8 papers · 1 filter
Blending experimental electron density information to protein-ligand interaction
Seonghyun Park, Shuan Chen, Jun Jin Choong +6
Machine learning models for protein-ligand interactions commonly rely on atomic coordinates, which are constructed by fitting molecular models to experimental electron-density maps…
scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning
Yunhak Oh, Yoonho Lee, Junseok Lee +9
Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what constitutes signal or nuisanc…
Co-folding with a Soup of Representations
Hyosoon Jang, Taewon Kim, Sungsoo Ahn
Co-folding models such as AlphaFold3, Protenix, ESMFold2, and OpenDDE have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. In th…
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…
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…
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…
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…