Table 1 The result of AUC (Area under Curve) and test accuracy for all models based on 71 nucleoside derivatives

Models	Features	Test Accuracy		AUC
Models	Features	Mean	SEM	Mean	SEM
DT*	Descriptor_4175	0.65	0.01	0.65	0.02
LR	Descriptor_4175	0.65	0.02	0.67	0.02
RF	Descriptor_4175	0.63	0.01	0.72	0.02
XGBoost	Descriptor_4175	0.63	0.01	0.69	0.02
DT	Descriptor_144	0.64	0.01	0.64	0.01
LR	Descriptor_144	0.68	0.02	0.80	0.02
RF	Descriptor_144	0.67	0.01	0.75	0.02
XGBoost	Descriptor_144	0.64	0.02	0.72	0.02
DT	Descriptor_40	0.66	0.02	0.69	0.02
LR	Descriptor_40	0.70	0.01	0.81	0.02
RF	Descriptor_40	0.67	0.01	0.74	0.02
XGBoost	Descriptor_40	0.65	0.01	0.75	0.02
DT	Descriptor_ REF ^#	0.59	0.02	0.63	0.02
LR	Descriptor_ REF ^#	0.71	0.01	0.84	0.02
RF	Descriptor_ REF ^#	0.67	0.01	0.75	0.02
XGBoost	Descriptor_ REF ^#	0.70	0.02	0.79	0.02

*LR Logistic regression, DT Decision tree, RF Random forest, XGBoost Extreme gradient boosting, SEM Standard error of the mean.
^#Descriptors-REF: Recursive feature elimination (REF) has different optimal descriptors for different Algorithms: LR, n = 24; XGBoost, n = 16; DT, n = 30; RF, n = 37.

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