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Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method
AP. Toropova, AA. Toropov, M. Raskova, I. Raska,
Jazyk angličtina Země Nizozemsko
Typ dokumentu časopisecké články
- MeSH
- biologické modely * MeSH
- chemické látky znečišťující vodu chemie toxicita MeSH
- Cyprinidae růst a vývoj metabolismus MeSH
- kvantitativní vztahy mezi strukturou a aktivitou * MeSH
- LD50 MeSH
- metoda Monte Carlo * MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r(2)=0.7630, RMSE=0.654 (training set); n=114, r(2)=0.7024, RMSE=0.766 (calibration set); n=226, r(2)=0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed.
Citace poskytuje Crossref.org
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- $a By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r(2)=0.7630, RMSE=0.654 (training set); n=114, r(2)=0.7024, RMSE=0.766 (calibration set); n=226, r(2)=0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed.
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