70 citations · 74 across the 3 of their papers we have counts for
3 papers
physics.chem-ph2022★ 3 cited
MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties
Yeji Kim, Yoonho Jeong, Jihoo Kim +3
The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs. Most GNN models collect and update ato…
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…