A train-test run. For both the MACCS FP and fixed-length random walk representations, a "train-test run" of an SVM comprises the following procedure. First, we randomly shuffle then split the examples into a 80%/20% train/test split. We stratify the split to preserve the distribution of class labels in the two splits. Second, using only the training split, we use stratified K = 3-fold cross-validation to determine the optimal hyperparameter(s). For the MACCS fingerprint, the hyperparameter is the C parameter of the SVM. For the fixed-length random walk representation, the hyperparameters are both C and L, the length of the random walks. Via grid search, we choose the optimal hyperparameter(s) as the one(s) providing the K SVMs (each trained on K -1 folds) with the maximal mean F1 score on the validation sets (one fold each). The hyperparameter grid comprises (i) log 10 C ∈ {-6, ..., 1} and (ii) L ∈ {0, ..., 12}. Finally, we train a deployment SVM with the optimal hyperparameter(s) on all training data and evaluate its performance (precision, recall, accuracy, and F1 score) on the hold-out test set.