Compound B illustrates how uncertainty scores can help identify unreliable predictions. AWESOM correctly predicts three out of the five experimentally reported SOMs, but misses two and produces two false-positive predictions. The high aleatoric uncertainty scores for the missed SOMs are due to the variability of the SOM labels in structurally related compounds in the training data, which contains 21 PCBs. Twelve of these PCBs contain a trichlorophenyl moiety with the potential for oxidation at the meta position. However, such a reaction is recorded for only eight of these 12 PCBs. Furthermore, seven of the 21 PCBs in the training data feature a non-chlorinated phenyl group (similar to PCB 30) but display diverse oxidation patterns. These observations illustrate the complexity of determining and understanding metabolism. The recorded oxidation patterns reflect the molecules' intrinsic reactivity and the limitations of experiments, data collection, interpretation, and reporting. The exact SOMs may not always be deductible from measured data, particularly for oxidation reactions on aromatic rings. In such cases, experts may adopt different annotation strategies: some mark all possible positions, others select based on their best judgment, while others omit SOMs entirely when the exact location is unclear. This example emphasizes the critical importance of consistent and thorough data curation in advancing the field of metabolism prediction.