In our study, we focused on the construction of embeddings and the strategic use of permutation testing to evaluate model robustness and performance. The literature offers a variety of protein and ligand representations for ML and DL-based scoring functions. While "Y-permutation" is widely documented, our research highlights the advantages of "Xpermutation," demonstrating the minimal im-pact of protein permutations on model outcomes as opposed to the significant disruptions caused by ligand and label permutations. Furthermore, Gorantla et al. assessed the impact of random permutations of ligand and protein representations on binding affinity predictions with neural network models using Davis and KIBA 29 datasets. Notably, their models trained with randomly permuted protein contact maps performed on par with those trained on conventional 1D or 2D embeddings across various metrics. Conversely, when ligand representations were permuted, a marked decrease in performance was observed, emphasizing the critical role of ligand information in predictive accuracy.