Development of

Drug Discovery Software

Based on Semantic Data


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-omics 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


Project Structure

Research Impact

Use of Disease Diagnosis

Utilizing the biological 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


Participating Organizations

Seoul National University
SNU Dental Hospital

Developed by Biomedical Knowledge Engineering Laboratory