Literature DB >> 17112242

Top-priority fragment QSAR approach in predicting pesticide aquatic toxicity.

Mose' Casalegno1, Guido Sello, Emilio Benfenati.   

Abstract

In the framework of pesticide risk assessment, a fragment-based QSAR approach is presented to correlate LC50-96 h acute toxicity to the rainbow trout (Oncorhynchus mykiss). While there are other fragment-based modeling routes, our approach exploits the possibility of prioritizing fragments' contributions to toxicity. On the assumption that one fragment might be mainly responsible for the molecular toxicity, we developed a three-stage modeling strategy to select the most important moieties and to establish their priorities at a molecular level. This strategy was tested on a heterogeneous dataset containing 282 pesticides, collected under the EU-funded project Demetra. Quantitative toxicity prediction yielded good results for the training set (R2TR = 0.85) and the test set (R2TS = 0.75). The advantages and limitations of the current priority strategy are examined.

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Year:  2006        PMID: 17112242     DOI: 10.1021/tx0601814

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  4 in total

1.  In silico prediction of pesticide aquatic toxicity with chemical category approaches.

Authors:  Fuxing Li; Defang Fan; Hao Wang; Hongbin Yang; Weihua Li; Yun Tang; Guixia Liu
Journal:  Toxicol Res (Camb)       Date:  2017-07-31       Impact factor: 3.524

Review 2.  Recent advances in fragment-based QSAR and multi-dimensional QSAR methods.

Authors:  Kyaw Zeyar Myint; Xiang-Qun Xie
Journal:  Int J Mol Sci       Date:  2010-10-08       Impact factor: 5.923

3.  High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury.

Authors:  Lauren P Wills; Gyda C Beeson; Richard E Trager; Christopher C Lindsey; Craig C Beeson; Yuri K Peterson; Rick G Schnellmann
Journal:  Toxicol Appl Pharmacol       Date:  2013-06-26       Impact factor: 4.219

4.  Weighted feature significance: a simple, interpretable model of compound toxicity based on the statistical enrichment of structural features.

Authors:  Ruili Huang; Noel Southall; Menghang Xia; Ming-Hsuang Cho; Ajit Jadhav; Dac-Trung Nguyen; James Inglese; Raymond R Tice; Christopher P Austin
Journal:  Toxicol Sci       Date:  2009-10-04       Impact factor: 4.849

  4 in total

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