Fig. 2. Performance comparison of drug target prediction methods. The assay-AUC values for various target prediction algorithms based on ECFP6 features, graphs and sequences are displayed as boxplot. Each compared method yields 1310 AUC values for each modelled assay. On average, deep feed-forward neural networks (FNN) perform best followed by support vector machines (SVM), sequence-based networks (SmilesLSTM), GC graph convolution networks (GC), random forests (RF), Weave graph convolution networks (Weave), k-nearest neighbour (KNN), naive bayes (NB) and SEA.