Wonho Zhung

Wonho Zhung

Ph.D. Student, KAIST

Intelligent Chemistry Lab

Biography

I am a Ph.D. student at the Intelligent Chemistry Lab at KAIST (PI: Prof. Woo Youn Kim). My goal is to devise deep learning strategies for finding hit compounds to lessen the time and cost consumed in early-stage drug discovery. I’m currently working on protein-ligand binding affinity prediction and generative modeling for structure-based ligand design. Other approaches regarding the biochemical application of deep learning are highly intrigued.

Besides research, I love playing the violin. As a violinist in the KAIST orchestra, I enjoy performing classical music in ensembles.

Interests
  • DL for chemical & biological application
  • Structure-based Drug Design (SBDD)
  • 3D Graph Neural Networks (3D GNNs)
  • 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

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*
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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*
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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*
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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*
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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*
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Photoimmunotherapy Retains Its Anti-Tumor Efficacy with Increasing Stromal Content in Heterotypic Pancreatic Cancer Spheroids
Mohammad A. Saad, Wonho Zhung, Margaret Elizabeth Stanley, Sydney Formica, Stacey Grimaldo-Garcia, Girgis Obaid, Tayyaba Hasan*
PDF DOI
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were highlighted for their high accuracy with affordable …
Wonho Zhung, Seokhyun Moon, Soojung Yang, Jaechang Lim, Woo Youn Kim*
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Recent Posts

Tutorial: Protein-Ligand Complex MD Simulation with OpenMM
OpenMM toolkit을 이용한 protein-ligand complex MD simulation 맛보기
Last updated on Jun 17, 2022 3 min read
Tutorial: Tethered Docking with RxDock
RxDock의 scaffold-based tethered docking 기능 맛보기
Last updated on Jun 30, 2022 5 min read
NVAE: A Deep Hierarchical Variational Autoencoder
This post is based on a paper “NVAE: A Deep Hierarchical Variational Autoencoder” by Arash Vahdat and Jan Kautz at NeurIPS 2020.
Last updated on Sep 29, 2021 5 min read
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Popular Topics

3D Molecular generative model 3D Representation AI602 Autoregressive Bayesian Bioisostere Deep learning Deep neural network Diffusion model Docking Drug discovery Drug induced liver injury Drug-Target Interaction Fine-tuning Foundation model Graph neural network Paper Summary Protein-ligand interaction SBDD Structure-based drug design

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