期刊
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
卷 72, 期 3, 页码 787-794出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ecoenv.2008.09.002
关键词
Heuristic method; Radial basis function neural networks; Quantitative structure-activity; relationships; Toxicity; Chlorella vulgaris
资金
- National Natural Science Foundation of China [90612016]
- Allan Grey, foreign English teacher at Lanzhou University
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. (c) 2008 Elsevier Inc. All rights reserved.
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