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

Julio Caballero, Miguel Quiliano, Jans H. Alzate-Morales, Mirko Zimic, Eric Deharo

Research output: Contribution to journalArticle

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 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.

Original languageEnglish (US)
Pages (from-to)349-369
Number of pages21
JournalJournal of Computer-Aided Molecular Design
Volume25
Issue number4
DOIs
StatePublished - Apr 2011
Externally publishedYes

Fingerprint

Quantitative Structure-Activity Relationship
Aniline
aniline
inhibitors
Phosphotransferases
Derivatives
Static Electricity
correlation coefficients
Inhibitory Concentration 50
Hydrogen
Linear Models
Weights and Measures
activity (biology)
Bioactivity
Autocorrelation
Linear regression
Crystal orientation
cross correlation
autocorrelation
Conformations

Keywords

  • c-Met kinase inhibitors
  • CoMSIA
  • Molecular docking
  • Quantitative structure-activity relationships
  • Topological descriptors

ASJC Scopus subject areas

  • Drug Discovery
  • Physical and Theoretical Chemistry
  • Computer Science Applications

Cite this

@article{4622c6229b7945f58fa403ac625541c3,
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",
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 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.",
keywords = "c-Met kinase inhibitors, CoMSIA, Molecular docking, Quantitative structure-activity relationships, Topological descriptors",
author = "Julio Caballero and Miguel Quiliano and Alzate-Morales, {Jans H.} and Mirko Zimic and Eric Deharo",
year = "2011",
month = "4",
doi = "10.1007/s10822-011-9425-1",
language = "English (US)",
volume = "25",
pages = "349--369",
journal = "Journal of Computer-Aided Molecular Design",
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T1 - 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

AU - Caballero, Julio

AU - Quiliano, Miguel

AU - Alzate-Morales, Jans H.

AU - Zimic, Mirko

AU - Deharo, Eric

PY - 2011/4

Y1 - 2011/4

N2 - 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.

AB - 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.

KW - c-Met kinase inhibitors

KW - CoMSIA

KW - Molecular docking

KW - Quantitative structure-activity relationships

KW - Topological descriptors

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U2 - 10.1007/s10822-011-9425-1

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JO - Journal of Computer-Aided Molecular Design

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