Structural requirements of pyrido[2,3-d]pyrimidin-7-one as CDK4/D inhibitors: 2D autocorrelation, CoMFA and CoMSIA analyses

Julio Caballero, Michael Fernández, Fernando D. González-Nilo

Resultado de la investigación: Article

27 Citas (Scopus)

Resumen

2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.

Idioma originalEnglish
Páginas (desde-hasta)6103-6115
Número de páginas13
PublicaciónBioorganic and Medicinal Chemistry
Volumen16
N.º11
DOI
EstadoPublished - 1 jun 2008

Huella dactilar

Autocorrelation
Cyclin D
Weights and Measures
Neural Networks (Computer)
Quantitative Structure-Activity Relationship
Cyclin-Dependent Kinases
Static Electricity
Neural networks
Linear Models
Catalytic Domain
Linear regression
Structural properties
Electrostatics
Genetic algorithms
Statistics

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Pharmaceutical Science
  • Drug Discovery
  • Clinical Biochemistry
  • Organic Chemistry

Citar esto

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abstract = "2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.",
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Structural requirements of pyrido[2,3-d]pyrimidin-7-one as CDK4/D inhibitors : 2D autocorrelation, CoMFA and CoMSIA analyses. / Caballero, Julio; Fernández, Michael; González-Nilo, Fernando D.

En: Bioorganic and Medicinal Chemistry, Vol. 16, N.º 11, 01.06.2008, p. 6103-6115.

Resultado de la investigación: Article

TY - JOUR

T1 - Structural requirements of pyrido[2,3-d]pyrimidin-7-one as CDK4/D inhibitors

T2 - 2D autocorrelation, CoMFA and CoMSIA analyses

AU - Caballero, Julio

AU - Fernández, Michael

AU - González-Nilo, Fernando D.

PY - 2008/6/1

Y1 - 2008/6/1

N2 - 2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.

AB - 2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.

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