CPI-MIF: Compound-Protein Interaction Prediction with Multiview Information Fusion

Compound-protein interaction (CPI) prediction is a critical step in the drug discovery process. Deep learning approaches have played a significant role in CPI prediction in recent years. However, existing studies often overlook the role of proteins in CPI recognition and fail to incorporate the complex interaction information between substructures. To this end, we propose a multiview information fusion model named CPI-MIF, which mines the structural information on compounds and biological information on proteins, and uses the multiview interaction module to aggregate compound and protein information from both the micro and macro views. In the micro view, CPI-MIF focuses on the mechanism of interaction between compound atoms and protein amino acids, while in the macro view, it explores the relationship between compound sequences and protein sequences, enabling the aggregation of multilevel feature information and relationship prediction. We conducted CPI prediction experiments on three real-world data sets and demonstrated that CPI-MIF outperforms existing CPI prediction methods in accuracy, AUC, and AUPR, while exhibiting strong stability on imbalanced data sets.