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PLAPF-NGGNN: Protein-ligand affinity prediction framework based on neighbour statistical sage and GlobalGAT graph neural network

computational-biology
artificial-intelligence
computer-vision
data-mining-and-machine-learning
neural-networks

Abstract

Protein–Ligand Affinity (PLA) prediction plays a pivotal role in drug screening and optimization. However, existing deep learning approaches based on Graph Neural Networks (GNNs) still suffer from limited expressive power and difficulties in modeling long-range dependencies, which restrict their performance in practical applications. To address these challenges, we propose PLAPF-NGGNN, a novel GNN framework for PLA prediction that effectively integrates both local and global structural features. The framework consists of two key modules: Neighbour Statistical SAGEConv (NSSAGE), which enhances local feature representation by incorporating statistical information of neighborhood differences, and the Global Graph Attention Network (GGAT), which employs globally biased graph attention convolution to balance local node features and global significance, thereby strengthening global awareness. Extensive experiments demonstrate that PLAPF-NGGNN significantly outperforms existing baselines, reducing the prediction RMSE on three independent benchmark datasets by 14.03%, 16.96%, and 13.7%, respectively.
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