@inproceedings{
sestak2023vnegnn,
title={{VN}-{EGNN}: Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification},
author={Florian Sestak and Lisa Schneckenreiter and Sepp Hochreiter and Andreas Mayr and G{\"u}nter Klambauer},
booktitle={NeurIPS 2023 Workshop: New Frontiers in Graph Learning},
year={2023},
url={https://openreview.net/forum?id=TsjGaj45Li}
}
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 iden-
tification 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 rely on geometric deep learning, which takes
geometric invariances and equivariances into account. Such methods turned out
to be very beneficial for physics-related tasks like binding energy or motion tra-
jectory prediction. However, their performance at binding site identification is
still limited, which might be due to limited expressivity or oversquashing effects
of E(n)-Equivariant Graph Neural Networks (EGNNs). Here, we extend EGNNs
by adding virtual nodes and applying an extended message passing scheme. The
virtual nodes in these graphs both improve the predictive performance and can also
learn to represent binding sites. In our experiments, we show that VN-EGNN sets
a new state of the art at binding site identification on three common benchmarks,
COACH420, HOLO4K, and PDBbind2020.
VN-EGNN: Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
Florian Sestak Lisa Schneckenreiter Sepp Hochreiter Andreas Mayr Günter Klambauer