QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Table 8 Overall performance of the final non-linear models for case study 4

Classification ^b	RF (without HPO^c/QSAR-Co)^d			RF (with HPO/QSAR-Co-X)			GB (with HPO/QSAR-Co-X)
Classification ^b	Str(tenfold CV)^e	Ts^f	Vd^g	Str(tenfold CV)^e	Ts^f	Vd ^g	Str (tenfold CV)^e	Ts^f	Vd^g
TP	994	431	341	969	433	343	996	443	346
TN	953	405	317	936	405	316	949	406	318
FP	46	17	10	63	17	11	50	16	9
FN	44	20	14	69	18	12	42	8	9
Sn (%)	95.76	95.57	96.06	93.35	95.97	96.64	95.95	96.21	97.46
Sp (%)	95.4	95.97	96.94	93.69	96.01	96.62	94.99	98.22	97.25
Acc (%)	95.58	91.52	96.48	93.52	95.99	96.63	95.48	97.25	97.36
MCC^h	0.912	0.915	0.93	0.884	0.920	0.932	0.91	0.945	0.947

^aThe most significant results are highlighted in bold. QSAR-Co-X were generated using random state 1 in Module 2 of the toolkit
^bTP: True positive, TN: True negative, FP: False positive, FN: False negative, Sn: Sensitivity, Sp: Specificity, Acc: Accuracy
^cHPO: Hyperparameter optimisation
^dModel previously reported in [15]
^eSub-training set
^fTest set
^gValidation set
^hMatthews correlation coefficient

ISSN: 1758-2946