  Efficient and effective drug-target binding affinity (DTBA) prediction is a
challenging task due to the limited computational resources in practical
applications and is a crucial basis for drug screening. Inspired by the good
representation ability of graph neural networks (GNNs), we propose a
simple-structured GNN model named SS-GNN to accurately predict DTBA. By
constructing a single undirected graph based on a distance threshold to
represent protein-ligand interactions, the scale of the graph data is greatly
reduced. Moreover, ignoring covalent bonds in the protein further reduces the
computational cost of the model. The GNN-MLP module takes the latent feature
extraction of atoms and edges in the graph as two mutually independent
processes. We also develop an edge-based atom-pair feature aggregation method
to represent complex interactions and a graph pooling-based method to predict
the binding affinity of the complex. We achieve state-of-the-art prediction
performance using a simple model (with only 0.6M parameters) without
introducing complicated geometric feature descriptions. SS-GNN achieves
Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming
state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model
structure and concise data processing procedure improve the prediction
efficiency of the model. For a typical protein-ligand complex, affinity
prediction takes only 0.2 ms. All codes are freely accessible at
https://github.com/xianyuco/SS-GNN.
