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Contact
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Full Name: Jeonghyeon Gu (구정현)
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Email: rnwjdgus96@gmail.com
Education
Seoul National University •
Department of Chemical & Biological Engineering (2014~2016)
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B.S. in Pharmacy (2016~2020)
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M.S. in Interdisciplinary Program of Artificial Intelligence (2021~2023)
License
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Pharmacist License, Ministry of Health and Welfare (Republic of Korea) (2020)
Awards
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한빛사 논문 선정 (2023)
Publications
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Gu, J.*, Bang, D.*, Yi, J.*, Lee, S., Kim, D. & Kim, S. (2023). A Model-agnostic Framework to Enhance Knowledge Graph-based Drug Combination Prediction with Drug-Drug Interaction Data and Supervised Contrastive Learning. Briefings in Bioinformatics
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Lim, S., Kim, Y., Gu, J., Lee, S., Shin, W., & Kim, S. (2023). Supervised chemical graph mining improves drug-induced liver injury prediction. iScience
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Bang, D.*, Gu, J.*, Park, J., Jeong, D., Koo, B., Yi, J., ... & Lee, S. (2022). A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective. International Journal of Molecular Sciences
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Lim, S., Lee, S., Piao, Y., Choi, M., Bang, D., Gu, J., & Kim, S. (2022). On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Computational and Structural Biotechnology Journal
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Bang Y., … Gu, J., .., Hasun Yu (2024). Accurate antibody loop structure prediction enables zero-shot design of target-specific antibodies. bioRxiv
Interests
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Research topics & techniques
Currently (2023) interested in chemical graph generation, diffusion models, 3D geometric learning and chemical spectrometry (NMR, MS, IR, …) data.
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Career
Interested in business development, communication, networking & governance.
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Private life
Love delicious foods, wines and cooking.
Experience
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A working research group of people interested in Deep Learning
Official Member (2021~2024.02)
President (2023~2024.02)
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Scholarship
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Merit based Scholarship (2015.03, 2015.09, 2016.03)
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SNU AI Fellowship Scholarship (2022.03, 2022.09)
Skills
Python (fluent), Rust (beginner) PyTorch, Pandas, Numpy, Matplotlib Korean (native), English (fluent), Spanish (intermediate)