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Furuhama, A.; Hasunuma, K.; Aoki, Y. Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols. SAR QSAR Environ. Res. 2015, 26, 301–323.

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Furuhama, A.; Hasunuma, K.; Aoki, Y. Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols. SAR QSAR Environ. Res. 2015, 26, 301–323.

QDB archive DOI: 10.15152/QDB.195   DOWNLOAD

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Property pLC50: 96-h acute fish toxicity as log(1/LC50) [-log(mmol/L)]

Compounds: 119 | Models: 1 | Predictions: 2

Eq.3: Model for aromatic amines and phenols (fish) i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 109 0.757 0.485
Validation set external validation 10 0.369 0.760

Property pEC50: 72-h acute algae toxicity as log(1/EC50) [-log(mmol/L)]

Compounds: 114 | Models: 1 | Predictions: 2

Eq.4: Model for aromatic amines and phenols (algae) i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 104 0.753 0.454
Validation set external validation 10 0.531 0.731

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Title: Furuhama, A.; Hasunuma, K.; Aoki, Y. Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols. SAR QSAR Environ. Res. 2015, 26, 301–323.
Abstract: We propose interspecies quantitative structure–activity–activity relationships (QSAARs), that is, QSARs with descriptors, to estimate species-specific acute aquatic toxicity. Using training datasets consisting of more than 100 aromatic amines and phenols, we found that the descriptors that predicted acute toxicities to fish (Oryzias latipes) and algae were daphnia toxicity, molecular weight (an indicator of molecular size and uptake) and selected indicator variables that discriminated between the absence or presence of various substructures. Molecular weight and the selected indicator variables improved the goodness-of-fit of the fish and algae toxicity prediction models. External validations of the QSAARs proved that algae toxicity could be predicted within 1.0 log unit and revealed structural profiles of outlier chemicals with respect to fish toxicity. In addition, applicability domains based on leverage values provided structural alerts for the predicted fish toxicity of chemicals with more than one hydroxyl or amino group attached to an aromatic ring, but not for fluoroanilines, which were not included in the training dataset. Although these simple QSAARs have limitations, their applicability is defined so clearly that they may be practical for screening chemicals with molecular weights of ≤364.9.
URI: http://hdl.handle.net/10967/195
http://dx.doi.org/10.15152/QDB.195
Date: 2017-05-08


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