7 papers
Learning Adaptive Perturbation-Conditioned Contexts for Robust Transcriptional Response Prediction
Yinhua Piao, Hyomin Kim, Seonghwan Kim +7
Predicting high-dimensional transcriptional responses to genetic perturbations is challenging because signals are sparse and experimental noise is severe. Existing methods often su…
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…
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling
Kiyoung Seong, Sungsoo Ahn, Sehui Han +1
Crystal modeling spans a family of conditional and unconditional generation tasks, including crystal structure prediction (CSP) and de novo generation (DNG). While recent deep gene…
DNACHUNKER: Learnable Tokenization for DNA Language Models
Taewon Kim, Jihwan Shin, Hyomin Kim +5
DNA language models are increasingly used to represent genomic sequence, yet their effectiveness depends critically on how raw nucleotides are converted into model inputs. Unlike n…
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…