Search Results for author:
Found 2 papers, 2 papers with code
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
However, the performance of GNNs at binding site identification is still limited potentially due to the lack of dedicated nodes that model hidden geometric entities, such as binding pockets.
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
