VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification

arXiv:2404.07194v1 Announce Type: new
Abstract: Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can now profit from the availability of large amounts of 3D structures in protein structure databases or from AlphaFold predictions. Current binding site identification methods heavily rely on graph neural networks (GNNs), usually designed to output E(3)-equivariant predictions. Such methods turned out to be very beneficial for physics-related …

abstract arxiv availability cs.ai cs.lg drugs graph graph neural networks identification identify networks neural networks nodes profit protein proteins protein structure q-bio.bm type virtual