A short biography

The main research area of Mr. Chung involves developing new optimization algorithms and training prediction models applicable to computational biology and structural bioinformatics.

One of the main research topics in structural bioinformatics is to understand the molecular recognition between receptor and small chemical compounds in aqueous environment and its dynamical interaction.

As diverse molecular recognitions can be simulated in silico based on statistical mechanics through molecular dynamics or Monte Carlo methodology, it is important to extend the capability of such methodology with respect to theory and software.

He is mainly aiming at developing a new simulation software, which can support a drug discovery project, starting from selection of druggable targets, finding hit compounds, to lead optimization.

Currently, several processes of a virtual screening to find new hit compounds was supported with the new package for in house drug discovery project related to the Parkinson's disease and it showed successful performance to find new compounds with high affinity.

Main components of the new software include new knowledge-based scoring function derived from structural database, classical force field and related molecular simulation algorithm, and global optimization methods to sample diverse conformations of protein ligand complexes.

Active collaboration with medicinal chemists is also his concern because such computational software should be verified and can only be enhanced by related experiments.