KR-20260064656-A - Method for providing results of analysis of the relationship between natural products and disease using Artificial Intelligence

KR20260064656AKR 20260064656 AKR20260064656 AKR 20260064656AKR-20260064656-A

Abstract

A method for providing a result of analyzing the correlation between a natural product and a disease using artificial intelligence according to an embodiment of the present invention comprises: a data search step for searching for relevant data regarding natural product information and disease information input by a user; a core target extraction step for extracting a core target based on the relevant data regarding the natural product information and disease information by a target extraction unit of a server; an analysis step for analyzing the correlation between the natural product and the disease using the relevant data regarding the natural product information and disease information by an enrichment analysis unit of a server; and a result provision step for delivering the analysis result regarding the correlation between the natural product and the disease to a customer terminal by an output unit of a server.

Inventors

강재하
신지헌
이래형
정대식
강기성

Assignees

주식회사 인실리콕스

Dates

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

Claims (5)

As a method of providing analysis results of the relationship between natural products and diseases using artificial intelligence, A data retrieval step for searching for relevant data regarding natural product information and disease information entered by the user; A core target extraction step for extracting core targets based on relevant data regarding the natural product information and disease information by the target extraction unit of the server; and An analysis step of analyzing the correlation between natural products and diseases using relevant data regarding the natural product information and disease information by the concentration analysis unit of the server; and A result providing step of transmitting the analysis results regarding the correlation between the natural product and the disease to a customer terminal by the output unit of the server; Includes, The above result provision step includes generating a CTP network, After creating the CTP network, A step of inputting component pairs for the constituent components into a drug-drug interaction prediction model; and A step of outputting the drug interaction type and the probability (p) for each interaction type for each pair of components; Includes, 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 the step of outputting the probability (p) for each interaction type mentioned above, A step of correcting the probability (p) by calculating a modified probability (p') for each pair of components based on the above drug interaction type and the probability (p) for each interaction type; and A step of reflecting the modified probability (p') for each pair of components into the CTP network; Characterized by further performing, A method for providing analysis results on the relationship between natural products and diseases using artificial intelligence.
In paragraph 1, The above data retrieval step is, A step of searching 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 A step of searching for at least one disease-related gene associated with the input disease through a pre-prepared natural product database; including, A method for providing analysis results on the relationship between natural products and diseases using artificial intelligence.
In paragraph 1, 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 method for providing analysis results on the relationship between natural products and diseases using artificial intelligence.
In paragraph 3, The above result provision step includes generating a CTP network, but, The method includes the step of reflecting the modified probability (p') for each of the above-mentioned component pairs into the CTP network. A method for providing analysis results on the relationship between natural products and diseases using artificial intelligence.
A computer program stored on a computer-readable recording medium to execute a method of providing results of an analysis of the correlation between a natural product and a disease using artificial intelligence according to any one of claims 1 to 4, combined with hardware.

Description

Method for providing results of analysis of the relationship between natural products and disease using Artificial Intelligence The present invention relates to a method for providing results of an analysis 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 ac