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. Author manuscript; available in PMC: 2019 Sep 8.

Published in final edited form as: Mol Pharm. 2019 May 3;16(6):2605–2615. doi: 10.1021/acs.molpharmaceut.9b00182

Table 3.

AUC Values of All Machine Learning Models with Each Dataset on Test Set^a

datasets			MLP
datasets		SVM	MLP_1	MLP_2	MLP_3	MLP_4	MLP_5	RF	ABDT	DT	NB	logistic
molecular descriptor	CB1 test	0.922	0.852	0.854	0.866	0.898	0.891	0.914	0.915	0.818	0.935	0.826
	CB2 test	0.931	0.914	0.906	0.908	0.918	0.920	0.904	0.927	0.807	0.917	0.891
	CB1O/CB1A test	0.923	0.940	0.769	0.800	0.887	0.925	0.915	0.979	0.822	0.924	0.915
MACCS	CB1 test	0.892	0.902	0.882	0.902	0.907	0.893	0.848	0.880	0.796	0.832	0.903
	CB2 test	0.917	0.924	0.928	0.923	0.920	0.915	0.891	0.895	0.839	0.872	0.928
	CB1O/CB1A test	0.935	0.970	0.969	0.955	0.945	0.948	0.868	0.970	0.834	0.813	0.937
ECFP6	CB1 test	0.861	0.916	0.896	0.899	0.909	0.917	0.893	0.899	0.764	0.942	0.912
	CB2 test	0.936	0.945	0.940	0.947	0.930	0.934	0.900	0.926	0.811	0.957	0.953
	CB1O/CB1A test	0.939	0.979	0.979	0.984	0.982	0.944	0.873	0.972	0.872	0.973	0.978

^a

Each bold entry shows the highest metric value among the machine learning models using different algorithms.