We explored various generation and augmentation techniques for protein representations to assess their efficacy in providing informative input for ML/DL models that predict kinaseligand pair bioactivity. This included rigorous permutation testing of both protein and ligand embeddings to parse out their respective contributions to model accuracy. Additionally, we explored how class imbalances, data splitting methodologies, and the dimensionality of embeddings influence the robustness and accuracy of these models. Our goal is to develop a comprehensive framework for evaluating PCMs that not only exposes and addresses inherent biases but also improves the generalizability and interpretability of these models across diverse datasets.