Chemometrics and Intelligent Laboratory Systems

1989

DOI: 10.1016/0169-7439(89)80063-2

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Composition activity relationships — CARE

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Cited by 7 publications

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Comparative quantitative structure–activity relationship study of some 1-aminocyclopentyl-3-carboxyamides as CCR2 inhibitors using stepwise MLR, FA-MLR, and GA-PLS

¹

,

Madadkar-Sobhani

²

,

³

et al. 2010

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Multiple linear regression (MLR), factor analysis in combination with multiple linear regression (FA-MLR), and genetic algorithm subset selection partial least square (GA-PLS) regression methods were used for quantitative structure-activity relationships (QSAR) model building. These approaches were employed to investigate the correlation between pIC 50 and various physicochemical descriptors of 28 compounds of 1-aminocyclopentyl-3-carboxyamides including substituted tetrahydropyran moieties as CCR2 inhibitors. The obtained models were validated using cross-validation and external test set. The predictability and robustness of the developed models were considered by some figures of merit such as RMSEP and Y-randomization. MLR, FA-MLR, and GA-PLS have R 2 equal to 0.84, 0.69, and 0.93, respectively. Predicted variance by MLR, FA-MLR, and GA-PLS (R 2 test) is 78, 75, and 78%, respectively. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. The prediction results by models are in good agreement with the experimental value.

Comparative quantitative structure–activity relationship study of some 1-aminocyclopentyl-3-carboxyamides as CCR2 inhibitors using stepwise MLR, FA-MLR, and GA-PLS

¹

,

Madadkar-Sobhani

²

,

³

et al. 2010

View full text Add to dashboard Cite

Multiple linear regression (MLR), factor analysis in combination with multiple linear regression (FA-MLR), and genetic algorithm subset selection partial least square (GA-PLS) regression methods were used for quantitative structure-activity relationships (QSAR) model building. These approaches were employed to investigate the correlation between pIC 50 and various physicochemical descriptors of 28 compounds of 1-aminocyclopentyl-3-carboxyamides including substituted tetrahydropyran moieties as CCR2 inhibitors. The obtained models were validated using cross-validation and external test set. The predictability and robustness of the developed models were considered by some figures of merit such as RMSEP and Y-randomization. MLR, FA-MLR, and GA-PLS have R 2 equal to 0.84, 0.69, and 0.93, respectively. Predicted variance by MLR, FA-MLR, and GA-PLS (R 2 test) is 78, 75, and 78%, respectively. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. The prediction results by models are in good agreement with the experimental value.

Composition activity relationships — CARE

Vogt¹,

²

,

³

et al. 1989

Chemometrics and Intelligent Laboratory Systems

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Composition activity relationships — CARE

Vogt¹,

1989

Chemometrics and Intelligent Laboratory Systems

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