5 papers
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…
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…
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…
Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models
Teppei Suzuki, Keisuke Ozawa
We propose an efficient evaluation protocol for large vision-language models (VLMs). Given their broad knowledge and reasoning capabilities, multiple benchmarks are needed for comp…
Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection
Keisuke Ozawa, Tomoya Itakura, Taisuke Ono
Smoothing is widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjust…