2018Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry :Open access

QSAR Studies of amino-pyrimidine derivatives as Mycobacterium tuberculosis Protein Kinase B inhibitors

Saida Khamoulı, Salah Belaıdı, Houmam Belaıdı, Lotfi Belkhırı

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Abstract

Quantitative structure activity relationship (QSAR) analysis was applied to a series of amino-pyrimidine derivatives as PknB inhibitors using a combination of various physicochemical and quantum descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the amino-pyrimidine derivatives. Good agreement between experimental and predicted activity values, obtained in the validation procedure, indicated the good quality of the derived QSAR model. The statistically significant best QSAR model has a cross validated correlation coefficient R2CV= 0.973 and external predictive ability of prediction R2 = 0.778 was developed by MLR. The proposed model has good robustness and predictability when verified by internal and external validation.

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Quantitative structure activity relationship (QSAR) analysis was applied to a series of amino-pyrimidine derivatives as PknB inhibitors using a combination of various physicochemical and quantum descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the amino-pyrimidine derivatives. Good agreement between experimental and predicted activity values, obtained in the validation procedure, indicated the good quality of the derived QSAR model. The statistically significant best QSAR model has a cross validated correlation coefficient R2CV= 0.973 and external predictive ability of prediction R2 = 0.778 was developed by MLR. The proposed model has good robustness and predictability when verified by internal and external validation.

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Available abstract

Quantitative structure activity relationship (QSAR) analysis was applied to a series of amino-pyrimidine derivatives as PknB inhibitors using a combination of various physicochemical and quantum descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the amino-pyrimidine derivatives. Good agreement between experimental and predicted activity values, obtained in the validation procedure, indicated the good quality of the derived QSAR model. The statistically significant best QSAR model has a cross validated correlation coefficient R2CV= 0.973 and external predictive ability of prediction R2 = 0.778 was developed by MLR. The proposed model has good robustness and predictability when verified by internal and external validation.

Key concepts: Quantitative structure–activity relationship, Pyrimidine, Molecular descriptor, Mycobacterium tuberculosis, Linear regression, Robustness (evolution), Correlation coefficient, Predictability

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