. 2022 Apr 4;221:115824. doi: 10.1016/j.poly.2022.115824

Table 5.

QSAR equations and obtained statistical parameters from QSAR models.

No	QSAR equations	Training		Validation
No	QSAR equations	R_t²	RMSE_t	Q²_LOO	RMSE_LOO	R²_LMO	RMSE_LMO
Model 1	(△G) = 40.7407 + 98.3112(Gap)-2.4502(S)-10.7921(X)	0.935	0.101	0.858	0.264	0.807	0.189
Model 2	(△G) = -5.445 + 7.772(X) + 0.059(Ss)-0.234(nN)-0.266 (nB0) −0.199(nH_ACC)	0.700	0.351	0.664	0.427	0.615	0.422
Model 3	(△G) = 6.904–0.975(AMW) −2.003(nCIR)	0.861	0.526	0.829	0.5468	0.753	0.587
Model 4	(△G) = -2.732–0.089 nH_Acc −0.412 SNar −17.240 X - 0.085 Ss + 0.165 D + 0. 102 μ	0.843	0.341	0.788	0.397	0.732	0.387

R²_t is a correlation coefficient of the training set; RMSE_t is a root mean square error of the training set; Q²_LOO is a correlation coefficient of leave-one-out cross-validation; RMSE_LOO is a root mean square error LOO–CV.