  Binding affinity prediction of three-dimensional (3D) protein ligand
complexes is critical for drug repositioning and virtual drug screening.
Existing approaches transform a 3D protein-ligand complex to a two-dimensional
(2D) graph, and then use graph neural networks (GNNs) to predict its binding
affinity. However, the node and edge features of the 2D graph are extracted
based on invariant local coordinate systems of the 3D complex. As a result, the
method can not fully learn the global information of the complex, such as, the
physical symmetry and the topological information of bonds. To address these
issues, we propose a novel Equivariant Line Graph Network (ELGN) for affinity
prediction of 3D protein ligand complexes. The proposed ELGN firstly adds a
super node to the 3D complex, and then builds a line graph based on the 3D
complex. After that, ELGN uses a new E(3)-equivariant network layer to pass the
messages between nodes and edges based on the global coordinate system of the
3D complex. Experimental results on two real datasets demonstrate the
effectiveness of ELGN over several state-of-the-art baselines.
