Abstract: A method for processing data for discovering a new drug candidate substance by a data processing apparatus includes receiving at least some of omics levels that make up omics through a user interface, receiving at least some types of mutual association degrees among a plurality of types of mutual association degrees, selecting a DB for the at least some of the omics levels and a DB for the at least some types of mutual association from an omics DB including data for each omics level and data for each type of mutual association, generating a first matrix composed of the DB for the at least some of the omics levels and the DB for the at least some types of mutual association degrees, receiving a predetermined search word through the user interface, extracting a plurality of biological entities, and generating a multi-omics network in which a plurality of nodes.

Abstract: A method includes generating a DB matrix composed of a selected biological entity and a selected type of mutual association degree from an omics DB, receiving a search word, extracting biological entities, extracting a degree of mutual association between the search word and the biological entities from the DB matrix, generating a first knowledge network in which the search word and each of the biological entities are used as nodes and a plurality of nodes are connected using a connection line according to a degree of mutual association between the search word and the biological entities or a degree of mutual association between the biological entities, computing a graph theory index for each of the plurality of nodes of the first knowledge network, and generating a second knowledge network using some nodes selected using the graph theory index among the plurality of nodes of the first knowledge network.

Abstract: A method for processing data for discovering a new drug candidate substance by a data processing apparatus, includes receiving a predetermined search word, extracting at least one biological entity related to the predetermined search word from a big data database (DB), extracting a degree of mutual association between the predetermined search word and the at least one biological entity, generating a first knowledge network in which a plurality of nodes including the predetermined search word and the at least one biological entity are connected according to the degree of mutual association, computing a graph theory index of the first knowledge network, and generating a second knowledge network using some nodes of the plurality of nodes of which the graph theory index is equal to or greater than a threshold value.

Abstract: A method for processing data for discovering a new drug candidate substance by a data processing apparatus includes receiving at least some of omics levels that make up omics through a user interface, receiving at least some types of mutual association degrees among a plurality of types of mutual association degrees, selecting a DB for the at least some of the omics levels and a DB for the at least some types of mutual association from an omics DB including data for each omics level and data for each type of mutual association, generating a first matrix composed of the DB for the at least some of the omics levels and the DB for the at least some types of mutual association degrees, receiving a predetermined search word through the user interface, extracting a plurality of biological entities, and generating a multi-omics network in which a plurality of nodes.

Abstract: A method for deriving a new drug candidate substance that is executed by a computing apparatus is disclosed. The method includes generating a refined knowledge network in which nodes representing biological entities are connected to each other by using a connecting line representing a correlation between the nodes, determining a basic drug for deriving a new drug candidate substance by analyzing drug-disease node pairs existing in the refined knowledge network, and obtaining an analogous substance having a chemical structure analogous to a structure of the basic drug by using an artificial neural network-based structure prediction model. The biological entity includes at least one of a gene, a protein, a metabolite, a symptom, a disease, a compound, and a drug, and a simplified molecular-input line-entry system (SMILES) based character string of the basic drug is input in the structure prediction model.

Abstract: A data processing method for discovering a new drug candidate substance by a data processing apparatus according to an embodiment of the present invention includes receiving a predetermined search word through a user interface unit, extracting a plurality of druggable paths related to the predetermined search word and a druggable path (DP) index for each druggable path by using an artificial neural network (ANN) model, selecting some of the druggable paths having a relatively high DP index among the plurality of druggable paths, extracting information on absorption, distribution, metabolism, excretion, and toxicity (ADMET information) for the some of the druggable paths by using an ADMET model, and outputting the DP index and the ADMET information for each of the some of the druggable paths.