Our experimental analysis demonstrates that lower total uncertainty correlates with better predictive performance. High epistemic uncertainty signals underrepresented or insufficient training data, while high aleatoric uncertainty suggests noisy or inconsistent labeling in the data. Case studies on durlobactam, PCB 30, and etrasimod exemplify how the total uncertainty can identify unreliable predictions, while the aleatoric and epistemic components can detect issues related to inconsistent data labeling and data gaps, respectively. Furthermore, we show how uncertainty scores can aid in elucidating metabolic pathways for novel compounds by prioritizing certain atoms for further investigation.