In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization

Table 3 The statistical characteristics of CORAL models for pIC₅₀ generated with TF1 and TF2

Split	Target function	Set	n	R²	CCC	IIC	Q²	s	MAE	Y-rand	\({cR}_{p}^{2}\)	\({\overline{r} }_{m}^{2}\)	\({\Delta r}_{m}^{2}\)	F
1	TF1	TRN	29	0.813	0.897	0.551	0.779	0.234	0.189	0.033	0.797	–	–	117
		iTRN	20	0.813	0.866	0.607	0.773	0.275	0.229	0.050	0.788	–	–	78
		CAL	12	0.575	0.706	0.378	0.228	0.437	0.334	0.162	0.487	–	–	14
		VAL	19	0.679	0.765	0.402	0.549	0.364	0.259	–	–	0.510	0.279	36
	TF2	TRN	29	0.723	0.839	0.794	0.674	0.284	0.214	0.032	0.707	–	–	70
		iTRN	20	0.804	0.806	0.462	0.759	0.338	0.285	0.050	0.778	–	–	74
		CAL	12	0.688	0.802	0.828	0.552	0.313	0.267	0.064	0.655	–	–	22
		VAL	19	0.729	0.789	0.489	0.666	0.272	0.207	–	–	0.555	0.253	46
2	TF1	TRN	30	0.881	0.937	0.821	0.864	0.185	0.133	0.033	0.865	–	–	207
		iTRN	19	0.854	0.896	0.492	0.820	0.226	0.165	0.079	0.813	–	–	99
		CAL	11	0.502	0.653	0.279	0.029	0.546	0.378	0.087	0.456	–	–	9
		VAL	20	0.652	0.759	0.642	0.563	0.399	0.321	–	–	0.494	0.287	34
	TF2	TRN	30	0.781	0.877	0.773	0.740	0.251	0.175	0.027	0.767	–	–	100
		iTRN	19	0.805	0.831	0.865	0.752	0.278	0.216	0.049	0.780	–	–	70
		CAL	11	0.939	0.863	0.968	0.923	0.319	0.263	0.040	0.919	–	–	140
		VAL	20	0.775	0.826	0.611	0.713	0.334	0.269	–	–	0.613	0.218	62
3	TF1	TRN	30	0.736	0.848	0.751	0.674	0.288	0.204	0.043	0.714	–	–	78
		iTRN	18	0.754	0.853	0.856	0.704	0.277	0.231	0.077	0.714	–	–	49
		CAL	12	0.418	0.571	0.476	0.137	0.440	0.336	0.076	0.378	–	–	7
		VAL	20	0.651	0.798	0.651	0.457	0.302	0.231	–	–	0.518	0.190	34
	TF2	TRN	30	0.782	0.878	0.774	0.735	0.261	0.194	0.020	0.772	–	–	101
		iTRN	18	0.850	0.909	0.857	0.812	0.223	0.189	0.048	0.825	–	–	90
		CAL	12	0.777	0.807	0.881	0.697	0.300	0.246	0.100	0.725	–	–	35
		VAL	20	0.727	0.847	0.628	0.642	0.266	0.209	–	–	0.615	0.165	48

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