Wonho Zhung

Ph.D. Candidate, KAIST

Intelligent Chemistry Lab

Biography

I am a Ph.D. student at the Intelligent Chemistry Lab at KAIST (PI: Prof. Woo Youn Kim). My research focuses on developing deep learning methodologies to advance structure-based drug discovery. My work spans molecular design and protein-ligand interaction prediction while integrating physical and chemical principles into deep learning models to enable more generalizable and interpretable drug discovery. I am currently interested in (but not limited to) the following topics:

AI for chemistry and biology
Molecular generative modeling
Structure-based drug design

Interests

Deep Learning for Chemical & Biological Applications
Structure-based Drug Design (SBDD)
Generative Models

Education

MSc & PhD Integrated Program in Chemistry
Korea Advanced Institute of Science and Technology (KAIST)
BSc in Chemistry and Biological Science (Double Major), 2020
Korea Advanced Institute of Science and Technology (KAIST)
High School, 2016
Korea Science Academy of KAIST (KSA)

Recent Publications

BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design

Joongwon Lee, Wonho Zhung, Jisu Seo, Woo Youn Kim*

HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model

Liver toxicity poses a critical challenge in drug development due to the liver’s pivotal role in drug metabolism and …

Jiyeon Han, Wonho Zhung, Insoo Jang, Joongwon Lee, Min Ji Kang, Timothy Dain Lee, Seung Jun Kwack, Kyu-Bong Kim, Daehee Hwang, Byungwook Lee, Hyung Sik Kim*, Woo Youn Kim*, Sanghyuk Lee*

NCIDiff: Non-covalent Interaction-generative Diffusion Model for Improving Reliability of 3D Molecule Generation Inside Protein Pocket

Advancements in deep generative modeling have changed the paradigm of drug discovery. Among such approaches, target-aware methods that …

Wonho Zhung, Joongwon Lee, Woo Youn Kim*

DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties

Optimizing molecules to improve their properties is a fundamental challenge in drug design. For a fine-tuning of molecular properties …

Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung, Jaechang Lim, Woo Youn Kim*

3D Molecular Generative Framework for Interaction-guided Drug Design

Deep generative modeling has a strong potential to accelerate drug design. However, existing generative models often face challenges in …

Wonho Zhung, Hyeongwoo Kim, Woo Youn Kim*

Toward Generalizable Structure-based Deep Learning Models for Protein–ligand Interaction Prediction: Challenges and Strategies

Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental challenge of drug discovery. Deep learning …

Wonho Zhung, Seokhyun Moon, Woo Youn Kim*