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Found 3 papers, 2 papers with code
MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties
The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs.
InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation
Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design.
Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph.
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