Comprehensive ensemble in QSAR prediction for drug discovery

Kwon, Sunyoung; Bae, Ho; Jo, Jeonghee; Yoon, Sungroh

doi:10.1186/s12859-019-3135-4

BMC Bioinformatics

Table 2 Performance comparison between the proposed comprehensive ensemble and the individual models on 19 bioassay datasets

From: Comprehensive ensemble in QSAR prediction for drug discovery

BioAssay	PubChem fingerprint				ECFP fingerprint				MACCS fingerprint				SMILES	comprehensive
	RF	SVM	GBM	NN	RF	SVM	GBM	NN	RF	SVM	GBM	NN	NN	ensemble
1851_1a2	0.921	0.896	0.900	0.921	0.919	0.906	0.882	0.920	0.912	0.879	0.894	0.912	0.922	0.934
1851_2c19	0.871	0.852	0.848	0.872	0.882	0.871	0.854	0.880	0.874	0.842	0.850	0.885	0.875	0.900
1851_2c9	0.871	0.857	0.851	0.873	0.880	0.866	0.843	0.880	0.858	0.828	0.840	0.870	0.877	0.898
1851_2d6	0.858	0.847	0.832	0.869	0.867	0.850	0.833	0.856	0.854	0.816	0.830	0.852	0.846	0.884
1851_3a4	0.877	0.868	0.865	0.887	0.891	0.887	0.855	0.895	0.867	0.832	0.851	0.875	0.891	0.914
1915	0.754	0.692	0.709	0.722	0.731	0.700	0.700	0.712	0.758	0.716	0.736	0.741	0.701	0.755
2358	0.787	0.705	0.736	0.770	0.780	0.767	0.722	0.761	0.774	0.731	0.763	0.775	0.697	0.803
463213	0.673	0.639	0.652	0.651	0.685	0.652	0.644	0.661	0.668	0.642	0.655	0.651	0.636	0.689
463215	0.620	0.576	0.592	0.604	0.617	0.585	0.598	0.595	0.629	0.600	0.630	0.625	0.587	0.627
488912	0.679	0.643	0.634	0.668	0.693	0.654	0.668	0.675	0.667	0.634	0.650	0.673	0.644	0.698
488915	0.718	0.686	0.679	0.713	0.731	0.693	0.680	0.708	0.692	0.659	0.680	0.693	0.679	0.735
488917	0.808	0.777	0.759	0.805	0.814	0.788	0.760	0.799	0.788	0.726	0.752	0.786	0.780	0.834
488918	0.762	0.745	0.735	0.778	0.778	0.766	0.729	0.767	0.737	0.690	0.708	0.742	0.746	0.799
492992	0.829	0.784	0.783	0.800	0.849	0.807	0.802	0.822	0.825	0.726	0.759	0.790	0.802	0.845
504607	0.694	0.678	0.692	0.686	0.690	0.668	0.673	0.656	0.676	0.640	0.662	0.655	0.649	0.721
624504	0.884	0.850	0.857	0.867	0.884	0.858	0.858	0.861	0.872	0.832	0.862	0.876	0.868	0.897
651739	0.791	0.770	0.773	0.781	0.802	0.782	0.771	0.788	0.779	0.729	0.759	0.754	0.792	0.804
651744	0.884	0.862	0.872	0.885	0.889	0.883	0.875	0.896	0.869	0.829	0.843	0.853	0.899	0.901
652065	0.800	0.752	0.782	0.780	0.785	0.775	0.758	0.774	0.776	0.736	0.759	0.772	0.763	0.826
average	0.794	0.762	0.766	0.786	0.798	0.777	0.763	0.784	0.783	0.741	0.762	0.778	0.771	0.814

Each value shows the averaged AUC from twenty repeated experiments on the test set (bold: top 3 AUC on each dataset), and the last row shows the averaged AUC calculated from 19 AUC results

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