1 citations · 1 across the 10 of their papers we have counts for
10 papers
TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology
Hyunjin Seo, Hyeon Hwang, Gyubok Lee +7
The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology. However, existing b…
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…
VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design
Hyunjin Seo, Hongjoon Ahn, Jimin Park +16
Protein design aims to compose amino-acid sequences that fold into stable three-dimensional structures while satisfying targeted functional properties. The field is increasingly sh…
A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning
Hyosoon Jang, Hyunjin Seo, Honghui Kim +4
Small-molecule foundation models are typically pretrained on standalone molecular data, unlike vision and language models that often benefit from cross-modal or relational supervis…
Learning Flexible Forward Trajectories for Masked Molecular Diffusion
Hyunjin Seo, Taewon Kim, Sihyun Yu +1
Masked diffusion models (MDMs) have achieved notable progress in modeling discrete data, while their potential in molecular generation remains underexplored. In this work, we explo…
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
Hyunjin Seo, Kyusung Seo, Joonhyung Park +1
Recent advancements in graph neural networks (GNNs) have highlighted the critical need of calibrating model predictions, with neighborhood prediction similarity recognized as a piv…
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
Taewon Kim, Hyunjin Seo, Sungsoo Ahn +1
Predicting the ground-state 3D molecular conformations from 2D molecular graphs is critical in computational chemistry due to its profound impact on molecular properties. Deep lear…
Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models
Hyunjin Seo, Taewon Kim, June Yong Yang +1
Recent advancements in text-attributed graphs (TAGs) have significantly improved the quality of node features by using the textual modeling capabilities of language models. Despite…
TEDDY: Trimming Edges with Degree-based Discrimination strategY
Hyunjin Seo, Jihun Yun, Eunho Yang
Since the pioneering work on the lottery ticket hypothesis for graph neural networks (GNNs) was proposed in Chen et al. (2021), the study on finding graph lottery tickets (GLT) has…
PC-Adapter: Topology-Aware Adapter for Efficient Domain Adaption on Point Clouds with Rectified Pseudo-label
Joonhyung Park, Hyunjin Seo, Eunho Yang
Understanding point clouds captured from the real-world is challenging due to shifts in data distribution caused by varying object scales, sensor angles, and self-occlusion. Prior…