Biography

Gyutae Lim is a postdoctoral research fellow in the Drug Metabolism and Pharmacokinetics Core within NCATS’ Division of Preclinical Innovation, where his primary focus is on the use of machine-learning methods with in vitro absorption, distribution, metabolism and excretion (ADME) data sets for developing in silico ADME models to predict novel compound properties. By applying in silico ADME models while designing drug-like molecules and identifying lead candidates, he aims to accelerate the discovery of new drugs for unmet medical needs.

Lim earned his doctorate in bioinformatics from the University of Science and Technology in South Korea, where he specialized in analyzing protein structures using computational methods. During his studies, he focused on developing methods for predicting new or active binding sites for ligands, using the big data of 3-D protein structures.

Following his graduate studies, Lim continued his research as a postdoctoral researcher at the Korea Research Institute of Chemical Technology, where he focused on the repositioning and repurposing of existing compounds for new indications and targets through the analysis of gene expression data. Based on his research in protein–compound interactions, he expanded the scope of his research by applying machine-learning techniques to various biological data sources to develop more accurate prediction models.

Research Topics

Lim’s research primarily focuses on accurately predicting the efficacy of novel drug candidates before they advance to clinical trials, as well as helping researchers identify new drug candidates more quickly to reduce the time and costs associated with the drug discovery process. To achieve these goals, he applies state-of-the-art machine-learning methods to leverage a range of data sources to screen drug compounds that are safe and effective for human use, considering such factors as absorption, distribution, metabolism, excretion and toxicity. He also focuses on identifying compounds that can efficiently bind to the active site of proteins, using protein structure analysis as a key tool.

Selected Publications

1. Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived From Machine Learning and Transcriptome-Based Drug Repositioning Approaches
2. Identification of New Target Proteins of a Urotensin-II Receptor Antagonist Using Transcriptome-Based Drug Repositioning Approach