89 citations · 89 across the 5 of their papers we have counts for
6 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…
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning
Kaushalya Madhawa, Tsuyoshi Murata
The objective of active learning (AL) is to train classification models with less number of labeled instances by selecting only the most informative instances for labeling. The AL…
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago +1
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adja…
Exploring Partially Observed Networks with Nonparametric Bandits
Kaushalya Madhawa, Tsuyoshi Murata
Real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topo…