21 citations · 27 across the 8 of their papers we have counts for
8 papers
Atom-level Protein Representation Learning Improves Protein Structure Prediction
Taewon Kim, Hyosoon Jang, Hyunjin Seo +6
Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protei…
On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists
Seungone Kim, Dongkeun Yoon, Kiril Gashteovski +55
With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remain in question: many scientis…
Progressive Multi-Agent Reasoning for Biological Perturbation Prediction
Hyomin Kim, Sang-Yeon Hwang, Jaechang Lim +6
Predicting gene regulation responses to biological perturbations requires reasoning about underlying biological causalities. While large language models (LLMs) show promise for suc…
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
Tony Shen, Seonghwan Seo, Ross Irwin +4
Many generative applications, such as synthesis-based 3D molecular design, involve constructing compositional objects with continuous features. Here, we introduce Compositional Gen…
Generative Flows on Synthetic Pathway for Drug Design
Seonghwan Seo, Minsu Kim, Tony Shen +4
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synt…
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung +3
Optimizing molecular properties while preserving biological activity is a central challenge in drug design. Bioisosteric replacement, which substitutes a molecular fragment with a…
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
Tony Shen, Seonghwan Seo, Grayson Lee +5
Searching the vast chemical space for drug-like molecules that bind with a protein pocket is a challenging task in drug discovery. Recently, structure-based generative models have…
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
Seonghwan Seo, Woo Youn Kim
As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging. Different pre-screening…