Provided is a method for predicting pharmacological effects of a new drug candidate substance performed by a computing device, wherein the method may include receiving information on a new drug candidate substance, selecting a structural similarity type, which is a reference for determining the similarity between substances, preparing pharmacological effect prediction models corresponding to the selected structural similarity type from among a plurality of pharmacological effect prediction models created by structural similarity type and pharmacological class, and predicting whether the new drug candidate substance will have a pharmacological class corresponding to each of the pharmacological effect prediction models based on an output value obtained by inputting information on the new drug candidate substance into each of the prepared pharmacological effect prediction models.
A method for predicting a pharmacological effect of a new drug candidate material carried out by a computing device may comprise the steps of: receiving information of a new drug candidate material; selecting a type of structural similarity which is a reference for determining similarity between materials; among a plurality of pharmacological effect prediction models generated for the respective types of structural similarity and respective pharmacological classifications, preparing pharmacological effect prediction models corresponding to the type of structural similarity which has been selected; and, on the basis of output values acquired by inputting the information of the new drug candidate material into each of the prepared pharmacological effect prediction models, predicting whether the new drug candidate material will belong to the pharmacological classifications corresponding to the respective pharmacological effect prediction models. The types of structural similarity may be classified in detail on the basis of which calculation method, among a Dice similarity calculation method and a Tanimoto similarity calculation method, to apply, whether to apply a Bemis-Murcko scaffold, and whether to apply hydrogen atom bonding, and the plurality of pharmacological effect prediction models generated for the respective types of structural similarity and the respective types of pharmacological classification may each be generated on the basis of machine learning using materials for which whether the materials belong to specific pharmacological classifications is already known.
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.
G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p. ex. leurs effets secondaires ou leur usage prévu
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
G16C 20/40 - Recherche de structures chimiques ou de données physicochimiques
4.
APPARATUS AND METHOD FOR PROCESSING DATA DISCOVERING NEW DRUG CANDIDATE SUBSTANCE
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.
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.
G16B 45/00 - TIC spécialement adaptées à la visualisation de données liées à la bio-informatique, p. ex. affichage de cartes ou de réseaux
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
G16H 70/60 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pathologies
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p. ex. leurs effets secondaires ou leur usage prévu
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
6.
METHOD AND APPARATUS FOR DERIVING NEW DRUG CANDIDATE SUBSTANCE
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.
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.
A method for deriving a new drug candidate substance by means of a computing device may be disclosed. The method for deriving a new drug candidate substance comprises the steps of: creating a refined knowledge network in which nodes representing respective biological entities are connected using connection lines representing correlations between the nodes; analyzing drug-disease node pairs present in the refined knowledge network to determine a basic drug for deriving a new drug candidate substance; and using an artificial neural network-based structure prediction model to acquire an analogous substance having a chemical structure similar to that of the basic drug, wherein the biological entities include at least one among genes, proteins, metabolites, symptoms, diseases, compounds, and drugs, and a simplified molecular-input line-entry system (SMILES)-based character string of the basic drug may be input to the structure prediction model.
A data processing method for discovering a new drug candidate, of a data processing device, comprises the steps of: generating, from an omics DB, a DB matrix comprising selected biological entities and selected correlation types; receiving a predetermined keyword; extracting, from the DB matrix, at least one biological entity related to the predetermined keyword; extracting, from the DB matrix, a correlation between the predetermined keyword and the at least one biological entity; building a first knowledge network in which a plurality of nodes, comprising the predetermined keyword and the at least one biological entity, are connected in accordance with the correlation; calculating a graph theory indicator of the first knowledge network; and building a second knowledge network by using some nodes, among the plurality of nodes, extracted using the graph theory indicator.
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p. ex. leurs effets secondaires ou leur usage prévu
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
G16C 60/00 - Science informatique des matériaux, c.-à-d. TIC spécialement adaptées à la recherche des propriétés physiques ou chimiques de matériaux ou de phénomènes associés à leur conception, synthèse, traitement, caractérisation ou utilisation
10.
DATA PROCESSING DEVICE AND METHOD FOR DISCOVERING NEW DRUG CANDIDATE MATERIAL
A data processing method for discovering a new drug candidate material by a data processing device according to an embodiment of the present invention comprises the steps of: receiving a predetermined search word; extracting at least one biological entity related to the predetermined search word from a big data database (DB); extracting the correlation between the predetermined search word and the at least one biological entity; building a first knowledge network in which a plurality of nodes, comprising the predetermined search word and the at least one biological entity, are connected in accordance with the correlation; calculating a graph theory indicator of the first knowledge network; and building a second knowledge network by means of some nodes extracted from the plurality of nodes by means of the graph theory indicator.
A method for processing data for discovering a new-drug candidate material according to an embodiment of the present invention comprises the steps of: receiving input, via a user interface, of at least some omics levels among a plurality of omics levels constituting omics; receiving, via the user interface, at least some mutual relationship types among a plurality of mutual relationship types constituting the omics; selecting a DB related to the at least some omics levels and a DB related to the at least some mutual relationship types from among omics DBs including data per omics level and data per mutual relationship type; generating a first matrix consisting of the DB related to the at least some omics levels and the DB related to the at least some mutual relationship types; receiving input of a predetermined search word via the user interface; extracting a plurality of biological entities related to the predetermined search word and a mutual relationship between the plurality of biological entities from among the DB related to the at least some omics levels and the DB related to the at least some mutual relationship types; and generating a multi-omics network by connecting a plurality of nodes including the plurality of biological entities according to the mutual relationship between the plurality of biological entities.
A data processing method for discovering new drug candidate substances in a data processing apparatus according to one embodiment of the present invention comprises the steps of: receiving a predetermined search word input 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 of the druggable paths by using an artificial neural network (ANN) model; selecting some druggable paths having high DP indexes, among the plurality of druggable paths; extracting absorption, distribution, metabolism, excretion, and toxicity (ADMET) information of the druggable paths by using an ADMET model; and outputting ADMET information and a DP index for each of the some druggable paths.
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