Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?

Figures & data

Figure 1. Generalized graphic representation of the evolution of Monte Carlo optimization with different target functions.

Table 1. The statistical quality of models when applying TF_IICand TF_CII.

Target function	Split	Set	n^a	R^2	CCC	IIC	CII	Q^2	RMSE	F
TFIIC	1	A	46	0.7526	0.8588	0.6673	0.8687	0.7318	0.659	134
		P	47	0.7763	0.8795	0.8635	0.8697	0.7567	0.598	156
		C	48	0.707	0.8083	0.8398	0.8095	0.6645	0.597	111
		V	47	0.8962	–	–	–	–	0.36	–
	2	A	47	0.8908	0.9423	0.7623	0.9245	0.8837	0.463	367
		P	47	0.902	0.9497	0.9041	0.9235	0.8966	0.454	414
		C	47	0.77	0.8762	0.8775	0.8623	0.7539	0.374	151
		V	47	0.8889	–	–	–	–	0.29	–
	3	A	48	0.8281	0.906	0.91	0.8867	0.8152	0.548	222
		P	46	0.8736	0.9265	0.6723	0.8978	0.8663	0.582	304
		C	48	0.7293	0.8444	0.8538	0.8219	0.7112	0.335	124
		V	46	0.6701	–	–	–	–	0.36	–
	4	A	47	0.8684	0.9296	0.8201	0.9046	0.8587	0.459	297
		P	44	0.9216	0.949	0.9301	0.9382	0.9162	0.467	494
		C	48	0.8798	0.9314	0.9375	0.9167	0.8699	0.377	337
		V	49	0.7761	–	–	–	–	0.36	–
	5	A	47	0.8861	0.9396	0.7603	0.9249	0.8739	0.418	350
		P	47	0.7905	0.8733	0.8844	0.8784	0.7669	0.533	170
		C	46	0.6485	0.8009	0.8053	0.8255	0.621	0.571	81
		V	48	0.8891	–	–	–	–	0.44	–
TFCII	1	A	46	0.9121	0.954	0.6721	0.9398	0.9052	0.393	456
		P	47	0.9122	0.9517	0.8044	0.9348	0.9059	0.391	468
		C	48	0.9647	0.98	0.6998	0.9748	0.9618	0.205	1259
		V	47	0.9625	–	–	–	–	0.22	–
	2	A	47	0.9082	0.9519	0.6467	0.9339	0.9021	0.425	445
		P	47	0.9081	0.9523	0.7574	0.9327	0.9027	0.449	445
		C	47	0.9508	0.9615	0.5931	0.9767	0.9445	0.206	870
		V	47	0.9654	–	–	–	–	0.19	–
	3	A	48	0.9294	0.9634	0.8157	0.9479	0.9248	0.351	606
		P	46	0.9343	0.959	0.5368	0.9515	0.9304	0.454	626
		C	48	0.9437	0.9691	0.7439	0.9673	0.9387	0.166	771
		V	46	0.9603	–	–	–	–	0.13	–
	4	A	49	0.9281	0.9627	0.785	0.9448	0.9237	0.332	607
		P	47	0.9251	0.9535	0.8414	0.9444	0.9197	0.398	556
		C	47	0.9565	0.9762	0.6567	0.9686	0.9536	0.225	990
		V	45	0.9641	–	–	–	–	0.18	–
	5	A	48	0.885	0.939	0.9407	0.9142	0.878	0.452	354
		P	49	0.9103	0.9504	0.8665	0.9377	0.9042	0.426	477
		C	45	0.8957	0.8972	0.9294	0.9612	0.8792	0.256	369
		V	46	0.9635	–	–	–	–	0.2	–

^a n: the number of quasi-SMILES in a set; R²: determination coefficient; CCC: concordance correlation coefficient; IIC: index of ideality of correlation; CII: correlation intensity index; RMSE: root mean squared error; F: Fischer’s F-ratio. Numbers in bold indicate the best model.

Table 2. The promoters of increase and decrease for toxicity toward tadpoles.

Attributes of quasi-SMILES	CWs 1	CWs 2	CWs 3	CWs 4	CWs 5	NA	NP	NC	The role
[xyzyx0]….	4.0021	3.0038	1.4311	5.3658	3.2339	47	47	47	Increaser
1………	1.2097	0.1997	0.7278	1.9921	1.0509	45	46	46	Increaser
Cl(…….	0.2749	0.0949	0.0551	0.4824	0.3633	22	25	16	Increaser
[O-]……	0.3785	0.2178	0.0611	1.0397	1.0764	18	17	20	Increaser
[72h]…….	1.0175	1.0447	1.2006	1.0955	0.8959	13	5	7	Increaser
[xyx2]……	0.7536	0.9893	1.5252	0.5997	0.8828	11	13	6	Increaser
c…Cl……	0.3332	1.7503	2.8998	3.4210	1.7013	9	11	10	Increaser
[96h]…….	1.0456	1.1544	1.4407	1.2321	1.0888	8	9	7	Increaser
N………	−1.0130	−1.0474	−1.7282	−2.0304	−1.2024	22	22	17	Decreaser

Table 3. Assessment of the predictive potential models observed for different target functions.

Target function	Average value and dispersion of determination coefficient for validation set
TF0	0.8845 ± 0.0674
TFIIC	0.8241 ± 0.0890
TFCII	0.9632 ± 0.0017
TFIIC-CII	0.8419 ± 0.0953

Table 4. Statistical quality of different models of toxicity toward tadpoles.

Number of compounds in the training set	Determination coefficient value for the training set	Number of compounds in the validation set	Determination coefficient value for the validation set	Reference
44	0.84	14	0.82	Nath and Roy (2022)
44	0.72	14	0.96	Toropov et al. (2022)
141	0.83	47	0.97	Toropov, Di Nicola, et al. (2023) and Toropov, Toropova, et al. (2023)
141	0.92	47	0.97	This study

Nath A, Roy K. 2022. Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica. Toxicol In Vitro. 83:105427. doi: 10.1016/j.tiv.2022.105427.

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Toropov AA, Toropova AP, Roncaglioni A, Benfenati E. 2023. In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry. Mutat Res Genet Toxicol Environ Mutagen. 891:503684. doi: 10.1016/j.mrgentox.2023.503684.

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Data availability statement

The data used in this work and developed models are freely available in the Supplementary materials section.