12 citations · 18 across the 3 of their papers we have counts for
3 papers
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter +3
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…
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak +3
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of…
Context-enriched molecule representations improve few-shot drug discovery
Johannes Schimunek, Philipp Seidl, Lukas Friedrich +4
A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically o…