From the 1 of 7 linked papers with an AI index.
7 papers
TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology
Hyunjin Seo, Hyeon Hwang, Gyubok Lee +7
The paper introduces TheBioCollection, a 52.6‑billion‑token unified corpus that aggregates diverse biological resources for pre‑training large language models, and shows that train…
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…
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…
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…
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…