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Table 3 RMSE performance of each model on the four tasks

From: A multitask GNN-based interpretable model for discovery of selective JAK inhibitors

  Methods Training set Validation set Test set
Global MTATFP 0.23 0.51 0.52
STATFP 0.29 0.56 0.57
LightGBM_MD 0.30 0.62 0.61
LightGBM_ECFP4 0.33 0.60 0.60
JAK1 MTATFP 0.27 0.52 0.53
STATFP 0.27 0.51 0.55
LightGBM_MD 0.34 0.61 0.60
LightGBM_ECFP4 0.35 0.57 0.60
JAK2 MTATFP 0.26 0.51 0.54
STATFP 0.28 0.55 0.56
LightGBM_MD 0.31 0.66 0.62
LightGBM_ECFP4 0.36 0.63 0.64
JAK3 MTATFP 0.21 0.58 0.58
STATFP 0.36 0.70 0.66
LightGBM_MD 0.33 0.67 0.65
LightGBM_ECFP4 0.38 0.66 0.62
TYK2 MTATFP 0.20 0.44 0.42
STATFP 0.26 0.49 0.52
LightGBM_MD 0.21 0.52 0.55
LightGBM_ECFP4 0.25 0.52 0.52
  1. The lower the RMSE value was, the better the model performed