|
Test set
|
Descriptors
|
ROC AUC
|
Average precision
|
Sensitivity
|
Specificity
|
CCR
|
|---|
|
Random
|
Molecular
|
0.92
|
0.83
|
0.92
|
0.78
|
0.85
|
|
Protein target
|
0.85
|
0.71
|
0.81
|
0.73
|
0.77
|
|
Tox21 assay
|
0.60
|
0.40
|
0.47
|
0.67
|
0.57
|
|
Molecular and protein target
|
0.91
|
0.82
|
0.85
|
0.79
|
0.82
|
|
Rare scaffolds
|
Molecular
|
0.80
|
0.68
|
0.64
|
0.81
|
0.72
|
|
Protein target
|
0.70
|
0.51
|
0.70
|
0.59
|
0.65
|
|
Tox21 assay
|
0.57
|
0.36
|
0.67
|
0.43
|
0.55
|
|
Molecular and protein target
|
0.80
|
0.68
|
0.83
|
0.63
|
0.73
|
|
Single source
|
Molecular
|
0.83
|
0.65
|
0.70
|
0.81
|
0.75
|
|
Protein target
|
0.79
|
0.63
|
0.76
|
0.67
|
0.72
|
|
Tox21 assay
|
0.61
|
0.39
|
0.43
|
0.73
|
0.58
|
|
Molecular and protein target
|
0.85
|
0.69
|
0.77
|
0.76
|
0.76
|
- Generally, the best performing models were those trained using either molecular descriptors alone or in combination with protein target descriptors. Classifiers found the random test set less challenging to predict than the two more challenging test sets
