Members
M.S. Students
Bongsung Bae (배봉성)
B.S. in Computer Science
De Novo Optimization
I’m interested in the problem of de novo molecular optimization in computational drug design. In terms of methods, I am studying deep generative modeling and reinforcement learning for data-driven optimization.
Hansol Lee (이한솔)
B.S. in Statistics
Antimicrobial peptide prediction
Bacteria are always evolving and becoming antibiotic resistant due to high rates of mutations in their DNA. Antimicrobial peptides (AMPs) kill invasive bacteria through non-specific mechanisms, and compared to conventional drugs, AMPs have shown a lower likelihood for bacteria to form resistance to. My goal is to facilitate the drug discovery process of AMP-influenced antibiotics through deep learning recognition of AMPs using protein data.
Dongok Nam (남동옥)
B.S. in Genetic Engineering
Cytochrome P450 inhibition prediction
Human cytochrome P450 enzyme (CYP450) is included in drug metabolism. Since inhibition of CYP450 may lead to changed metabolic pathways of drug substrates and adverse drug-drug interaction, it plays an important role in drug discovery. I’m studying the prediction of inhibitors of CYP450 via deep learning model.
Haelee Bae (배해리)
B.E. in Computer Science and Engineering
Hit identification via deep learning
Identification of hit is important in drug discovery. Because there are many compounds and proteins, It takes much time and cost to find hit identification with experiment. Computation method can help to discover whether the compound and protein have interaction or not. I am interested in applying deep learning based data driven features to hit identification.
Minsu Park (박민수)
B.S. in Chemistry
Compound Membrane permeability prediction
I’m interested in predicting permeability of drugs through the biological membranes. Drugs need to pass biological membranes to reach their target, permeability prediction is important for the drug efficacy. Membrane permeation is highly associated with molecular solvation property, I am studying about solvation property prediction with deep learning methods.