3 papers
cs.LG2026
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…
cs.AI2026
MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring
Yiming Zhang, Hikaru Shindo, Shuan Chen +5
Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unregistered molecules disconnec…
cs.LG2026
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa +6
Therapeutic peptides are a promising drug modality, but their generation must satisfy multiple therapeutic constraints. We introduce BindSafe-PepBench, a fixed-budget benchmark tha…