KR-20260064657-A - System for providing results of analysis of the relationship between natural products and disease using Artificial Intelligence

KR20260064657AKR 20260064657 AKR20260064657 AKR 20260064657AKR-20260064657-A

Abstract

A system for providing an analysis result of the relationship between a natural product and a disease using artificial intelligence according to an embodiment of the present invention comprises: a search unit for searching for relevant data regarding natural product information and disease information input by a user; a target extraction unit for extracting a core target based on the relevant data regarding the natural product information and disease information; a concentration analysis unit for analyzing the relationship between the natural product and the disease using the relevant data regarding the natural product information and disease information; and an output unit for delivering the analysis result regarding the relationship between the natural product and the disease to a customer terminal; wherein the target extraction unit comprises: a potential target extraction unit for extracting at least one potential target associated with a constituent component; and a core target extraction unit for filtering the potential targets based on centrality analysis criteria to extract at least one core target.

Inventors

강재하
신지헌
이래형
정대식
강기성

Assignees

주식회사 인실리콕스

Dates

Publication Date: 20260507
Application Date: 20260223
Priority Date: 20241031

Claims (12)

As a system that provides analysis results of the correlation between natural products and diseases using artificial intelligence, A search unit that searches for relevant data regarding natural product information and disease information entered by the user; A target extraction unit that extracts a core target based on relevant data regarding the above natural product information and disease information; and A concentration analysis unit that analyzes the correlation between natural products and diseases using relevant data regarding the above natural product information and disease information; and An output unit that transmits the analysis results regarding the correlation between the above natural product and the disease to a customer terminal; Includes, The above target extraction unit is, A potential target extraction unit that extracts at least one potential target associated with a component; and A core target extraction unit that filters the above potential targets based on centrality analysis criteria to extract at least one core target; Characterized by including, A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 1, The above search unit is, Search for the components of the input natural product and at least one related target protein for each of the components through a pre-prepared natural product DB, and Searching for at least one disease-related gene associated with an input disease through a pre-prepared natural product database, A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 2, The above target extraction unit is, It further includes a common target extraction unit for extracting common targets commonly included in the above natural product and the above disease, and The above common target extraction unit extracts a protein commonly included in the set of related target proteins and the set of disease-related genes as a common target before extracting the potential target, and The above potential target extraction unit filters the common targets based on a relevance score entered by the user to extract at least one potential target associated with a component. A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 3, After the above target extraction unit extracts the above common targets, The components of the natural product and the common targets mentioned above are input into a drug-protein interaction prediction model, and Outputs the result of component-target interaction between the component and the target, and The above component-target interaction result is the presence or absence of interaction between the component and the target, or an affinity score, and The above potential target extraction unit extracts potential targets based on the component-target interaction results in addition to the relevance score, A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 2, The above target extraction unit outputs the result of component-target interaction between the constituent component and the disease-related gene, and The above component-target interaction result is the presence or absence of interaction between the component and the target, or an affinity score, and The above potential target extraction unit extracts at least one potential target associated with a component by filtering based on the presence or absence of interaction or affinity score entered by the user. A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 5, Outputting the above component-target interaction results involves inputting the above components and the above disease-related genes into a drug-protein interaction prediction model and outputting the component-target interaction results between the components and the disease-related genes. A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 2, The above output unit generates a CTP network, and After creating the CTP network, For the components, input component pairs into a drug interaction prediction model, and For each pair of components, output the drug interaction type and the probability (p) for each interaction type, and The above drug interaction prediction model is an artificial intelligence model trained to perform calculations on gene expression information based on drug structure information and drug attribute information, and After outputting the probability (p) for each interaction type mentioned above, The probability (p) is corrected by calculating the modified probability (p') for each pair of components based on the above drug interaction type and the probability (p) for each interaction type, and Reflecting the modified probability (p') for each pair of components into the CTP network, A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In Paragraph 7, The above modified probability (p') is expressed by the following formula, and p' = p * m_s * max(1-λσ, 0) Here, p is the probability for each interaction type, m_s is the severity factor, λ is the uncertainty penalty constant, and σ is the standard deviation (prediction uncertainty), A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 8, The above output unit generates a CTP network, but, Reflecting the modified probability (p') for each of the above component pairs into the CTP network, A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In paragraph 2, After extracting the above potential targets, the method further includes a network generation unit that performs PPI analysis on the above potential targets to generate a protein interaction network. A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In Paragraph 10, The above concentration analysis unit performs a first concentration analysis and a second concentration analysis for each core target after extracting the core target, and The method comprises performing the first enrichment analysis to predict the function of each core target and generating a gene ontology, and performing the second enrichment analysis to generate a biological pathway of each core target. A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.
In Paragraph 11, The above network generation unit is, After performing a second concentration analysis in the concentration analysis unit, the relationship between the constituent components of the natural product, key targets, and biological pathways is expressed as a CTP network. A system that provides analysis results on the correlation between natural products and diseases using artificial intelligence.

Description

System for providing results of analysis of the relationship between natural products and disease using Artificial Intelligence The present invention relates to a system that provides analysis results of the correlation between natural products and diseases using artificial intelligence. Network pharmacology is an analytical method based on systems biology that explores drug efficacy by connecting the active ingredient of a drug, target genes (or proteins), and diseases (pathologies) associated with the target genes to form a network. Traditionally, new drug development has proceeded based on the single-component, single-target theory—that is, identifying components that interact with a single target. However, while this methodology offers advantages in terms of safety, it was not suitable for proving the efficacy of natural products, which are multi-component, multi-target. In this context, the concept of multi-compound, multi-target is gaining new attention. In particular, since herbal medicines, a type of natural product, are composed of diverse components, this is a new methodology attracting significant interest in the field of herbal medicine development. Specifically, it involves networking the components contained in herbal ingredients and understanding them in conjunction with biological and disease networks. This approach not only facilitates a comprehensive understanding of herbal ingredients but also enables the analysis of the associations between herbal ingredients, their corresponding target proteins, and disease genes. To conduct research based on such network pharmacology, it is fundamentally necessary to secure a large amount of data at each stage of the study. However, natural products, such as herbal medicines, are extremely diverse, and each product consists of multiple constituent components, resulting in the existence of multiple sets of protein data associated with those components. Furthermore, numerous diseases exist, each with multiple related genes. Moreover, as this vast amount of data is currently dispersed across various institutions, acquiring the necessary data remains a difficult reality. Furthermore, due to the nature of network pharmacology, researchers must perform a relatively large number of activities when conducting drug-based studies on the relationship between natural products and diseases. The primary goal of Korean traditional medicine research is to elucidate the mechanisms of how herbal medicines interact within the human body, and the core of the research is to identify the associations between drugs, genes, and diseases. If a researcher has identified a specific herbal medicine of interest and a related disease, they must undergo numerous trials and errors to determine the connection between the two, given the vast amount of associated data. In addition, various programs must be used to visualize and understand the interactions between data. FIG. 1 is a conceptual diagram of a system that performs a method of providing results of an analysis of the relationship between natural products and diseases using artificial intelligence according to one embodiment of the present invention. FIG. 2 is a configuration diagram of a server that performs a method of providing results of an analysis of the correlation between natural products and diseases using artificial intelligence according to an embodiment of the present invention. Figure 3 is a reference diagram to explain the concept of the components of natural products and related target proteins. Figure 4 is a reference diagram to explain the concept of disease-related genes. FIG. 5 is a flowchart of a method for providing results of an analysis of the correlation between natural products and diseases using artificial intelligence according to an embodiment of the present invention. Figure 6 is a flowchart regarding the data retrieval step of Figure 5. FIG. 7 is a flowchart relating to the core target extraction step in one embodiment of the present invention. FIG. 8a is a flowchart for one embodiment of extracting potential targets based on relevance scores in the core target extraction step of FIG. 7. FIG. 8b is a flowchart relating to an embodiment in which the component-target interaction result is additionally applied in the step of extracting potential targets of FIG. 8a. FIG. 9 is a flowchart relating to another embodiment of extracting potential targets based on relevance scores and affinity scores in the core target extraction step of FIG. 5. Figure 10 is a flowchart regarding the correlation analysis step. Figure 11 is a flowchart regarding the correction of the probability (p) by interaction type of component pairs in the correlation analysis step of Figure 10. Figure 12 is a diagram exemplarily showing the constituent components of a natural product and related target protein information. Figure 13 is a diagram visually illustrating a method for extracting potential targets from a common target accordin