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

Q3 Biochemistry, Genetics and Molecular Biology Turkish Computational and Theoretical Chemistry Pub Date : 2018-12-15 DOI:10.33435/TCANDTC.397449
S. Khamouli, S. Belaidi, H. Belaidi, L. Belkhiri
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引用次数: 2

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 R 2 CV = 0.973 and external predictive ability of prediction R 2 = 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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氨基嘧啶衍生物作为结核分枝杆菌蛋白激酶B抑制剂的QSAR研究
利用多种物理化学和量子描述符,对一系列氨基嘧啶衍生物作为PknB抑制剂进行了定量构效关系(QSAR)分析。采用多元线性回归(MLR)方法对分子描述符与氨基嘧啶衍生物的化疗活性之间的关系进行了建模。验证过程中得到的实验活度值与预测活度值吻合良好,表明所建立的QSAR模型质量良好。交叉验证的最佳QSAR模型的相关系数r2 CV = 0.973,具有统计学显著性,MLR的预测能力为r2 = 0.778。经内外验证,该模型具有良好的鲁棒性和可预测性。
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来源期刊
Turkish Computational and Theoretical Chemistry
Turkish Computational and Theoretical Chemistry Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
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