Finally, the results from employing Stochastic Negative Addition also highlighted unexpected outcomes. While the AUROC scores confirm SNA's utility, detailed examination of the F1score indicated no real improvement in model performance. Additionally, protein feature importances remained static post-SNA, indicating that the models failed to extract enhanced interpretability from the protein embeddings despite data augmentation. This emphasizes the necessity of employing multiple metrics to comprehensively evaluate model performance, thereby avoiding false optimism based on singular metrics. Balancing the dataset before model construction is also crucial, as evidenced by its beneficial impact on ligand-based QSAR and c-RASAR models, preventing model overfitting and promoting more reliable predictions .