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• W2912898683 abstract "The toxicities of agrochemicals to non-target aquatic organisms are key items in chemical ecological risk assessment. However, it is still an urgent need to develop new tools to assess the agrochemical aquatic toxicity efficiently and accurately. In this work, QSTR studies were performed on a data set containing 639 diverse pesticides with measured EC50 toxicity against Daphnia magna, by using five machine learning methods combined with seven fingerprints and a set of molecular descriptors. The imbalance problem of the data set was successfully solved by clustering analysis. The top-10 QSTR models displayed greater predicative abilities than ECOSAR. The optimal model, Ext-SVM, showed the best performance in 10-fold cross validation (Qhigh=0.807, Qmoderate=0.806, Qlow=0.755, Qtotal=0.794), and also in the test set verification (Qhigh=0.865, Qmoderate=0.783, Qlow=0.931, Qtotal=0.848). The relevance of the key physical-chemical properties with the toxicity was also investigated, in which the MW, a_np, logP(o/w), GCUT_SLOGP_1, chilv and SMR_VSA7 values displayed positive correlation with Daphnia magna toxicity, whereas the logS and a_don showed negative correlation. The robust QSTR models provided efficient tools for assessing agrochemical aquatic toxicity, and the revealed different physical-chemical properties between the high and low toxic compounds might be useful in the discovery and design of low aquatic toxic pesticides." @default.
• W2912898683 created "2019-02-21" @default.
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• W2912898683 date "2019-05-01" @default.
• W2912898683 modified "2023-10-17" @default.
• W2912898683 title "Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna" @default.
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• W2912898683 doi "https://doi.org/10.1016/j.ecoenv.2019.02.014" @default.
• W2912898683 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30776561" @default.
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