Seokhyun Moon
Ph.D. Candidate, KAIST Chemistry ยท Intelligent Chemistry Lab
aidd / molecular docking / molecular generative models
I am a Ph.D. Candidate in KAIST Chemistry working on AI-driven drug discovery. My recent work focuses on co-folding, molecular docking, and molecular generative models, with an emphasis on reliable structure and interaction modeling.
This site collects research notes, technical posts, and project logs. Outside my core research, I am also increasingly interested in AI agent-driven automation and productivity workflows.
Research
My research focuses on developing computational frameworks for understanding and predicting molecular interactions. Building on PIGNet, a physics-informed graph neural network for binding affinity estimation, I have worked on structure and binding affinity prediction for diverse biomolecular complexes. I am currently developing next-generation foundation models for biomolecular complex structure prediction.
In parallel, I am interested in molecular generative models for lead optimization and de novo drug design, and more recently in phenotypic drug discovery using virtual cells, where my previous work can be effectively leveraged.
- Biomolecular complex structure prediction
- Co-folding and molecular docking
- Physics-informed and equivariant deep learning
- Molecular generative models for lead optimization
- AI4Science infrastructure for reproducible research
Publications
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
Joongwon Lee, Seonghwan Kim, Seokhyun Moon, Hyunwoo Kim, and Woo Youn Kim
ICLR 2026
MoAgent: A Hypothesis-Driven Multi-Agent Framework for Drug Mechanism of Action Discovery
Jun Hyeong Kim, Seokhyun Moon, Seonghwan Kim, Junhyeok Jeon, Taein Kim, Jisu Seo, Songmi Kim, and Woo Youn Kim
NeurIPS 2025 Workshop: AI4D3, FM4LS
Discrete Diffusion Schrodinger Bridge Matching for Graph Transformation
Jun Hyeong Kim, Seonghwan Kim, Seokhyun Moon, Hyeongwoo Kim, Jeheon Woo, and Woo Youn Kim
ICLR 2025, NeurIPS 2024 Workshop: AI for New Drug Modalities
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung, and Woo Youn Kim
Under Review
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
Scaffold-based molecular design with a graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seokhyun Moon, Seungsu Kim, and Woo Youn Kim
Chemical Science, 2020
Blog
- 2026-03-11» Hello