Seokhyun Moon
Ph.D. Candidate in KAIST Chemistry
Ph.D. Candidate in KAIST Chemistry
My research focuses on the development of advanced computational frameworks for understanding and predicting molecular interactions. Starting from PIGNet, a Physics-informed graph neural network for binding affinity estimation, I am currently working on unifying accurate estimation of energetically favorable states and their binding affinities, by integrating physics and energy-based models. I'm also interested in improving hit compounds by editing their building blocks with generative AI.
Discrete Diffusion Schördinger Bridge Matching for Graph Transformation
Jun Hyeong Kim*, Seonghwan Kim*, Seokhyun Moon*, Hyeongwoo Kim*, Jeheon Woo*, and Woo Youn Kim†
Arxiv Preprint, 2024
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Hyeongwoo Kim*, Seokhyun Moon*, Wonho Zhung, and Woo Youn Kim†
Arxiv Preprint, 2024
Toward Generalizable Structure-Based Deep Learning Models for Protein-Ligand Interaction Prediction: Challenges and Strategies
Seokhyun Moon*, Wonho Zhung*, and Woo Youn Kim†
WIREs Computational Molecular Science, 2024
PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening
Seokhyun Moon*, Sang-Yeon Hwang, Jaechang Lim, and Woo Youn Kim†
Digital Discovery, 2024
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
Hyeonsu Kim*, Jeheon Woo*, Seonghwan Kim*, Seokhyun Moon*, Jun Hyeong Kim*, and Woo Youn Kim†
NeurIPS, 2023
PIGNet: a physics-informed deep learning model toward generalized drug-target interaction prediction
Seokhyun Moon*, Wonho Zhung*, Soojung Yang*, Jaechang Lim, and Woo Youn Kim†
Chemical Science, 2022
Recipient, 10th EDISON Computational Chemistry software application competition
Recipient, KAIST Undergraduate Research Program participaiton prize
Recipient, Qualcomm-KAIST Innovation Awards 2019