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Ph.D. Students


Iljung Jin (진일중)

B.S. in Computer Science and Information Engineering | CV

Cancer-Drug Response Prediction

Cancer patients show different responses on the treatment although they were treated with the same dose of the same drug; in some cases the therapy can aggravate the patient. So the identification of drug response on cancer is one of the important problems for precision medicine. I’m developing the deep learning model that can predict the cancer-drug response precisely and suggest MoA of cancer-drug response.


Dohyun Kim (김도현)

B.S. in Computer Science | CV

Cancer Prognosis Prediction

Cancer prognosis is important to decide which treatment should be applied to the patient. If we are able to predict the accurate prognosis of cancer patients, we can recommend and offer appropriate therapies or drugs to patients. I'm interested in deep learning based cancer prognosis prediction model construction and robust prognostic feature selection.


Hyunho Kim (김현호)

B.S. in Computer Science | CV

Compound toxicity prediction and optimization

The research field that I have been interested in is compound toxicity prediction and optimization through deep learning methods. Since the numerous experimental databases are available, data-driven methods could be a cost-effective and less-biased solution. I’m currently focusing on developing hERG blocker prediction (Cardiotoxicity) using interpretable deep learning methods.


Songyeon Lee (이송연)

M.S. in Electrical Engineering and Computer Science | CV

Cancer specific metabolic biomarker identification

For supporting rapid cancer growth and metastasis, some specific metabolic pathways are altered resulting in differentiation of the metabolites' concentration. To find metabolic biomarker candidates that represent the state of cancer and diagnosis, I'm applying the statistical methods and metabolic pathway analysis.


Bongsung Bae (배봉성)

B.S. in Computer Science | CV

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 | CV

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.


Haelee Bae (배해리)

M.S. in Electrical Engineering and Computer Science | CV

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 | CV

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.


Dongok Nam (남동옥)

M.S. in Electrical Engineering and Computer Science | CV

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