Table . Uncertainty estimation with DeepCocrystal. External test set molecules were represented as 10 SMILES strings each before prediction (using DeepCocrystal). Two approaches were considered to estimate uncertainty, i.e., majority voting, which picks the most frequent class among the predictions (per molecular pair), and standard deviation computed on the individual model predictions per each pair. Different uncertainty thresholds on each approach were analyzed for their effect on the model performance, as well as on the number of molecular pairs predicted. The number and percentage of predicted pairs (i.e., predictions below the considered thresholds), Balanced Accuracy (BAcc), Recall, Specificity, and Precision are reported. DeepCocrystal on canonical SMILES (which is invariant to augmentation and cannot be used for uncertainty estimation) was used as a performance baseline. The best performing models per metric are highlighted in boldface. ).