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Table 2 Summary of ENB and EPA performance on external datasets

From: Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development

Dataset Method Regression metrics Classification metrics
PCC MAE MSE AUC F1 score Precision Recall
Metz ENB 0.47 0.51 0.49 0.81 0.45 0.39 0.54
EPA 0.54 0.44 0.39 0.84 0.49 0.41 0.61
Davis ENB 0.38 1.37 0.90 0.67 0.58 0.77 0.46
EPA 0.42 1.10 0.79 0.69 0.64 0.76 0.55
PKIS1 ENB 0.29 0.64 0.59 0.75 0.25 0.21 0.30
EPA 0.33 0.56 0.44 0.79 0.26 0.22 0.32
KinaseSafari ENB 0.33 1.1 2.14 0.68 0.56 0.74 0.45
EPA 0.44 1.01 1.72 0.73 0.61 0.77 0.51
MRC ENB 0.38 0.83 1.14 0.68 0.33 0.53 0.24
EPA 0.45 0.78 0.99 0.74 0.38 0.54 0.30
  1. The bold number denotes the better result between ENB and EPA for predicting the corresponding external dataset