In recent years, publicly available CPI databases, such as ChEMBL, BindingDB, PubChem, DrugBank, and PDBbind , have been rapidly growing, thereby accelerating the development of various structure-free methods using machine learning (ML) algorithms. These ML methods consider compound information, protein information, and their interactions in a unified framework . More recently, the CPI prediction models using deep learning (DL) techniques, such as convolutional neural networks (CNNs), graph convolutional networks (GCNs) , and Transformer algorithms 16 , have shown substantially improved predictive performance and interpretability . These models can extract the feature representations of compounds and proteins during end-to-end learning of their interactions.