Zhang Yunuo, Wen Bozhu, Li Yaru, Liu Yunjiong, Zhang Peiliang, Jin Bo, Che Chao
Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian University, Dalian 116622, China.
Department of Library and Information Science, Yonsei University, Seoul 03722, Korea.
ACS Omega. 2025 Jul 13;10(28):30155-30166. doi: 10.1021/acsomega.5c00113. eCollection 2025 Jul 22.
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.
复合蛋白相互作用（CPI）预测是药物发现过程中的关键步骤。近年来，深度学习方法在CPI预测中发挥了重要作用。然而，现有研究往往忽视了蛋白质在CPI识别中的作用，未能纳入子结构之间复杂的相互作用信息。为此，我们提出了一种名为CPI-MIF的多视图信息融合模型，该模型挖掘化合物的结构信息和蛋白质的生物学信息，并使用多视图交互模块从微观和宏观两个视角聚合化合物和蛋白质信息。在微观视角下，CPI-MIF关注化合物原子与蛋白质氨基酸之间的相互作用机制，而在宏观视角下，它探索化合物序列与蛋白质序列之间的关系，从而实现多级特征信息的聚合和关系预测。我们在三个真实世界数据集上进行了CPI预测实验，结果表明，CPI-MIF在准确率、AUC和AUPR方面优于现有的CPI预测方法，同时在不平衡数据集上表现出很强的稳定性。
