Protein–ligand interactions are fundamental to critical biological processes such as transcription, translation, and drug–target interaction. The precise identification of protein–ligand binding residues is essential for accurately modeling these interactions and facilitating subsequent research. However, the development of computational methods that are both efficient and accurate for this purpose remains a significant challenge. Progress in this field holds the potential to drive substantial advancements in biotechnology and drug discovery. In response to this need, we have recently developed a method known as Contrastive Learning And Pretrained Encoder (CLAPE). This approach integrates a pretrained protein language model with contrastive learning to predict ligand-binding residues. In this chapter, we provided a detailed overview of the CLAPE framework, including instructions for using the associated Python package and command-line tools. Additionally, we outline the methods employed for result visualization and describe the comprehensive processes involved in model training.