Development of

Drug Discovery Software

Based on Semantic Data

About

It takes a lot of time and money to develop a new drug. Cost reduction requires more precise planning, such as minimizing the side effects of drugs or targeting only those with specific genetic variants. It is also quite beneficial to find another applicability of the drug already in use.

Our project explores biomolecular targets to aid development, selects drug candidates, and recommends compounds as optimized drugs. To do this, we construct a genomic NGS data analysis pipeline of patients while building semantically enriched bio-comics network data for gene-protein- drug interactions.

Based on this, we propose new drug candidates that predict drug-target protein interactions through machine learning, while reducing drug resistance and side effects.

Director, Hong-Gee Kim

Architecture

Project Structure

Research Impact

Use of Disease Diagnosis

Utilizing the biologic process extraction of disease associated with the development of diagnostic kits

Drug Repurposing

Change of drug use based on biological process similarity

Patient-specific drug recommendations through bio-network calculations

Drug Candidate Recommendation

Recommendation of personalized drug candidates using personal genomic data

Newsroom

Participating Organizations

Seoul National University
SNU Dental Hospital
CbsBioscience

Developed by Biomedical Knowledge Engineering Laboratory