Accuracy of models generated with various single and paired molecular representations using support vector machine (SVM) during cross-validation (purple heatmap) and testing (blue heatmap)

The top performing standalone fingerprints for each of the 5 ML algorithms

The best and worst performing models using a merged fingerprint for all 5 ML algorithms

Accuracy (%) of models trained with an imbalanced training dataset where the number of BRAF actives is decreased but the number of BRAF inactives is maintained at a fixed number (3600)

Accuracy (%) of models trained with a balanced training dataset where the numbers of BRAF actives and BRAF inactives are both similarly decreased

Recall and precision (%) of models trained with an imbalanced training dataset where the number of BRAF actives is decreased but the number of BRAF inactives is maintained at a fixed number (3600)

Recall and precision (%) of models trained with a balanced training dataset where the numbers of BRAF actives and BRAF inactives are both similarly decreased

Accuracy of models generated with various single and paired molecular representations using random forest (RF) during cross-validation (purple heatmap) and testing (blue heatmap)

Accuracy of models generated with various single and paired molecular representations using naïve bayes (NBayes) during cross-validation (purple heatmap) and testing (blue heatmap)

Accuracy of models generated with various single and paired molecular representations using k-nearest neighbour (kNN) during cross-validation (purple heatmap) and testing (blue heatmap)

Accuracy of models generated with various single and paired molecular representations using gradient-boosting decision tree (GBDT) during cross-validation (purple heatmap) and testing (blue heatmap)

Average accuracy for the ‘spiked-in’ “less active”-trained models based on testing with 10 balanced BRAF actives and inactives hold-out test sets

Average accuracy for the ‘spiked-in’ decoy-trained models based on testing with 10 balanced BRAF actives and inactives hold-out test sets

Accuracy for the ‘spiked-in’ decoy-trained models based on testing with a balanced BRAF actives and decoys hold-out test set