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TableĀ 5 The best classification models for some ADME/T related properties

From: ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

Property Method Features Fivefold cross validation External validation dataset
Sensitivity Specificity Accuracy AUC Sensitivity Specificity Accuracy AUC
HIA RF MACCS 0.820 0.743 0.782 0.846 0.801 0.743 0.773 0.831
F (20%) RF MACCS 0.731 0.647 0.689 0.759 0.680 0.663 0.671 0.746
F (30%) RF ECFP6 0.743 0.605 0.669 0.715 0.751 0.601 0.667 0.718
BBB SVM ECFP2 0.962 0.813 0.926 0.948 0.993 0.854 0.962 0.975
Pgp-inhibitor SVM ECFP4 0.887 0.789 0.848 0.908 0.863 0.802 0.838 0.913
Pgp-substrate SVM ECFP4 0.839 0.807 0.824 0.899 0.826 0.854 0.840 0.905
CYP1A2-inhibitor SVM ECFP4 0.833 0.864 0.849 0.928 0.853 0.880 0.867 0.939
CYP1A2-substrate RF ECFP4 0.768 0.636 0.702 0.801 0.768 0.637 0.702 0.802
CYP3A4-inhibitor SVM ECFP4 0.759 0.858 0.817 0.901 0.788 0.860 0.829 0.909
CYP3A4-substrate RF ECFP4 0.798 0.716 0.757 0.835 0.819 0.679 0.749 0.835
CYP2C19-inhibitor SVM ECFP2 0.826 0.819 0.822 0.893 0.812 0.825 0.819 0.899
CYP2C19-substrate RF ECFP2 0.735 0.744 0.740 0.816 0.871 0.667 0.769 0.853
CYP2C9-inhibitor SVM ECFP4 0.719 0.898 0.837 0.900 0.730 0.882 0.830 0.894
CYP2C9-substrate RF ECFP4 0.746 0.709 0.728 0.819 0.746 0.709 0.734 0.824
CYP2D6-inhibitor RF ECFP4 0.770 0.811 0.793 0.868 0.771 0.812 0.795 0.882
CYP2D6-substrate RF ECFP4 0.765 0.73 0.748 0.823 0.792 0.73 0.76 0.833
hERG RF 2D 0.908 0.700 0.844 0.879 0.888 0.762 0.848 0.873
H-HT RF 2D 0.780 0.520 0.689 0.710 0.785 0.487 0.681 0.683
Ames RF MACCS 0.800 0.841 0.820 0.890 0.848 0.816 0.834 0.897
SkinSen RF MACCS 0.685 0.727 0.706 0.760 0.715 0.727 0.731 0.774
DILI RF MACCS 0.866 0.813 0.840 0.904 0.830 0.857 0.843 0.910
FDAMDD RF ECFP4 0.848 0.812 0.832 0.904 0.853 0.782 0.821 0.892