Our results suggest that current limitations in SOM prediction stem more from aleatoric uncertainty and model bias rather than approximation uncertainty. This implies that simply increasing the size of training data may offer limited benefits. Instead, future efforts should focus on improving data quality and reducing model uncertainty by developing better descriptors of metabolic processes. Reaching this ambitious goal will require substantial effort.