Table 3.
Models | Type | Q (%) | SE (%) | SP (%) | AUC (%) |
---|---|---|---|---|---|
Ensemble SVM | machine learning | 67.5 | 60.9 | 76.5 | 81.8 |
Ensemble RF | machine learning | 65.0 | 56.5 | 76.5 | 80.1 |
Ensemble XGBoost | machine learning | 70.0 | 65.2 | 76.5 | 80.3 |
admetSAR | machine learning | 50.0 | 34.8 | 70.6 | 49.6 |
PreADMET | machine learning | 62.5 | 52.2 | 76.5 | —a |
VEGA CAESAR | machine learning | 70.0 | 65.2 | 76.5 | —a |
VEGA ISS | rule based | 70.0 | 73.9 | 64.7 | —a |
VEGA IRFMN/Antares | rule based | 70.0 | 78.3 | 58.8 | —a |
VEGA IRFMN/ISSCAN-CGX | rule based | 75.0 | 82.6 | 64.7 | —a |
Toxtree | rule based | 70.0 | 78.3 | 58.6 | —a |
lazar | similarity search | 75.0 | 87.0 | 58.8 | —a |
aThe AUC cannot be calculated for this software because there are no probability values in its results.