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

@article{Zhang2022SSGNNAS,
  title={SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction},
  author={Shuke Zhang and Yan Jin and Tianmeng Liu and Qi Wang and Zhaohui Zhang and Shuliang Zhao and Bo Shan},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.07015}
}
Efficient and effective drug-target binding affinity (DTBA) prediction is a challeng-ing 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… 
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