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Please use this identifier to cite or link to this item: http://dspace.utalca.cl/handle/1950/8887

Title: 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
Authors: Caballero, J.
Quiliano, M.
Alzate-Morales, J.H.
Zimic, M.
Deharo, E.
Keywords: c-Met kinase inhibitors
Molecular docking
Quantitative structure-activity relationships
Topological descriptors
Issue Date: Apr-2011
Publisher: SPRINGER
Citation: JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN Volume: 25 Issue: 4 Pages: 349-369 DOI: 10.1007/s10822-011-9425-1
Abstract: 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 IC(50) 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.
Description: Caballero, J (reprint author), Univ Talca, Fac Ingn & Bioinformat, Ctr Bioinformat & Simulac Mol, 2 Norte 685,Casilla 721, Talca, Chile.
URI: http://dspace.utalca.cl/handle/1950/8887
ISSN: 0920-654X
Appears in Collections:Artículos en publicaciones ISI - Universidad de Talca

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