ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

Table 5 Statistical results for the QSAR models based on 150 descriptors and Substructural fingerprints for the test set

	R ²_adj	q ²	\(q_{ext}^{2}\)	RMSE_train	MAE_train	RMSE_test	MAE_test	AD coverage (%)
kNN	0.859	0.851	0.642	0.335	0.241	0.667	0.358	41.8
RF	0.942	0.923	0.646	0.241	0.172	0.693	0.527	77.8
SVM	0.751	0.736	0.638	0.446	0.272	0.682	0.500	58.4
RVM	0.938	0.937	0.659	0.218	0.168	0.660	0.495	55.9
laGP	0.761	0.741	0.635	0.442	0.331	0.692	0.528	68.8
MPLE	0.651	0.630	0.591	0.528	0.384	0.735	0.563	79.2
XGBoost	0.922	0.904	0.635	0.269	0.203	0.687	0.521	67.4
Consensus	0.894	NA	0.689	0.323	0.242	0.652	0.493	68.8
Consensus (Except MPLE)	0.904	NA	0.690	0.303	0.228	0.646	0.487	65.8

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