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

Table 6 Overall performance of the final RF models in CS3

Classification^b	Method4 modified			Method1 (Eq. 1) ^d			Method2 (Eq. 3) ^d			Method3 (Eq. 4) ^d
Classification^b	Str^e	Ts^f	Vd^g	Str^e	Ts^f	Vd^g	Str^e	Ts^f	Vd^g	Str^e	Ts^f	Vd^g	Str^e	Ts^f	Vd^g
TP	1011	252	419	1023	251	420	1015	250	412	1030	243	418	1020	249	427
TN	744	190	303	753	193	315	746	193	313	730	187	303	731	188	302
FP	230	56	95	221	53	83	228	53	85	244	59	95	243	58	96
FN	191	46	73	179	47	72	187	48	80	172	55	74	182	49	65
Sn (%)	84.11	77.24	76.13	85.11	78.45	79.15	84.44	78.45	78.64	85.69	76.02	76.13	84.86	76.42	75.88
Sp (%)	76.39	84.56	85.16	77.31	84.22	85.37	76.59	83.89	83.74	74.95	81.54	84.96	75.05	83.56	86.79
Acc (%)	80.65	81.25	81.12	81.62	81.62	82.58	80.93	81.43	81.46	80.88	79.04	81.01	80.47	80.33	81.91
F1 score (%)	82.77	83.17	83.3	83.65	83.39	84.42	83.03	83.19	83.32	83.2	81	83.18	82.76	82.31	84.13
MCC^c	0.608	0.621	0.617	0.627	0.628	0.647	0.613	0.625	0.625	0.612	0.576	0.614	0.604	0.602	0.633

^aThe most significant results are highlighted in bold. All the models were generated using random state ‘None’ in Module 2 of the toolkit
^bTP: True positive, TN: True negative, FP: False positive, FN: False negative, Sn: Sensitivity, Sp: Specificity, Acc: Accuracy
^cMatthews correlation coefficient
^dNo secondary experimental elements used
^eSub-training set
^fTest set
^gValidation set

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