activity
20242026
most citedCRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement

2 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

MolDeTox: Evaluating Language Model's Stepwise Fragment Editing for Molecular Detoxification

Jueon Park, Wonjune Jang, Jiwoo Lee +2

Large Language Models (LLMs) and Vision Language Models (VLMs) have recently shown promising capabilities in various scientific domain. In particular, these advances have opened ne…

q-bio.QM2026

ToxReason: A Benchmark for Mechanistic Chemical Toxicity Reasoning via Adverse Outcome Pathway

Jueon Park, Wonjune Jang, Chanhwi Kim +2

Recent advances in large language models (LLMs) have enabled molecular reasoning for property prediction. However, toxicity arises from complex biological mechanisms beyond chemica…

q-bio.QM2026

ATTNSOM: Learning Cross-Isoform Attention for Cytochrome P450 Site-of-Metabolism

Hajung Kim, Eunha Lee, Sohyun Chung +3

Identifying metabolic sites where cytochrome P450 enzymes metabolize small-molecule drugs is essential for drug discovery. Although existing computational approaches have been prop…

cs.CE2025

GraphCliff: Short-Long Range Gating for Modeling Critical Activity Changes Caused by Subtle Molecular Differences

Hajung Kim, Jueon Park, Junseok Choe +3

The quantitative structure-activity relationship assumes a smooth mapping between molecular structure and biological activity. However, activity cliffs, defined as pairs of structu…

cs.LG2025

CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and Prediction

Jueon Park, Yein Park, Minju Song +4

Drug toxicity remains a major challenge in pharmaceutical development. Recent machine learning models have improved in silico toxicity prediction, but their reliance on annotated d…

cs.LG2024★ 2 cited

CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement

Seungheun Baek, Soyon Park, Yan Ting Chok +4

Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in…

cs.LG2024

MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints

Mogan Gim, Jueon Park, Soyon Park +5

Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper…