, Figure 10).

[0203] Furthermore, the present invention allows for the acquisition of chemical reaction prediction results for multiple molecular structures from a chemical reaction prediction model (see S1070, Figure 10). The chemical reaction prediction process of Chemical Reaction Prediction Model 400 has been explained in detail above, so to avoid repetition, it will be explained briefly below.

[0204] When the chemical reaction prediction model 400 receives the second and fourth molecular graphs as input, it can use information about multiple molecular structures (second molecular structure 621 and fourth molecular structure 941) to obtain embedding vectors corresponding to the second and fourth molecular graphs from the embedding layer 411 of the encoder 410. The chemical reaction prediction model 400 can also use the multi-head self-attention layer 413 to perform attention calculations related to the interactions between atoms of the multiple molecular structures 621 and 941, and update the embedding vectors based on the calculations. Next, using the embedding vectors updated through the update process, the chemical reaction prediction model 400 can perform bond and atom predictions for the chemical reactions of the multiple molecular structures 621 and 641, and use the bond and atom prediction results to output the final chemical reaction results predicted from the chemical reactions of the multiple molecular structures 621 and 641. As described above, the updated embedding vector can be understood as an updated version of the attention score calculated in each of the multiple heads of the multi-head self-attention layer 413, with different biases applied depending on the bonding type between the nodes constituting the second and fourth molecular graphs. Furthermore, bonding prediction is performed by performing a dot-product operation using the updated embedding vector, and atomic prediction is performed by predicting the atomic properties of the atoms corresponding to the updated embedding vector using the atomic property probability distribution of each atom corresponding to the updated embedding vector, and the atomic properties may include the charge state of atoms that can change during the chemical reaction process of multiple molecular structures.

[0205] In other words, the chemical reaction prediction model 400 can output the final chemical reaction result predicted from the chemical reaction between the second molecular structure 621 and the fourth molecular structure 941 through the process described above. Here, the chemical reaction prediction result obtained from the chemical reaction prediction model 400 may correspond to the final result stabilized through a diffusion feedback process. In one embodiment, the output data (or chemical reaction prediction result) of the chemical reaction prediction model 400 may include a specific molecular structure generated as a result of predicting a chemical reaction between multiple molecular structures, and at least one piece of information (or content) related to that specific molecular structure. For example, the output data may include at least one of the following: a fifth molecular structure generated as a result of predicting a chemical reaction between a second molecular structure 621 and a fourth molecular structure 941, a molecular structure image of the fifth molecular structure, a name of the fifth molecular structure, a description of the fifth molecular structure, and the SMILES notation for the fifth molecular structure.

[0206] On the other hand, the present invention allows for the generation of answers to user queries using chemical reaction prediction results (see S1080, Figure 10). The large-scale base model 200 can determine an answer generation procedure executed for predicting a response to the user query 1032 and tools used in the answer generation procedure. For example, as shown in FIG. 17, the large-scale base model 200 can determine what procedures and tools to use to generate an answer to the user query 1032.

[0207] Also, the answer generation system 100 can provide information on the answer generation procedure and tools determined from the large-scale base model 200 to the service page 1000. For example, the answer generation system 100 can provide information 1101 on the answer generation procedure and tools determined from the large-scale base model 200 via the service page 1000 to perform a prediction corresponding to the user query 1100 (e.g., "Can you predict the reaction between m2 and m4?"). Furthermore, the large-scale base model 200 can use a plurality of molecular structures (e.g., the second molecular structure 621 and the fourth molecular structure 941) corresponding to specific labels (the second label M2 and the fourth label M4) included in the user query 1032 to generate an answer to the user query 1032. More specifically, the large-scale base model 200 can use the chemical reaction prediction result of the chemical reaction prediction model 400 and the content constituting the grouped content (e.g., the content related to the second molecular structure 621 and the fourth molecular structure 941), and the information 1101 on the determined answer generation procedure and tools to generate an answer 1110 to the user query 1100.

[0208] On the other hand, the answer generation system 100 can use a pre-trained molecular physical property prediction model 500 to predict the physical properties of a specific molecular structure and provide, as an answer 1110 to the user query 1100, information on the physical properties of the specific molecular structure predicted from the molecular physical property prediction model 500 together. As described above, the molecular property prediction model 500 may be a model constructed for material structure design. The molecular property prediction model 500 may be configured to predict physical properties from a molecular structure or to design a molecule having properties (or new properties) desired by a user.

[0209] Specifically, the answer generation system 100 can process the fifth molecular structure as an input to the molecular property prediction model 500. The molecular property prediction model 500 can receive the fifth molecular structure as an input and output a physical property prediction result for the fifth molecular structure as output data. The answer generation system 100 can obtain the physical property prediction result for the fifth molecular structure output from the molecular property prediction model 500. Furthermore, the answer generation system 100 can input the physical property prediction result for the fifth molecular structure into the ultra-large-scale base model 200. The ultra-large-scale base model 200 can generate an answer 1110 for the user query 1100 using the physical property prediction result for the fifth molecular structure. In this case, the answer generation system 100 can provide information regarding the physical properties of the fifth molecular structure predicted from the molecular property prediction model 500 together as the answer 1110 for the user query 1100.

[0210] On the other hand, when the answer 1110 for the user query 1100 includes a specific molecular structure (or a new molecular structure) generated by a pre-trained prediction model, a label may be assigned to the specific molecular structure. The answer generation system 100 can perform labeling for a specific molecular structure so that a new label for identifying the specific molecular structure is assigned. For example, the answer generation system 100 can perform labeling for the fifth molecular structure so that the fifth label M5 is assigned to the fifth molecular structure generated by the chemical reaction prediction model 400.

[0211] Here, a specific molecular structure (fifth molecular structure) and a label assigned to that specific molecular structure (fifth label M5) may be linked to a user account and stored in the storage unit 140 along with the extracted molecular structure and the label assigned to the extracted molecular structure. Furthermore, the response generation system 100 can generate specific graphic objects corresponding to specific molecular structures based on the fact that specific molecular structures are generated from a pre-trained prediction model. For example, the response generation system 100 can generate a fifth graphic object corresponding to a fifth molecular structure using a fifth molecular structure stored in the memory unit 140.

[0212] Furthermore, the response generation system 100 can update the service page 1000 so that a specific graphic object corresponding to a specific molecular structure is included in a region of the service page 1000. More specifically, as shown in Figure 18, the response generation system 100 can update the first region 1210 so that a graphic object 1215 (or the fifth graphic object) corresponding to the fifth molecular structure is included in the first region 1210 of the service page 1000. Based on the update, the first area 1210 of the service page 1000 may provide a specific graphic object along with detailed information about the molecular structure corresponding to that specific graphic object. For example, the first area 1210 may include a molecular structure image 1215a of the fifth molecular structure corresponding to the fifth graphic object 1215, the name of the fifth molecular structure 1215b, a description of the fifth molecular structure 1215c, the SMILES notation for the fifth molecular structure 1215d, and the physical properties 1215e of the fifth molecular structure.

[0213] On the other hand, the response generation system 100 can provide responses to user queries 1100 generated from the ultra-large-scale infrastructure model 200 (for example, "The product produced by the chemical reaction between m2 and m4 is m5...") to one area 1220 (or a second area) of the service page 1000. The answers provided in the second area 1220 of service page 1000 may include content indicating that a new specific molecular structure (e.g., a fifth molecular structure) was generated as a result of predicting a chemical reaction between multiple molecular structures (e.g., a second molecular structure 621 and a fourth molecular structure 941).

[0214] Specifically, the answer may include molecular structure image 1212a of the second molecular structure 621 and molecular structure image 1214a of the fourth molecular structure 941, and may also include molecular structure image 1215a of the fifth molecular structure produced as a result of a chemical reaction between the second molecular structure 621 and the fourth molecular structure 941. Furthermore, graphic objects (e.g., plus signs, arrows, etc.) representing the relationships between the molecular structures corresponding to each image may be displayed between the images of the second molecular structure (1212a), the fourth molecular structure (1214a), and the fifth molecular structure (1215a) included in the answer.

[0215] Furthermore, the response may include at least one of the details of the fifth molecular structure generated by the chemical reaction prediction model 400 (e.g., the name of the fifth molecular structure 1215b and the description of the fifth molecular structure 1215c). Furthermore, the response may also include information about the properties of the fifth molecular structure predicted from the molecular property prediction model 500, and information about the fifth label M5 assigned to the fifth molecular structure may be displayed in the surrounding area of ​​the molecular structure image 1215a of the fifth molecular structure.

[0216] On the other hand, the response generation method of the ultra-large-scale infrastructure model described above was explained assuming that a document has been received. Below, however, we will examine the response generation method of the ultra-large-scale infrastructure model in more detail, assuming that text has been received. First, the response generation system 100 can receive user queries in text format from the user terminal 10 to which the service page 1000 is provided.

[0217] Specifically, the response generation system 100 can receive a user query containing information about at least one molecular structure via a region of the service page 1000. Here, the information about the molecular structure included in the user query may vary. For example, information about the molecular structure may include the name of a specific molecular structure, a description of a specific molecular structure, the SMILES notation for a specific molecular structure, or the mathematical formula for a specific molecular structure. However, the above-mentioned information about the molecular structure is merely an example, and it goes without saying that the information about the molecular structure included in the user query in the present invention is not necessarily limited to this, and may further include various other information related to molecular structures. For example, as shown in Figure 19, the response generation system 100 can receive a user query 1532 from the user terminal 10 that includes the names of specific molecular structures (e.g., "Molecular structure A", "Molecular structure B"), based on the selection of a graphic object 1531 contained in the third region 1530 (e.g., "Can you predict the reaction between Molecular structure A and Molecular structure B?").

[0218] Based on the fact that the user query 1532 has been received, the response generation system 100 can input the user query 1532 into the ultra-large-scale infrastructure model 200. The ultra-large-scale infrastructure model 200 can identify the molecular structures to be predicted based on the names of specific molecular structures included in user query 1532. For example, the ultra-large-scale infrastructure model 200 can identify the first and second molecular structures corresponding to each of the specific molecular structures included in user query 1532 (e.g., "Molecular Structure A", "Molecular Structure B").

[0219] Once the molecular structure is identified, the response generation system 100 can extract information about the identified molecular structure. In this case, the extraction of information about the identified molecular structure may also be configured to be extractable by the ultra-large-scale base model 200. In one embodiment, as shown in Figure 20, the response generation system 100 can extract at least one of the following related to the first molecular structure 1611 identified from the user query 1600: the molecular structure image of the first molecular structure 1611, the name of the first molecular structure 1612, the description of the first molecular structure 1613, the SMILES notation of the first molecular structure 1614, and the physical properties of the first molecular structure 1614.

[0220] In another embodiment, the response generation system 100 can extract at least one of the following related to the second molecular structure 1621 identified from the user query 1600: a molecular structure image of the second molecular structure 1621, a name of the second molecular structure 1622, a description of the second molecular structure 1623, a SMILES notation for the second molecular structure 1624, and physical properties of the second molecular structure 1625. Here, various information related to the identified molecular structure (e.g., detailed information, multiple contents, etc.) may be i) extracted from various molecular structure-related contents (or information or data) stored in the memory unit 140, or ii) generated by at least one of the chemical reaction prediction model 400 and the molecular property prediction model 500.

[0221] In one embodiment, the information on various molecular structures stored in the memory unit 140 may include content related to molecular structures relating to at least one of chemistry, biotechnology, new materials, new substances, and new drug development, extracted from each of multiple documents using the document understanding model 300. The ultra-large-scale base model 200 can extract at least one piece of content related to a specific molecular structure from the molecular structure-related content stored in the memory unit 140 as content. In another embodiment, when a specific molecular structure is identified from a user query, the response generation system 100 can use at least one of a pre-trained chemical reaction prediction model 400 and a molecular property prediction model 500 to generate at least one piece of content related to the identified molecular structure.

[0222] On the other hand, the answer generation system 100 can group content related to the same molecular structure among multiple contents based on the relationships between them. More specifically, the answer generation system 100 can group the molecular structure image of the first molecular structure 1611, the name of the first molecular structure 1612, the description of the first molecular structure 1613, the SMILES notation for the first molecular structure 1614, and the physical properties of the first molecular structure 1615, which contain content related to the first molecular structure 1611, as content related to the first molecular structure 621. In addition, the answer generation system 100 can group the molecular structure image of the second molecular structure 1621, the name of the second molecular structure 1622, the description of the second molecular structure 1623, the SMILES notation for the second molecular structure 1624, and the physical properties of the second molecular structure 1625, which contain content related to the second molecular structure 621, as content related to the second molecular structure 621. Through the grouping process described above, grouped content based on molecular structure can be generated. For example, the first molecular structure 1611, the molecular structure image of the first molecular structure 1611, the name of the first molecular structure 1612, the description of the first molecular structure 1613, the SMILES notation of the first molecular structure 1614, and the physical properties of the first molecular structure 1615 can be grouped to generate grouped first content 1610. Similarly, the second molecular structure 1621, the molecular structure image of the second molecular structure 1621, the name of the second molecular structure 1622, the description of the second molecular structure 1623, the SMILES notation of the second molecular structure 1624, and the physical properties of the second molecular structure 1625 can be grouped to generate grouped second content 1620.

[0223] Furthermore, the grouped contents 1610 and 1620 may be stored in the storage unit 140 in association with the user account. On the other hand, the answer generation system 100 can obtain a molecular graph regarding a plurality of molecular structures by converting atoms into nodes and bonds between atoms into edges based on the plurality of molecular structures stored in the storage unit 140 (or memory).

[0224] As described above, the answer generation system 100 can obtain a molecular graph regarding a molecular structure by converting the atoms included in the molecular structure into nodes and the bonds between atoms into edges based on the molecular structure. In one embodiment, as shown in FIG. 20, the answer generation system 100 converts the atoms constituting the first molecular structure 1611 stored in the storage unit 140 into nodes 1611a, 1611b, and 1611c, and the bond relationships between the atoms constituting the first molecular structure 1611 into edges 1611d, 1611e, and 1611f, thereby obtaining a first molecular graph including nodes 1611a, 1611b, 1611c and edges 1611d, 1611e, 1611f corresponding to the first molecular structure 1611.

[0225] In other embodiments, the answer generation system 100 converts the atoms constituting the second molecular structure 1621 stored in the storage unit 140 into nodes 1621a, 1621b, and 1621c, and the bond relationships between the atoms constituting the second molecular structure 1621 into edges 1621d, 1621e, and 1621f, thereby obtaining a second molecular graph including nodes 1621a, 1621b, 1621c and edges 1621d, 1621e, 1621f corresponding to the second molecular structure 1621. Furthermore, the answer generation system 100 can store information regarding a plurality of specified molecular structures in the storage unit 140. More specifically, the answer generation system 100 can store various information (e.g., the first molecular graph and the second molecular graph) regarding the first molecular structure 1611 and the second molecular structure 1621 in the storage unit 140 (or memory).

[0226] On the other hand, the response generation system 100 can label multiple molecular structures 1611 and 1621 such that each of the multiple molecular structures 1611 and 1621 is assigned a different label from the others. For example, the response generation system 100 can assign a first label M1 to the first molecular structure 1611 and a second label M2 to the second molecular structure 1621 by labeling the first molecular structure 1611 (or first molecular structure graph) and the second molecular structure 1621 (or second molecular structure graph). Furthermore, the response generation system 100 can store in the storage unit 140 (or memory) information regarding the first molecular graph obtained through the process of acquiring the molecular graph, and the label M1 assigned to the first molecular graph, as well as information regarding the second molecular graph obtained through the process of acquiring the molecular graph, and the label M2 assigned to the second molecular graph.

[0227] Furthermore, the ultra-large-scale infrastructure model 200 understands the content of user query 1600 and, based on the fact that user query 1032 contains the question "Can you predict the reaction between molecular structure A and molecular structure B?", can process the first molecular structure 1611, which is assigned the first label M1, and the second molecular structure 1621, which is assigned the second label M2, as input to the chemical reaction prediction model 400. On the other hand, the chemical reaction prediction process of chemical reaction prediction model 400 has been explained in detail above, so to avoid repetition, it will be explained briefly below.

[0228] When the chemical reaction prediction model 400 receives the first molecular graph and the second molecular graph as input, it can use information about multiple molecular structures (first molecular structure 1611 and second molecular structure 1621) to obtain embedding vectors corresponding to the first molecular graph and the second molecular graph from the embedding layer 411 of the encoder 410. Furthermore, the chemical reaction prediction model 400 can use the multi-head self-attention layer 413 to perform attention calculations related to the interactions between atoms of the multiple molecular structures 1611 and 1621, and update the embedding vectors based on the calculations. Subsequently, using the embedding vectors updated through the update process, the chemical reaction prediction model 400 can perform bond and atom predictions for the chemical reactions of the multiple molecular structures 1611 and 1621, and use the bond and atom prediction results to output the final chemical reaction results predicted from the chemical reactions of the multiple molecular structures 1611 and 1621. Furthermore, the ultra-large-scale infrastructure model 200 can obtain chemical reaction prediction results for multiple molecular structures 1611 and 1621 from the chemical reaction prediction model 400, and use the obtained results to generate answers to user queries 1600 (for example, "The product produced through the chemical reaction between m1 and m2 is m3...").

[0229] On the other hand, the response generation system 100 can generate third-party content (or detailed information) grouped based on a specific molecular structure (or new molecular structure) if the response to the user query 1600 contains such a structure. For the sake of explanation, the specific molecular structure will be referred to as the "third molecular structure" below. In this case, at least a portion of the information contained in the grouped third content may be generated by at least one of the chemical reaction prediction model 400 and the molecular property prediction model 500. As another example, at least a portion of the information contained in the grouped third content may be generated using information on various molecular structures stored in the memory unit 140.

[0230] Furthermore, the response generation system 100 can label the third content grouped based on the third molecular structure so that a new label is assigned to identify the third molecular structure. For example, the response generation system 100 can label the third molecular structure so that the third molecular structure generated by the chemical reaction prediction model 400 is assigned the third label M3. Here, the third molecular structure and the third label assigned to the third molecular structure may be linked to a user account and stored in the storage unit 140 together with the extracted molecular structures (e.g., the first molecular structure and the second molecular structure) and the labels assigned to the extracted molecular structures (e.g., the first label and the second label).

[0231] Furthermore, the response generation system 100 can generate specific graphic objects corresponding to the third molecular structure. For example, the response generation system 100 can generate a third graphic object corresponding to the third molecular structure generated by the chemical reaction prediction model 400. On the other hand, as shown in Figure 21, the response generation system 100 can provide the service page 1000 with graphic objects corresponding to each of the labeled contents, along with the responses generated from the ultra-large-scale infrastructure model 200. Here, the labeled contents may include grouped first contents 1610 and second contents 1620, and third contents grouped based on the third molecular structure included in the response to the user query 1600.

[0232] First, the first area 1710 of the service page 1000 may contain at least one grouped content that has been labeled. Specifically, the first region 1710 may include graphic objects 1711, 1712, 1713 corresponding to each of a plurality of molecular structures that have been assigned different labels by labeling (for example, a first molecular structure assigned a first label M1, a second molecular structure assigned a second label M2, and a third molecular structure assigned a third label M3), and at least one of the details of the plurality of molecular structures.

[0233] The first area 710 of service page 1000 may include a first sub-area 1710a containing graphic objects 1711, 1712, and 1713, and a second sub-area 1710b containing detailed information. The first sub-region 1710a may contain multiple graphic objects 1711, 1712, and 1713, each corresponding to a plurality of molecular structures. More specifically, among the plurality of graphic objects, the first graphic object 1711 may contain an image of the first molecular structure corresponding to the first graphic object 1711, and the third graphic object 1713 may contain an image of the third molecular structure corresponding to the third graphic object 1713.

[0234] Furthermore, the second sub-region 1710b may be provided with detailed information about the molecular structure corresponding to one of the multiple graphic objects 1711, 1712, and 1713 selected by user input. The response generation system 100 can provide detailed information about the graphic object selected by user input based on the user input receiving user input to select one of the multiple graphic objects 1711, 1712, and 1713. In this regard, each of the multiple graphic objects 1711, 1712, and 1713 contained in the first sub-region 1710a may be linked (or linked) to detailed information about the molecular structure corresponding to each of the multiple graphic objects 1711, 1712, and 1713. For example, suppose a third graphic object 1713 corresponding to a third molecular structure is selected from the user terminal 10. Based on the selection of the third graphic object 1713 contained in the first sub-region 1710a by the user terminal 10, the response generation system 100 can provide detailed information 1713a, 1713b, 1713c, 1713d, and 1713e regarding the third molecular structure linked to the third graphic object 1713 to the second sub-region 1710b. Furthermore, information about the third label M3 assigned to the third molecular structure may also be displayed in the second sub-region 1710b.

[0235] As another example, if the answer to a user query includes a new molecular structure (e.g., a third molecular structure), the answer generation system 100 can automatically select a graphic object corresponding to the new molecular structure contained in the first sub-region 1710a and provide detailed information about the new molecular structure in the second sub-region 1710b. Next, the second area 1720, which is separated from the first area 1710 of the service page 1000, may be provided with the answer to the user query 1600.

[0236] The answers provided in area 1720 of service page 1000 may include content indicating that a new specific molecular structure (e.g., a third molecular structure) was generated as a result of predicting a chemical reaction between multiple molecular structures (e.g., a first molecular structure and a second molecular structure). Specifically, the answer may include molecular structure image 1711a of the first molecular structure and molecular structure image 1712a of the second molecular structure, and may also include molecular structure image 1713a of the third molecular structure produced as a result of a chemical reaction between the first and second molecular structures.

[0237] Furthermore, graphic objects (e.g., plus signs, arrows, etc.) representing the relationships between the molecular structures corresponding to each image may be displayed between the molecular structure images 1711a (first molecular structure), 1712a (second molecular structure), and 1713a (third molecular structure) included in the answer. Furthermore, the response may include at least one of the details of the third molecular structure generated by the chemical reaction prediction model 400 (e.g., the name of the third molecular structure 1713b and the description of the third molecular structure 1713c).

[0238] Furthermore, the response may also include information about the properties of the third molecular structure predicted from the molecular property prediction model 500, and information about the third label M3 assigned to the third molecular structure may be displayed in the surrounding area of ​​the molecular structure image 1713a of the third molecular structure. Meanwhile, the response generation system 100 can receive new user queries via the service page 1000, including the third label M3 assigned to the third molecular structure.

[0239] Specifically, the response generation system 100 can receive a new user query containing the third label M3 assigned to the third molecular structure via the third area of ​​the service page 1000. For example, as shown in Figure 22, the response generation system 100 can receive a new user query 1832 (e.g., "Is the product m3 the same as this one Molecular structure D?") containing the third label M3 assigned to the third molecular structure from the user terminal 10 based on the selection of a graphic object 1831 contained in the third area 1830. Based on the receipt of a new user query 1832 that includes the third label M3 assigned to the third molecular structure, the response generation system 100 can input the new user query 1832 into the ultra-large-scale infrastructure model 200.

[0240] Here, the ultra-large-scale infrastructure model 200, based on the fact that the new user query 1832 contains the name of a new specific molecular structure (e.g., "Molecular Structure D") rather than labeled content, can identify the fourth molecular structure corresponding to the name of the specific molecular structure and extract content related to the fourth molecular structure. Furthermore, the response generation system 100 can group the content related to the fourth molecular structure, generate fourth content grouped based on the fourth molecular structure, and assign the fourth label M4 to the grouped fourth content. More specific details regarding this are explained above, so a brief explanation is provided here. The ultra-large-scale infrastructure model 200 can generate an answer to a new user query 1832 using at least a portion of the detailed information of the third molecular structure corresponding to the third label M3 and the fourth molecular structure corresponding to the fourth label M4. For example, the ultra-large-scale infrastructure model 200 can generate an answer to a new user query 1832 using at least a portion of the information contained in the grouped third content assigned the third label M3 and the grouped fourth content assigned the fourth label M4.

[0241] In this case, the ultra-large-scale infrastructure model 200 can leverage at least one predictive model to generate an answer to the new user query 1832. For example, the ultra-large-scale infrastructure model 200 can understand the content of the new user query 1832 and, based on the fact that the user query 1832 contains the question "Do m3 and molecular structure D have the same structure?", input information about the third content grouped based on the third molecular structure and the fourth content grouped based on the fourth molecular structure into the chemical reaction prediction model 400. The chemical reaction prediction model 400 can compare the binding structures of the third and fourth molecular structures corresponding to the new user query 1832. Furthermore, the chemical reaction prediction model 400 can output the comparison results of the third and fourth molecular structures as output data.

[0242] Meanwhile, the ultra-large-scale infrastructure model 200 determines the answer generation procedure to be performed for predictions corresponding to the new user query 1832, and the tools used in the answer generation procedure. Using the determined answer generation procedure and tools, the output data of the chemical reaction prediction model 400, and information on the third and fourth molecular structures, it can generate an answer 1921 to user query 1832 (for example, "m3 and m4 are different from each other...") (see Figure 23). Once response 1921 is generated, as shown in Figure 23, the response generation system 100 can provide the response 1921 to the user query 1832 generated from the ultra-large-scale infrastructure model 200 to the second area 1920 of the service page 1000.

[0243] In this case, the answer 1921 provided in the second area 1920 of service page 1000 may include content representing the results of a comparison of the binding structures between multiple molecular structures (e.g., a third molecular structure and a fourth molecular structure). For example, the answer 1921 may include at least one of the following: labels M3 and M4 assigned to the third and fourth molecular structures, respectively; molecular structure images 1913a and 1914a of the third and fourth molecular structures, respectively; and content 1921a explaining the results of a comparison of the binding structures between the third and fourth molecular structures. Furthermore, the first area 1910 of the service page 1000 may include a fourth graphic object 1914 corresponding to the fourth molecular structure, and at least one of the following linked to the fourth graphic object 1914: a molecular structure image 1914a of the fourth molecular structure, a name 1914b of the fourth molecular structure, a description 1914c of the fourth molecular structure, the SMILES notation for the fourth molecular structure 1914d, and physical properties 1914e of the fourth molecular structure.

[0244] On the other hand, the service pages provided by the response generation system 100 can be offered to users in various forms other than the service page 1000 described above. Below, we will examine in more detail the various forms in which the service pages of the response generation system 100 can be offered. In one embodiment, as shown in Figure 24, the response generation system 100 can provide the user terminal 10 with a service page 2400 that is linked to the system 100.

[0245] The service page 2400 may include at least one of a first area 2410 for receiving answers to user queries and a second area 2420 for providing answers to user queries. The answer generation system 100 can receive a user query 2411 (for example, "What about a reaction between 2-bromoethanol and ethane sulfonyl chloride. First look for their smiles and then predict their reaction.") which contains information about at least one molecule entered via the first area 2410 of the service page 2400. Based on the user query 2411 containing the message, "What is the reaction between the first compound (2-bromoethanol) and the second compound (ethanesulfonyl chloride)? First, find their respective SMILES strings," the ultra-large-scale base model 200 extracts the SMILES strings for the first and second compounds from the memory unit 140 and inputs the information about the first and second compounds into the chemical reaction prediction model 400. The chemical reaction prediction model 400 can predict the chemical reaction result between the first and second molecular structures under specific conditions and output the predicted result.

[0246] Furthermore, as shown in Figures 24 and 25, the ultra-large-scale base model 200 can use the chemical reaction prediction results between the first and second molecular structures output from the chemical reaction prediction model 400 to generate an answer 2421 to the user's query 2411 (for example, "The predicted product of the reaction between 2-bromoethanol and ethanesulfonyl chloride is S-ethyl isobutylethanesulfonothioate. This organic compound is commonly used as an insecticide. This reaction is a process in which the sulfonyl chloride group of ethanesulfonyl chloride substitutes the hydroxyl group of 2-bromoethanol, forming a sulfonothioester."). For example, answer 2421 may include molecular structure images (or molecular graphs) 2421a and 2421b of the identified first and second molecular structures, and detailed information about each molecular structure (e.g., SMILES string). Furthermore, response 2421 may include a molecular structure image 2421c of the product generated as a result of the chemical reaction prediction, and detailed information about the product (or molecular structure) (e.g., SMILES string). Next, the response generation system 100 can receive a new user query containing information about the product generated as a result of the chemical reaction prediction. For example, as shown in Figure 25, the response generation system 100 can receive a user query 2511 (e.g., "Is the generated compound the same as this compound?") containing information about the new product entered via the first area 2410 of the service page 2400.

[0247] The ultra-large-scale infrastructure model 200 can input information about the product and comparison compound included in the new user query 2511 into the chemical reaction prediction model 400, based on the content of the new user query 2511. The chemical reaction prediction model 400 can compare the bonding structures of the molecular structure of the new product corresponding to the new user query 2511 and the molecular structure of the comparison compound, and output the comparison results as output data. The ultra-large-scale base model 200 can generate an answer to user query 2511 using the output data of the chemical reaction prediction model 400. For example, as shown in Figure 26, the ultra-large-scale base model 200 can generate an answer 2521 to user query 2511 (e.g., "The two compounds have different atomic arrangements.") and provide it via the service page 2400. In this case, the answer 2521 may include content representing the comparison result between the molecular structure of the new product and the molecular structure of the comparison compound (e.g., a molecular structure image (or molecular graph) 2521a of the new product, a molecular structure image 2521b of the comparison compound, and the respective SMILES strings of the new product and the comparison compound).

[0248] On the other hand, a service page linked to the response generation system 100 may include various functions and be provided to the user. In one embodiment, as shown in Figure 27, the service page 2700 may include at least one of the following: a first area 2701 which includes a function for setting various reaction conditions (or experimental conditions) such as temperature and pressure; a second area 2702 which includes a function for selecting reactants used for predicting chemical reactions; and a third area 2703 which includes a function for selecting reagents and / or solvents necessary for the reaction.

[0249] In another embodiment, the service page 2700 may include a fourth area 2704 that includes a function that allows the user to derive the direction of the reaction and the starting materials when the user selects at least one of forward or reverse reaction simulations. The service page 2700 may also include a fifth area 2705 that comprehensively provides experimental protocols, conditions, compound information, reaction types, etc. In other embodiments, the service page 2700 may also include a sixth area 2706 that provides information on the main product predicted as a result of the simulation, information on the yield predicted according to the reaction conditions, and information on the predicted by-products and impurities.

[0250] Thus, in this invention, reactants, reagents, and experimental conditions are input, and the reaction results are predicted using text data. The predicted products, yields, and by-products are then visualized and provided. This allows the user to efficiently discover synthesis routes and set optimal experimental conditions. On the other hand, as described above, a user may have a user account that has been pre-registered in the response generation system 100 according to the present invention.

[0251] As a result, the storage unit 140 of the response generation system 100 may store various information related to the user account. Here, the information related to the user account may include at least one of the following: user (or user account) history information and user metadata (e.g., name, gender, age, major, occupation, workplace (or company), etc.). More specifically, user history information may include information about various events that occurred in the user account. For example, events that occur in a user account may include at least one of the following: i) inputting a user query to obtain a response from the ultra-large-scale infrastructure model 200, ii) inputting (or selecting) a document, or iii) inputting a new (or additional) query on the response generated from the ultra-large-scale infrastructure model 200.

[0252] Based on such events, the user history information may include at least one of the following: i) user queries entered by the user, ii) document information entered by the user (or the user's document input history), iii) content extracted from documents entered by the user (e.g., specific molecular structures and information about specific molecular structures), and iv) the responses of the ultra-large-scale infrastructure model 200 to the user queries. As a result, the storage unit 140 of the response generation system 100 may store at least one of the following in conjunction with the user account: the document to be analyzed, the extracted molecular structure, the label assigned to (or for) the extracted molecular structure, the user query, and the response to the user query.

[0253] Here, the extracted molecular structure information may be i) information extracted from the document being analyzed, or ii) information extracted from a user query. Alternatively, the extracted molecular structure information may be i) information generated from the chemical reaction prediction model 400, or ii) information generated from the molecular property prediction model 500. Furthermore, the memory unit 140 of the response generation system 100 may store various information related to the criteria being at least one of chemistry, biotechnology, new materials, new substances, and new drug development. For example, the memory unit 140 may store data such as text, molecular structures, mathematical formulas, charts, tables, and images related to at least one of chemistry, biotechnology, new materials, new substances, and new drug development.

[0254] Therefore, the present invention provides a user environment that allows users to utilize various functions using the DB built in the response generation system 100. As shown in Figure 28, the response generation system 100 can use various information stored in the memory unit 140 to provide the user terminal 10 with a service page 2800 configured to search for the physical and chemical attributes of a specific compound (or product or molecule) or to predict missing attributes.

[0255] In one embodiment, the first area 2810 of the service page 2800 may provide detailed information about the compound selected from the user terminal 10. For example, the first area 2810 of the service page 1000 may provide the structure of the compound along with its physical and chemical attributes. In other embodiments, the second area 2820 of the service page 2800 may include a function that allows the user to search for similar compounds. For example, the response generation system 100 may, based on receiving a search request from the user terminal 10 via the second area 2820 for compounds structurally similar to a specific compound, extract information about compounds structurally similar to a specific compound from the storage unit 140 and provide the extracted information about similar compounds on the second area 2820.

[0256] Furthermore, the present invention provides a user environment that allows the user to search for various chemical reaction data or chemical reaction data similar to a specific chemical reaction based on the various chemical reaction data stored in the memory unit 140. In one embodiment, as shown in Figure 29, the response generation system 100 can provide a service page 2900 configured to allow the user to explore various chemical reaction data to the user terminal 10. The user can explore data on various chemical reactions via the service page 2900, and the chemical reaction data may include, for example, at least one of reactants, products, and reaction conditions.

[0257] In this case, the response generation system 100 can visualize and provide various chemical reaction data as options selected by the user. For example, one area of ​​the service page 2900 may include graphic objects 2901, 2902, and 2903 that are linked to various visualization options. In one embodiment, when a first graphic object 2901 is selected from the user terminal 10, the response generation system 100 can provide various chemical reaction data 2911 and 2912 stored in the storage unit 140 in list format.

[0258] In another embodiment, when a second graphic object 2902 is selected from the user terminal 10, the response generation system 100 can provide various chemical reaction data 2911 and 2912 stored in the storage unit 140 in a table format. In another embodiment, when a third graphic object 2903 is selected from the user terminal 10, the response generation system 100 can provide various chemical reaction data 2911 and 2912 stored in the storage unit 140 in graph format.

[0259] Furthermore, the present invention can provide a user environment that enables users to conduct chemical synthesis research and experiments more efficiently and systematically. As shown in Figure 30, the answer generation system 100 can provide a service page 3000 that allows the user to study various synthetic routes related to forward and reverse synthesis. In this case, the service page 3000 may provide information on various routes through which a compound can be synthesized by supporting both forward and reverse simulations, or it may provide information on synthesizable compounds having specific attributes, or it may include a function that allows the user to select a compound having desired physical and chemical properties and plan a synthetic route.

[0260] In one embodiment, the first area 3010 of the service page 3000 may provide a reaction pathway between selected compounds. The first area 3010 may also provide a visualization of the reaction mechanism, including the main product and by-products. In other embodiments, the second area 3020 of the service page 3000 may provide a sorted list of synthesizable compounds and may include a function to add reagents and solvents required for the reaction.

[0261] In other embodiments, the third area 3030 of service page 3000 may provide visualizations of various reaction pathways. In this case, the compounds produced along a particular reaction pathway may be visualized step by step and provided in the third area 3030. Furthermore, the fourth area 3040 of service page 3000 may provide information on the major and by-products expected as a result of the simulation, including the predicted yield along each reaction pathway, detailed information on the major product and reaction conditions, expected impurities, and information on the amount of impurities. On the other hand, the service page receiving user queries may be provided with information about at least one labeled molecular structure. For example, as shown in Figure 31, the third area 3130 of the service page 3100 may be provided with multiple graphic objects 3131, 3132, 3133, 3134, and 3135 corresponding to multiple molecular structures, each containing different labels M1, M2, M3, M4, and M5. In this case, the multiple graphic objects 3131, 3132, 3133, 3134, and 3135 may include molecular structure images of the molecular structures or molecular graphs relating to the molecular structures.

[0262] In one embodiment, the response generation system 100 can provide a visualized version of the first compound as a response 3111 to the user query 3110, based on the receipt of a user query 3110 from the user terminal 10, which states, "Please visualize and provide the first compound (e.g., ethanol)." In this case, based on the fact that the response 3111 includes the molecular structure of the first compound, the response generation system 100 can assign a first label M1 to the molecular structure 3131 of the first compound and update the third region 3130 with the molecular structure 3131 of the first compound to which the first label M1 has been assigned. In another embodiment, the response generation system 100 may, based on receiving a user query 3120 from the user terminal 10 requesting the visualization and provision of compounds similar to the molecular structure 3131 of the first compound, provide a response 3121 to the user query 3120, which is a visualization of the molecular structure of compounds similar to the molecular structure of the first compound. In this case, based on the fact that the response 3121 includes the molecular structure of a new compound (or a second compound), the response generation system 100 may assign a second label M2 to the molecular structure 3132 of the new compound and update the third region 3130 with the molecular structure 3132 of the new compound to which the second label M2 has been assigned.

[0263] Furthermore, as shown in Figure 32, the response generation system 100 can generate Python code corresponding to the user's query and provide a response 3211 containing the Python code, based on the fact that it has received a user query 3210 via the service page 3200, which includes the message, "Check if there is experimental information regarding the boiling points of the compounds, and if not, provide Python code to estimate the boiling points of the two compounds." In other words, users can query experimental boiling point information for compounds, or, if experimental data is unavailable, they can receive Python code to obtain an estimate.

[0264] As described above, the method and system for generating responses to ultra-large-scale infrastructure models according to the present invention can propose the optimal research method to the user and minimize the risk of research failure by generating and providing responses suitable for user queries based on data extracted from documents. Furthermore, according to the ultra-large-scale infrastructure model response generation method and system of the present invention, responses to user queries can be provided using data extracted from documents or generated from a pre-trained predictive model. This enables the rapid and accurate provision of necessary information to users, thereby reducing the time and cost associated with research and / or development.

[0265] Furthermore, according to the ultra-large-scale infrastructure model response generation method and system of the present invention, it is possible to generate responses to user queries using results predicted from a pre-trained prediction model and provide the generated responses to the user. This allows users to reduce the time required for research and / or development and to reduce the number of trial-and-error iterations in research and / or development. Furthermore, according to the method and system for generating answers for ultra-large-scale base models of the present invention, by visualizing and providing extracted molecular structures and related data through a user interface, users can intuitively recognize and understand the necessary information more quickly, thereby improving the accuracy and efficiency of their research.

[0266] On the other hand, the present invention described above can be embodied as a program that is executed by one or more processes on a computer and can be stored on a medium (or recording medium) that is readable by such a computer. Furthermore, the present invention described above can be embodied as computer-readable code or instruction words on a medium on which a program is recorded. That is, the present invention can be provided in the form of a program.

[0267] On the other hand, computer-readable media include all types of recording devices that store data readable by a computer system. Examples of computer-readable media include HDDs (Hard Disk Drives), SSDs (Solid State Disks), SSDs (Silicon Disk Drives), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, and optical data storage devices. Furthermore, the computer-readable medium may include storage and may be a server or cloud storage accessible by electronic devices via communication. In this case, the computer can download the program according to the present invention from the server or cloud storage via wired or wireless communication.

[0268] Furthermore, in this invention, the computer described above is an electronic device equipped with a processor, i.e., a CPU (Central Processing Unit), and its type is not particularly limited. On the other hand, the above detailed description should not be interpreted restrictively in any way, but should be considered illustrative. The scope of the invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the scope of the equivalents of the invention are included within the scope of the invention.

Claims

1. A method by which memory and at least one processor cooperate to perform an action, The steps include identifying multiple molecular structures to be predicted based on user input received via the service page, The steps include storing information about the plurality of molecular structures in the memory, The steps include obtaining a molecular graph relating to the plurality of molecular structures by converting atoms into nodes and interatomic bonds into edges, based on the plurality of molecular structures stored in the memory, The steps include receiving user queries for predicting chemical reactions related to the multiple molecular structures via the service page, The steps include processing the molecular graph relating to the plurality of molecular structures as input to a chemical reaction prediction model so that a chemical reaction corresponding to the user query is predicted, The chemical reaction prediction model includes the step of performing chemical reaction predictions for the plurality of molecular structures, The steps include obtaining chemical reaction prediction results for the plurality of molecular structures from the chemical reaction prediction model, A method for generating an answer, comprising the step of generating an answer to a user query using the chemical reaction prediction result.

2. The aforementioned chemical reaction prediction model is The first module takes a molecular structure as input and predicts chemical reactions based on a graph, It includes a second module that analyzes information related to predicting chemical reactions associated with the multiple molecular structures from text data, In the step of predicting the chemical reaction, The method for generating an answer according to claim 1, characterized in that it generates the result product based on the chemical reaction prediction using the prediction result of the first module and the analysis result of the second module.

3. The method for generating an answer according to claim 2, characterized in that, in the step of predicting the chemical reaction, the result predicted through the first module is verified using the output data analyzed by the second module.

4. The aforementioned plurality of molecular structures include a first molecular structure and a second molecular structure, In the step of obtaining molecular graphs relating to the aforementioned multiple molecular structures, By converting the atoms constituting the first molecular structure into nodes and the bonding relationships between the atoms constituting the first molecular structure into edges, a first molecular graph including the nodes and edges corresponding to the first molecular structure is obtained. The method for generating an answer according to claim 1, characterized in that a second molecular graph including nodes and edges corresponding to the second molecular structure is obtained by converting the atoms constituting the second molecular structure into nodes and the bonding relationships between the atoms constituting the second molecular structure into edges.

5. The steps include labeling each of the aforementioned multiple molecular structures so that they are each assigned a different label, The answer generation method according to claim 4, further comprising the step of storing in the memory the first molecular graph obtained through the process of obtaining the molecular graph and information relating to the label assigned to the first molecular graph, and the second molecular graph obtained through the process of obtaining the molecular graph and information relating to the label assigned to the second molecular graph.

6. When the user query, which includes the plurality of molecular structures to which each has a different label, is received, the step of generating the answer is as follows: The plurality of molecular structures, each assigned a different label, are processed as input to the chemical reaction prediction model. Using the chemical reaction prediction results obtained from the chemical reaction prediction model, a response to the user query is generated. The response generation method according to claim 5, characterized in that if the response to the user query includes a specific molecular structure generated by the chemical reaction prediction model, a label is assigned to the specific molecular structure.

7. The step further includes providing a plurality of graphic objects corresponding to each of the plurality of molecular structures, each of which is assigned a different label, to a region of the service page where the user query is received. The method for generating an answer according to claim 5, characterized in that each of the plurality of graphic objects includes the first molecular graph and the second molecular graph.

8. The response generation method according to claim 7, characterized in that, based on the receipt of user input selecting one of the plurality of graphic objects, the service page is provided with detailed information corresponding to the graphic object selected by the user input.

9. In the step of predicting chemical reactions related to the aforementioned plurality of molecular structures, Using the information on the plurality of molecular structures, an embedding vector corresponding to the molecular graph is obtained. An attention calculation related to the interaction between atoms of the plurality of molecular structures is performed, and the embedding vector is updated based on the calculation. Using the updated embedding vectors, bond predictions and atom predictions are performed for the chemical reactions of the multiple molecular structures. The method for generating an answer according to claim 4, characterized in that, using the results of the bond prediction and the results of the atom prediction, a chemical reaction prediction result product predicted from the chemical reactions of the plurality of molecular structures is obtained.

10. The updated embedding vector is, The response generation method according to claim 9, characterized in that the attention score calculated based on the result of the attention calculation is updated by adding different biases to it according to the type of connections between the nodes constituting the first molecular graph and the second molecular graph.

11. The aforementioned combination prediction is performed by executing a dot product operation using the updated embedding vector. The atomic prediction predicts the atomic properties of the atoms corresponding to the updated embedding vector using the atomic property probability distribution of each atom corresponding to the updated embedding vector. The method for generating an answer according to claim 9, characterized in that the atomic properties include the charge state of the atoms which can change during the chemical reaction process of the plurality of molecular structures.

12. The method for generating an answer according to claim 11, characterized in that the predicted chemical reaction result corresponds to a final result stabilized through a diffusion feedback process.

13. The aforementioned service page is A first region is provided which provides a plurality of graphic objects corresponding to each of the plurality of molecular structures to which each has been assigned a different label, and detailed information relating to the plurality of molecular structures, A second area where the answer to the user query is provided, The third area for receiving user queries includes at least one of the following: Detailed information regarding the aforementioned multiple molecular structures is available at: The above-mentioned plurality of molecular structures include at least one of the following: molecular structure image, name, physical properties, and a string of characters according to the SMILES notation. The aforementioned detailed information is configured to be extracted from the user input or obtained from at least one pre-trained predictive model. The aforementioned pre-trained prediction model is The method for generating an answer according to claim 7, characterized in that it includes at least one of a chemical reaction prediction model that predicts chemical reactions between molecular structures and a molecular property prediction model that predicts the physical properties of molecular structures.

14. The steps include receiving an editing request for the plurality of molecular structures via a service page that provides answers to the user query, The method for generating an answer according to claim 4, further comprising the step of providing an editing interface on the service page that provides editing functions for the plurality of molecular structures.

15. The method for generating an answer according to claim 14, characterized in that the editing interface is provided with a molecular graph corresponding to at least one of the first molecular structure and the second molecular structure obtained by the molecular graph conversion in an editable state.

16. The editing of the aforementioned multiple molecular structures is This involves the deletion or repositioning of at least one of the nodes corresponding to each atom constituting each of the plurality of molecular structures, and the edges representing the bonding relationships of the atoms. The method for generating an answer according to claim 15, characterized by adding a new node corresponding to a new atom, or adding a new edge that generates a new bonding relationship to the atom.

17. Of the aforementioned multiple molecular structures, the edited molecular structure is stored in the memory. The edited molecular structure is given a new label to identify the edited molecular structure. When a user query containing the aforementioned new label is entered into the ultra-large-scale infrastructure model, The method for generating an answer according to claim 16, characterized in that the ultra-large-scale base model generates an answer using the edited molecular structure corresponding to the new label.

18. It is an answer generation system, The system includes memory and at least one processor, The memory and the processor work together to identify multiple molecular structures to be predicted based on user input received via the service page. The memory stores information about the plurality of molecular structures, Based on the plurality of molecular structures stored in the memory, a molecular graph relating to the plurality of molecular structures is obtained by converting atoms into nodes and interatomic bonds into edges. The service page receives user queries for predicting chemical reactions related to the multiple molecular structures. The molecular graphs relating to the multiple molecular structures are processed as input to a chemical reaction prediction model so that the chemical reaction corresponding to the user query is predicted. The chemical reaction prediction model performs chemical reaction predictions for the multiple molecular structures, From the chemical reaction prediction model, chemical reaction prediction results for the multiple molecular structures are obtained. A response generation system characterized by generating a response to a user query using the chemical reaction prediction result.

19. A program executed by one or more processes in an electronic device and stored on a computer-readable medium, The aforementioned program, The steps include identifying multiple molecular structures to be predicted based on user input received via the service page, The steps include storing information about the plurality of molecular structures in the memory, The steps include obtaining a molecular graph relating to the plurality of molecular structures by converting atoms into nodes and interatomic bonds into edges, based on the plurality of molecular structures stored in the memory, The steps include receiving user queries for predicting chemical reactions related to the multiple molecular structures via the service page, The steps include processing the molecular graph relating to the plurality of molecular structures as input to a chemical reaction prediction model so that a chemical reaction corresponding to the user query is predicted, The chemical reaction prediction model includes the step of performing chemical reaction predictions for the plurality of molecular structures, The steps include obtaining chemical reaction prediction results for the plurality of molecular structures from the chemical reaction prediction model, A program stored on a computer-readable recording medium, characterized by including a command to perform the step of generating a response to a user query using the chemical reaction prediction result.

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