Table 2.

The performance of different kinds of feature descriptors with various machine learning algorithms based on main dataset using 5-fold cross-validation.

Machine learning algorithm	Feature descriptor
	PP	EI	BP + NBP + PP	BP + NBP + EI	PP + EI	BP + NBP + PP + EI
	Accuracy (%)
SVM-SMO	83.2	85.7	85.6	87.3	87.3	89.6
Simple logistic regression	81.8	84.2	84.2	87.0	85.7	88.3
Random forest	81.3	84.3	83.5	86.7	85.9	88.1
Naive bayes	78.6	77.2	82.8	82.3	82.6	84.3
Decision tree	80.2	82.5	82.6	84.4	84.1	86.2
	Sensitivity (%)
SVM-SMO	82.4	84.9	84.4	86.5	85.8	88.4
Simple logistic regression	80.7	83.1	82.3	84.4	85.6	86.7
Random forest	81.1	83.6	82.8	86.0	85.3	86.2
Naive bayes	76.9	76.1	79.4	80.8	81.1	82.6
Decision tree	78.6	80.4	81.7	82.7	82.5	84.7
	Specificity (%)
SVM-SMO	84.6	86.3	86.7	88.2	88.6	90.8
Simple logistic regression	82.9	85.5	86.0	88.8	85.9	90.2
Random forest	81.6	85.2	84.1	87.5	86.3	90.0
Naive bayes	80.2	78.5	85.6	83.8	84.7	86.0
Decision tree	81.8	84.7	83.5	86.2	85.7	87.7
	Matthew correlation coefficient
SVM-SMO	0.55	0.58	0.62	0.66	0.66	0.67
Simple logistic regression	0.56	0.55	0.64	0.62	0.64	0.66
Random forest	0.55	0.56	0.60	0.62	0.63	0.66
Naive bayes	0.52	0.49	0.56	0.53	0.54	0.59
Decision tree	0.53	0.55	0.61	0.63	0.62	0.64
	AUC
SVM-SMO	0.83	0.86	0.86	0.88	0.87	0.90
Simple logistic regression	0.83	0.84	0.85	0.86	0.85	0.88
Random forest	0.81	0.84	0.84	0.86	0.85	0.87
Naive bayes	0.78	0.76	0.80	0.79	0.80	0.82
Decision tree	0.80	0.82	0.83	0.84	0.84	0.86

BP: binding propensity feature; NBP: nonbinding propensity feature; PP: physicochemical property feature; EI: evolutionary information feature.