Quantitative structure-retention relationships (QSRRs) are used to correlate paper chromatographic retention factors of disperse dyes with theoretical molecular descriptors. A data set of 23 compounds with known RF values was used. The genetic algorithm-multiple linear regression analysis (GA-MLR) with three selected theoretical descriptors was obtained. The stability and predictability of the model was validated by use of leave-one-out (LOO), leave-many-out (LMO) cross-validation, external validation, Y-randomization and applicability domain (AD) analysis. The GA-MLR revealed a statistically meaningful model showing the dependence of the RF value on sum of topological distances between N and Br atoms (T(N..Br)), global topological charge index (JGT) and R autocorrelation of lag 5 / unweighted (R5u_A) of the compounds.
Chaichi, M. J., & Fatemi, M. H. (2016). QSRR Study of Organic Dyes by Multiple Linear Regression Method Based on Genetic Algorithm (GA–MLR. Progress in Color, Colorants and Coatings, 9(3), 195-206. doi: 10.30509/pccc.2016.75886
MLA
M. J. Chaichi; M. H. Fatemi. "QSRR Study of Organic Dyes by Multiple Linear Regression Method Based on Genetic Algorithm (GA–MLR", Progress in Color, Colorants and Coatings, 9, 3, 2016, 195-206. doi: 10.30509/pccc.2016.75886
HARVARD
Chaichi, M. J., Fatemi, M. H. (2016). 'QSRR Study of Organic Dyes by Multiple Linear Regression Method Based on Genetic Algorithm (GA–MLR', Progress in Color, Colorants and Coatings, 9(3), pp. 195-206. doi: 10.30509/pccc.2016.75886
VANCOUVER
Chaichi, M. J., Fatemi, M. H. QSRR Study of Organic Dyes by Multiple Linear Regression Method Based on Genetic Algorithm (GA–MLR. Progress in Color, Colorants and Coatings, 2016; 9(3): 195-206. doi: 10.30509/pccc.2016.75886
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