¹³C NMR-distance matrix descriptors: optimal abstract 3D space granularity for predicting estrogen binding.
J Chem Inf Model
; 52(7): 1854-64, 2012 Jul 23.
Article
em En
| MEDLINE
| ID: mdl-22681591
ABSTRACT
An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ×10 ppm × Z Å (Z = 0.5, ..., 2.5 Å). It was hypothesized that this optimum depends on the chemical shifts' estimation error (±4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Receptores de Estrogênio
/
Estrogênios
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos