About Me
I am currently a Ph.D. candidate in chemistry at KAIST.
I am interested in deep learning methods for chemistry and drug discovery - quantum chemistry, molecular optimization, predicting biomolecule structures, and synthesis planning.
Google Scholar | GitHub | LinkedIn
Research Interest
My research focuses on how to integrate physics and chemistry into deep learning methods to solve large-scale systems such as proteins. I am very interested in Physics-Informed Neural Networks (PINN), geometric deep learning, and generative models.
Educations
Korea Advanced Institute of Science and Technology (KAIST)
September 2020 - present
Ph.D. candidate in Chemistry
Korea Advanced Institute of Science and Technology (KAIST)
March 2016 - August 2020
Major in Chemistry, Minor in Physics
KAIST presidential fellowship (KPF)
Kangwon Science High School
March 2014 - February 2016
Publications and preprints
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Jun Hyeong Kim*, Seonghwan Kim*, Seokhyun Moon*, Hyeongwoo Kim*, Jeheon Woo*, Woo Youn Kim
ICLR 2025
Paper
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Hyeongwoo Kim*, Seokhyun Moon*, Wonho Zhung, Jaechang Lim, Woo Youn Kim
Arxiv preprints, 2024
Paper | Code
3D molecular generative framework for interaction-guided drug design
Wonho Zhung*, Hyeongwoo Kim, Woo Youn Kim
Nature Communications, 2024
Paper | Code
DFRscore: Deep learning-based scoring of synthetic complexity with drug-focused retrosynthetic analysis for high-throughput virtual screening
Hyeongwoo Kim*, Kynghoon Lee*, Chansu Kim, Jaechang Lim, Woo Youn Kim
Journal of Chemical Information and Modeling, 2023
Paper | Code
Presentations
2024 Spring Korean Institute of Chemical Engineers Meeting - Oral
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Delicate Control of Multiple Molecular Properties
Hyeongwoo Kim*, Seokhyun Moon*, Wonho Zhung, Jaechang Lim, Woo Youn Kim
PPT
2022 Spring Korean Chemistry Society Conference - Poster
ENSS: Estimating the Number of Synthetic Steps by Graph-based Deep Learning for Virtual Screening (2022)
Hyeongwoo Kim*, Kynghoon Lee*, Woo Youn Kim
Poster
Awards
The Excellent Poster Award in the 2022 Spring Korean Chemistry Society Conference
ENSS: Estimating the Number of Synthetic Steps by Graph-based Deep Learning for Virtual Screening (2022)
Poster
Draft papers
Differentiable Structure of the Kabsch Algorithm (2025)
Hyeongwoo Kim*
Paper