To aggregate these results, we employed a voting procedure across the ensemble's predictions. Using the strictest threshold, where all 10 predictions had to agree for a label to be assigned by the model, we still retained predictions for 94% of the dataset while boosting the model's accuracy to above 91%. Both the transient augmentation and subsequent prediction on these SMILES adds to the computational time. The confidence metric suggested here is straightforward to implement and provides a robust indication of prediction reliability in addition to the interpretability framework.