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- W3159440372 abstract "Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure–activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC 50 of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R 2 above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC 50 of organic compounds against various fish species is successful. • A SVM model was established for toxicity to various fish species. • Only six descriptors were used in the models for 1121 toxicants. • The optimal SVM model is more accurate than the MLR model. • The optimal SVM model produces satisfactory prediction results." @default.
- W3159440372 created "2021-05-10" @default.
- W3159440372 creator A5017941814 @default.
- W3159440372 date "2021-07-01" @default.
- W3159440372 modified "2023-10-03" @default.
- W3159440372 title "Support vector machine-based model for toxicity of organic compounds against fish" @default.
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- W3159440372 doi "https://doi.org/10.1016/j.yrtph.2021.104942" @default.
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