Hyosoon Jang
Hyosoon Jang

Hyosoon Jang

Ph.D. Student · Graduate School of AI, KAIST

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About

I'm Hyosoon Jang, a second-year Ph.D. student advised by Sungsoo Ahn at KAIST. My goal is to build an in-house state-of-the-art drug design engine (DDE) that anyone can access and use. As cutting-edge models in drug discovery are increasingly kept proprietary or released under restrictive licenses, the gap between industry and open research keeps widening, and I want to help close it. My work spans five stages: target discovery, structure prediction, binding affinity, design and optimization, and ADMET.

  1. Target
    Discovery
    Exploring
  2. Structure
    Prediction
    Structure prediction success rates. SoupFold improves every base co-folding model on FoldBench and leads on GPCR and molecular glue complexes.

    Structure prediction success rates. SoupFold improves every base co-folding model on FoldBench and leads on GPCR and molecular glue complexes.

    Go to paper (P2)
  3. Binding
    Affinity
    Planned
  4. Design &
    Optimization
    Active
  5. ADME &
    Toxicity
    Win, draw and loss rates of co-folding model representations against five molecular representation baselines, across absorption, distribution, metabolism, excretion and toxicity tasks.

    Win, draw and loss rates of co-folding model representations against five molecular representation baselines, across absorption, distribution, metabolism, excretion and toxicity tasks.

    Go to paper (C5)

SOTA as of Oct 9, 2026

Publications

‡ equal contribution

Co-folding with a Soup of Representations
P2 NeurIPS 2026 AI4DD Workshop

Co-folding with a Soup of Representations

Hyosoon Jang, Taewon Kim, Sungsoo Ahn

Paper arXiv Code
Atom-level Protein Representation Learning Improves Protein Structure Prediction
C6 NeurIPS 2026

Atom-level Protein Representation Learning Improves Protein Structure Prediction

Hyosoon Jang‡, Taewon Kim‡, Hyunjin Seo, Seonghwan Seo, Hyeongwoo Kim, Wonho Zhung, Mingyeong Shin, Wooyoun Kim, Sungsoo Ahn

Order of the equal contributors was determined randomly.

Paper arXiv Code Project Models
A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning
C5 NeurIPS 2026

A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning

Hyosoon Jang, Hyunjin Seo, Honghui Kim, Seonghyun Park, Taewon Kim, Yunhui Jang, Sungsoo Ahn

Paper arXiv Code
Self-Training Large Language Models with Confident Reasoning
C4 EMNLP 2025

Self-Training Large Language Models with Confident Reasoning

Hyosoon Jang, Yunhui Jang, Sungjae Lee, Jungseul Ok, Sungsoo Ahn

Paper PDF Code
Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity
P1 arXiv 2025

Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity

Hyosoon Jang, Yunhui Jang, Jaehyung Kim, Sungsoo Ahn

Paper arXiv
Pessimistic Backward Policy for GFlowNets
C3 NeurIPS 2024

Pessimistic Backward Policy for GFlowNets

Hyosoon Jang, Yunhui Jang, Minsu Kim, Jinkyoo Park, Sungsoo Ahn

Paper Code
Learning Energy Decomposition for Partial Inference in GFlowNets
C2 ICLR 2024 · Oral, 85/7262 = 1.16%

Learning Energy Decomposition for Partial Inference in GFlowNets

Hyosoon Jang, Minsu Kim, Sungsoo Ahn

Paper Code
Diffusion Probabilistic Models for Structured Node Classification
C1 NeurIPS 2023

Diffusion Probabilistic Models for Structured Node Classification

Hyosoon Jang, Seonghyun Park, Sangwoo Mo, Sungsoo Ahn

Paper Code
De novo Drug Design through Gradient-based Regularized Search in Information-theoretically Controlled Latent Space
J2 J. Comput. Aided Mol. Des. 2024

De novo Drug Design through Gradient-based Regularized Search in Information-theoretically Controlled Latent Space

Hyosoon Jang, Sangmin Seo, Sanghyun Park, Byung Ju Kim, Geon-Woo Choi, Jonghwan Choi, Chihyun Park

Paper
Machine Learning Algorithms Using Systemic Inflammatory Markers for Predicting the Oncological Outcomes of Colorectal Cancer after Surgery
J1 Ann. Surg. Oncol. 2023

Machine Learning Algorithms Using Systemic Inflammatory Markers for Predicting the Oncological Outcomes of Colorectal Cancer after Surgery

Songsoo Yang‡, Hyosoon Jang‡, Inkyu Park‡, Sunhye Lee, Gaeul Oh, Chihyun Park, Jeonghyun Kang

Paper