SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction

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 graph neural network-multilayer perceptron (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 0.6 M 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.