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Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure-activity relationships methods.

Abstract：

:This paper presents the results of an optimization study on the toxicity of 91 aliphatic and aromatic compounds as well as a small subset of pesticides to algae Chlorella vulgaris, which was accomplished by using quantitative structure-activity relationships (QSAR). The linear (HM) and the nonlinear method radial basis function neural networks (RBFNN) were used to develop the QSAR models and both of them can give satisfactory prediction results. At the same time, by interpreting the descriptors, we can get some insight into structural features (molecular surface area, electrostatic repulsion, and hydrogen bonds) related to the toxic action. Finally, a detailed analysis on the model application domain defined the compounds, whose estimation can be accepted with confidence. The results of this study suggest that the proposed approaches could be successfully used as a general tool for the estimate of novel toxic compounds.

journal_name

Ecotoxicol Environ Saf

journal_title

Ecotoxicology and environmental safety

authors

Xia B,Liu K,Gong Z,Zheng B,Zhang X,Fan B

doi

10.1016/j.ecoenv.2008.09.002

subject

Has Abstract

pub_date

2009-03-01 00:00:00

pages

787-94

issue

3

eissn

0147-6513

issn

1090-2414

pii

S0147-6513(08)00245-5

journal_volume

72

pub_type

杂志文章

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