Protein-Protein Interaction Prediction Models Based on Graph Neural Networks

Protein-protein interactions (PPIs) are the foundation for numerous biological processes within cells, which are crucial for understanding cellular signaling networks, disease mechanisms, and drug development. Recently, numerous artificial intelligence (AI)-based approaches have emerged for predicting PPIs. Nevertheless, existing AI-based approaches either partially or loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for prediction. To address this issue, this paper proposes an end-to-end graph neural network model for protein-protein interaction prediction, termed the Knowledge Graph Fused Graph Neural Network (KGF-GNN). First, protein associated network (PAN) is constructed by comprehensively exploiting protein-associated relationships and mechanisms among drugs, diseases, ribonucleic acid, protein structures, etc. Then, a graph neural network (GNN) is built to extract both the topological and semantic features from PAN. Secondly, the observed interactions between proteins are constructed into a PPI network, and another GNN is built to extract the hidden topological features within the PPI network. Third, a multi-layer perceptron is designed to fuse the extracted various features by end-to-end learning. With such designs, the feature extractions and fusions of PPIs are guaranteed to be comprehensive and optimal for prediction. Finally, by conducting extensive experiments on real PPI datasets, we demonstrate that our KGF-GNN can accurately predict PPIs and significantly outperform state-of-the-art models.