Journal of Cheminformatics

Table 5 The detailed predictive ability of the chosen QSAR models using XGBoost

From: Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes

	Des	SE	SP	F	ACC	AUC
1A2_Sub	2D	0.72 ± 0.070	0.69 ± 0.073	0.71 ± 0.046	0.70 ± 0.040	0.77 ± 0.041
2C9_Sub	2D	0.79 ± 0.055	0.73 ± 0.072	0.76 ± 0.042	0.76 ± 0.039	0.83 ± 0.040
2C19_Sub	2D	0.76 ± 0.060	0.74 ± 0.070	0.75 ± 0.048	0.75 ± 0.043	0.82 ± 0.039
2D6_Sub	2D	0.80 ± 0.046	0.79 ± 0.044	0.79 ± 0.034	0.79 ± 0.030	0.86 ± 0.029
3A4_Sub	MACCS	0.77 ± 0.034	0.76 ± 0.037	0.77 ± 0.023	0.77 ± 0.021	0.84 ± 0.018
1A2_In	2D	0.84 ± 0.011	0.87 ± 0.009	0.85 ± 0.007	0.86 ± 0.006	0.93 ± 0.004
2C9_In_B^*	2D	0.83 ± 0.013	0.80 ± 0.014	0.82 ± 0.009	0.82 ± 0.009	0.89 ± 0.008
2C19_In	2D	0.82 ± 0.011	0.83 ± 0.010	0.81 ± 0.007	0.82 ± 0.006	0.89 ± 0.005
2D6_In_B^*	2D	0.78 ± 0.018	0.81 ± 0.018	0.79 ± 0.013	0.80 ± 0.012	0.87 ± 0.009
3A4_In	MACCS	0.76 ± 0.011	0.83 ± 0.010	0.77 ± 0.009	0.801 ± 0.007	0.88 ± 0.006

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ISSN: 1758-2946

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