Robust optimization of SVM hyperparameters in the classification of bioactive compounds

Table 4 The average AUC values–global, obtained for a particular fingerprint and particular target

Fingerprint/target	Bayes	Random	Grid search	SVMlight	libSVM
global	0.883*	0.870	0.799	0.676	0.792
EstateFP	0.847*	0.829	0.774	0.690	0.763
ExtFP	0.902*	0.891	0.806	0.669	0.874
KlekFP	0.899*	0.889	0.812	0.669	0.730
MACCSFP	0.890*	0.876	0.798	0.683	0.828
PubchemFP	0.898*	0.885	0.816	0.669	0.808
SubFP	0.864*	0.854	0.787	0.677	0.749
5-HT\(_\text {2A}\)	0.860*	0.850	0.780	0.683	0.743
5-HT\(_\text {2C}\)	0.848*	0.821	0.702	0.568	0.717
5-HT\(_\text {6}\)	0.913*	0.910	0.886	0.814	0.862
5-HT\(_\text {7}\)	0.830*	0.816	0.748	0.675	0.714
CDK2	0.876*	0.875	0.796	0.664	0.768
M\(_\text {1}\)	0.850*	0.843	0.778	0.557	0.748
ERK2	0.958	0.961*	0.949	0.931	0.942
AChE	0.884*	0.854	0.788	0.611	0.764
A\(_\text {1}\)	0.843*	0.835	0.764	0.564	0.720
alpha2AR	0.875*	0.874	0.773	0.563	0.725
beta1AR	0.910*	0.864	0.798	0.710	0.828
beta3AR	0.874*	0.823	0.826	0.545	0.722
CB1	0.874*	0.854	0.782	0.622	0.793
DOR	0.888*	0.880	0.734	0.599	0.814
D\(_\text {4}\)	0.841*	0.837	0.759	0.698	0.745
H\(_\text {1}\)	0.898*	0.880	0.638	0.548	0.801
H\(_\text {3}\)	0.937*	0.926	0.906	0.897	0.905
HIVi	0.939	0.945*	0.934	0.901	0.911
IR	0.936*	0.936*	0.925	0.886	0.897
ABL	0.850*	0.831	0.748	0.587	0.733
HLE	0.867*	0.865	0.763	0.578	0.779

The highest values obtained among all strategies tested are marked with an asterisk sign

ISSN: 1758-2946