Ph.D Candidate
- Generative flow network for structure-based drug discovery (TacoGFN, RxnFlow)
- The first deep learning framework for protein-based pharmacophore modeling (PharmacoNet, OpenPharmaco)
- Development of molecular voxelization tool (MolVoxel)
About Me
I am currently a Ph.D. student in the Department of Chemistry, KAIST, under the supervision of Prof.
Woo Youn Kim.
My research area is AI-based material discovery, with a particular focus on small molecule drugs. I have developed deep learning models in various areas of drug discovery including pharmacophore modeling, virtual screening, property prediction, and generative models. Looking ahead, my interest will be extended to generative flow network (GFlowNet).
My research area is AI-based material discovery, with a particular focus on small molecule drugs. I have developed deep learning models in various areas of drug discovery including pharmacophore modeling, virtual screening, property prediction, and generative models. Looking ahead, my interest will be extended to generative flow network (GFlowNet).
(Last Updated: 2024.11.07)
Research Experience
AI Scientist Intern
- Building block-based graph generative model for synthesizable molecular design (BBAR)
- Reaction template-based SMILES generative model for synthesizable molecular design
- Protein-ligand binding pose prediction by score-based modeling
Undergraduated Researcher
- Designing the deep learning framework for a new drug-likeness scoring
Research Area
- Drug Discovery
- Small Molecule
- Generative Model
- GFlowNet
- Pharmacophore Modeling
Education
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Ph.D in ChemistryKAISTAug 2022 - Aug 2027 (expected)
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B.S. in Chemistry & Computer Science (Double Major)KAISTFeb 2018 - Aug 2022
Skills
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Python
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PyTorch
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PyTorch Geometric
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PyTorch Lightning
Languages
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Korean
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English