Classification of Natural Estrogen-Like Isoflavonoids and Diphenolics by QSAR Tools

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Luan, F (Author) Other; Lu, YX (Author) Other; Liu, HT (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Combinatorial Chemistry and High Throughput Screening

Vol. 18

Pages: 712-722

ISSN: 1386-2073

Publisher: Bentham Science

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00G-NSV

DOI: 10.2174/1386207318666150803140614

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11