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Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network

arXiv 2210.16098 ↗First posted 27 Oct 2022q-bio.BM cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 48 references that are in the map. First posted on arXiv 27 Oct 2022.

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References · 29

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17 more
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