This provides additional evidence that it is the ligand information that is pivotal for bioactivity prediction using PCM methods. The likely reason for this is the greater diversity found in ligand embeddings compared to protein embeddings, enabling models to extract more useful patterns from the distribution of ligand data. This observation aligns with the long-standing tradition of using ligand-based QSAR models, highlighting the importance of ligand embeddings in the development of proteochemometric models. Consistent with other studies , this also underscores the significance of ligand features and reveals how dataset bias and potential overtraining predominantly influence model performance, rather than focusing solely on protein or protein-ligand interactions. Therefore, we encourage researchers developing PCM models and deep learningbased scoring functions to implement ablations, permutation tests, and thorough statistical analyses to validate their findings.