The results demonstrate that GGAP-CPI outperformed all variants, affirming the synergy and effectiveness of its comprehensive design. Notably, the variants without the molecular encoder and the ESM-2 embeddings -GGAP-CPI -w/o Mol. Encod. and GGAP-CPI -w/o ESM-2 Emb. -showed the worst performance across all datasets, underscoring the critical contributions of the pretrained ligand encoder and protein embeddings to model accuracy. Although the other architectural components, such as protein graph convolution and multi-head cross attention pooling, also enhanced performance, their impact was comparatively moderate. These findings from the ablation study suggest that each component of GGAP-CPI is essential and effectively contributes to its performance, with no redundancy in the model's architecture. The integration of these modules results in optimal performance, thereby validating the model's design.