Method and apparatus for deriving new drug candidate substance

A computing apparatus for deriving a new drug candidate substance, the computing apparatus comprising:

• a knowledge network generating unit configured to generate a refined knowledge network in which nodes representing biological entities are connected by using a connecting line representing a correlation between the nodes based on a database (DB) for each biological entity type and a DB for a correlation between biological entities;
• a basic drug determining unit configured to determine a basic drug for deriving a new drug candidate substance by analyzing drug-disease node pairs existing in the refined knowledge network;
• an analogous substance acquiring unit configured to obtain 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;
• and
• a physical property predicting unit configured to predict a physical property of the analogous substance through an artificial neural network-based physical property prediction model, wherein
• the biological entity includes at least one of a gene, a protein, a metabolite, a symptom, a disease, a compound, and a drug,
• a category of the correlation includes at least one of interact, participate, covariate, regulate, associate, bind, upregulate, cause, resemble, treat, downregulates, palliate, present, localize, include, and express, and
• the basic drug determining unit calculates standard scores of proximities of drug-disease node pairs existing in the refined knowledge network, selects at least one drug-disease node pair with the standard score of the proximity less than a reference value, and determines the drug indicated by a source node of the selected at least one drug-disease node pair as the basic drug when a intermediate node indicating a disease different from a disease indicated by a target node exists on a path for the drug-disease node pair,
• a simplified molecular-input line-entry system (SMILES)-based character string of the basic drug is input in the structure prediction model,
• wherein the physical property includes at least one of solubility, hydration energy, melting point, boiling point, toxicity, electrical stability, excited state property, protein-ligand binding, dissociation constant, and membrane permeability,
• the physical property prediction model is independently generated for each of the physical properties,
• the physical property prediction model is a classification model or a regression model, and
• the learning about the physical property prediction model is performed by applying substances with known physical properties and physical properties of the substances as inputs and outputs, respectively.