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Cronin, M.T.D.; Netzeva, T.I.; Dearden, J.C.; Edwards, R.; Worgan, A.D.P. Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris :  Development of a Novel Database. Chem. Res. Toxicol. 2004, 17, 545–554.

QsarDB Repository

Cronin, M.T.D.; Netzeva, T.I.; Dearden, J.C.; Edwards, R.; Worgan, A.D.P. Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris :  Development of a Novel Database. Chem. Res. Toxicol. 2004, 17, 545–554.

QDB archive DOI: 10.15152/QDB.162   DOWNLOAD

QsarDB content

Property pEC50: 15-minute algal toxicity as log(1/EC50) [log(1/mM)] i

Compounds: 91 | Models: 6 | Predictions: 8

Eq5: MLR with logKow - non-polar narcosis i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 91 0.756 0.724
Eq6: MLR with logKow and LUMO i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 91 0.868 0.532
Eq7: MLR with logKow, LUMO and ∆1χv i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 91 0.890 0.485
Eq8: MLR with logKow, LUMO and ∆1χv i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 73 0.892 0.485
Validation set external validation 18 0.861 0.559
Fig5: Non-linear kNN model, k = 7 i

k-Nearest neighbors (regression)

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Name Type n

R2

σ

Training set training 91 0.856 0.576
Fig7: Non-linear kNN consensus model, k = 5, 6, 7 i

k-Nearest neighbors ensemble (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 73 0.834 0.625
Validation set external validation 18 0.930 0.408

Citing

When using this data, please cite the original article and this QDB archive:

  • Cronin, M. T. D.; Netzeva, T. I.; Dearden, J. C.; Edwards, R.; Worgan, A. D. P. Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris :  Development of a Novel Database. Chem. Res. Toxicol. 2004, 17, 545–554. http://dx.doi.org/10.1021/tx0342518

  • Ahte, P. QDB archive #162. QsarDB repository, 2015. http://dx.doi.org/10.15152/QDB.162

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Title: Cronin, M.T.D.; Netzeva, T.I.; Dearden, J.C.; Edwards, R.; Worgan, A.D.P. Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris :  Development of a Novel Database. Chem. Res. Toxicol. 2004, 17, 545–554.
Abstract: This study reports a database of toxicity values for 91 compounds assessed in a novel, rapid, and economical 15 min algal toxicity test. The toxicity data were measured using the unicellular green alga Chlorella vulgaris in an assay that determined the disappearance of fluorescein diacetate. The chemicals tested covered a wide range of physicochemical properties and mechanisms of action. Quantitative activity-activity relationships with the toxicity of the chemicals to other species (Tetrahymena pyriformis, Vibrio fischeri, and Pimephales promelas) showed strong relationships, although some differences resulting from different protocols were established. Quantitative structure-activity relationships (QSARs) were determined using linear [multiple linear regression (MLR)] and nonlinear [k-nearest neighbors (KNN)] methods. Three descriptors, accounting for hydrophobicity, electrophilicity, and a function of molecular size corrected for the presence of heteroatoms, were found to be important to model toxicity. The predictivity of MLR was compared to KNN using leave-one-out cross-validation and the simulation of an external test set. MLR demonstrated greater stability in validation. The results of this study showed that method selection in QSAR is task-dependent and it is inappropriate to resort to more complicated but less transparent methods, unless there are clear indications (e.g., inability of MLR to deal with the data set) for the need of such methods.
URI: http://hdl.handle.net/10967/162
http://dx.doi.org/10.15152/QDB.162
Date: 2015-05-28


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