70 citations · 71 across the 3 of their papers we have counts for
3 papers
cs.LG2021
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
Hyeoncheol Cho, Youngrock Oh, Eunjoo Jeon
Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth…
q-bio.BM2020★ 1 cited
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation
Hyeoncheol Cho, Eok Kyun Lee, Insung S. Choi
Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design. Modeling the pro…
cs.LG2018★ 70 cited
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation
Hyeoncheol Cho, Insung S. Choi
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph…