Due to the variability of UQ methods across datasets, Autoparty includes other uncertainty estimation architectures as options for the user. Specifically, Autoparty allows for dropout-based uncertainty in which a single model repeatedly predicts a label for a single example with random dropout. Similar to ensemble methods, the final prediction is the average of multiple predictions, and the uncertainty is their variance. Autoparty also includes the option of distance-based uncertainty, where the distance to the closest neighbors in the training set is a proxy for uncertainty. Unlike typical classification labels, grades are ordinal, meaning that the output classes have an ordered relationship; A is closer to B than F. For ideal training, it is necessary to include this property in the formulation of output classes and the model's loss function. Some approaches treat ordinal labels as a regression task and bin the predictions into different classes, though this requires additional tuning of these cutoff values. We used a cumulative one-encoding strategy with labels as vectors of 1s followed by 0s. The count of 1s indicated the value of the grade (Additional detail provided in Supporting Methods). We trained models with binary cross-entropy loss. On a small dataset of MM/GBSA scores of complexes from a virtual screen against the Dopamine D4 receptor, models using cumulative one-encoding achieved a higher proportion of "close" mispredictions, or those that were a single grade away from the true value (Supporting Fig. ). Autoparty also supports nominal class labels in which case the output classes are conventionally one-hot encoded.