Classification of natural estrogen-like isoflavonoids and diphenolics by QSAR tools

Author(s): Feng Luan, Yuxi Lu, Huitao Liu, Maria N.D.S. Cordeiro

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 18 , Issue 8 , 2015

Become EABM
Become Reviewer

Abstract:

This work reports a detailed study of the ability of linear and non-linear classification methods to estimate the estrogenic activities of a series of 55 natural estrogen-like isoflavonoid and diphenolic compounds. In doing so, we examined the use of linear discriminant analysis (LDA) and nonlinear support vector machines (SVMs) techniques along with feature selection algorithms. The structural characteristics of each of the studied compounds were calculated from the optimized molecular geometries. Both the LDA and SVMs models contain four descriptors, however, the SVMs model (total accuracy 89.1%) was found to be superior to the LDA model (total accuracy 80.0%). The analysis of molecular descriptors within our models provided essential insights towards a better understanding of the estrogenic mechanisms of natural estrogen-like phytoestrogens. Furthermore, the derived models can be applied in the future screening of other natural estrogen-like compounds.

Keywords: Classification, QSAR, linear discriminant analysis, isoflavonoids and diphenolics, support vector machines.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 18
ISSUE: 8
Year: 2015
Page: [712 - 722]
Pages: 11
DOI: 10.2174/1386207318666150803140614
Price: $65

Article Metrics

PDF: 26
HTML: 1