SEUNGEON LEE
PORTFOLIO
Researching AI-driven drug discovery for medium-sized peptides, focusing on peptide language models and property prediction.
ABOUT
LEE SEUNGEON / 李 昇彦
Graduate School of Medicine, Kyoto University
Big Data Medical Science, Okuno Laboratory
AI Drug Discovery / Cheminformatics / Medium-sized Peptides / Machine Learning
I am a Master's student at the Graduate School of Medicine, Kyoto University, working in the Okuno Laboratory on AI-driven drug discovery. My current research focuses on transformer-based language models for medium-sized peptide property prediction, bridging the gap between small-molecule and protein informatics.
BIOGRAPHY
Graduate Student (M.S.)
Kyoto University, Graduate School of Medicine, Division of Human Health Sciences (Okuno Laboratory)
Undergraduate (B.Sc.)
Kwansei Gakuin University, School of Science and Technology, Department of Bioscience (Miura Laboratory)
BIBLIOGRAPHY
ARTICLES
- HELM-BERT: Topology-Aware Representations for Chemically Modified Peptides
- Empowering federated learning for robust compound–protein interaction prediction across heterogeneous cross-pharma domains
PREPRINTS
PRESENTATIONS
- TideFrag-GNN: A Fragment-Graph Foundation Encoder for Medium-Sized Peptide Property Prediction
- Comparative Evaluation of Peptide Representations and Similarity Measures
- Early-Run Confidence-Based Computational Budget Triage for Antibody-Antigen Complex Structure Prediction
- HELM-BERT: Topology-Aware Representations for Chemically Modified Peptides
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ゼロからわかるChatGPT — Practical AI Adoption for Healthcare Teams
-
HELM-BERT: A Transformer for Medium-sized Peptide Property Prediction
RECENT WORKS
WRITINGS
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Advances in Representation Learning, Interaction Understanding, and Candidate Molecule Design in AI for Small-Molecule Drug Discovery
SKILL
Python
Research experience with Python programming
Deep learning implementation with PyTorch
Machine Learning
Transformer-based language models
Peptide property prediction & federated learning
Cheminformatics
HELM notation for peptide representation
Compound-protein interaction modeling
Korean
Native speaker (mother tongue)
English
TOEIC 880
Japanese
JLPT N1
CONTACT
Feel free to reach out via SNS or email.