Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors

被引:28
作者
Caballero, Julio [1 ]
Quiliano, Miguel [2 ]
Alzate-Morales, Jans H. [1 ]
Zimic, Mirko [2 ]
Deharo, Eric [3 ,4 ]
机构
[1] Univ Talca, Fac Ingn & Bioinformat, Ctr Bioinformat & Simulac Mol, Talca, Chile
[2] Univ Peruana Cayetano Heredia, Fac Ciencias & Filosofia, Lab Invest & Desarrollo, Bioinformat Unit Drug Design Grp, Lima, Peru
[3] Univ Toulouse, UPS, UMR Lab Pharmacochim Subst Nat & Pharmacophores R, F-31062 Toulouse 9, France
[4] IRD, UMR 152, Lima, Peru
关键词
c-Met kinase inhibitors; Molecular docking; Quantitative structure-activity relationships; CoMSIA; Topological descriptors; GENETIC NEURAL-NETWORKS; SIGNALING PATHWAY; 2D AUTOCORRELATION; GETAWAY DESCRIPTORS; GUANINE DERIVATIVES; CDK2; INHIBITORS; BINDING MODES; PREDICTION; DESIGN; COMFA;
D O I
10.1007/s10822-011-9425-1
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC50 values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.
引用
收藏
页码:349 / 369
页数:21
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