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Piir, G.; Sild, S.; Roncaglioni, A.; Benfenati, E.; Maran, U. QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects. SAR QSAR Environ. Res. 2010, 21, 7-8, 711-729.

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Piir, G.; Sild, S.; Roncaglioni, A.; Benfenati, E.; Maran, U. QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects. SAR QSAR Environ. Res. 2010, 21, 7-8, 711-729.

QDB archive DOI: 10.15152/QDB.115   DOWNLOAD

QsarDB content

Property LogBCF: Fish bioconcentration factor as logBCF

Tab2: Multilinear model for diverse chemicals

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 310 0.751 0.669
External validation set 1 external validation 156 0.616 0.827
External validation set 2 external validation 161 0.464 1.025
Tab3: Multilinear model for diverse chemicals (Tab3*, statistical outliers (Tab4) removed)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 306 0.776 0.632
External validation set 1 external validation 149 0.726 0.678
External validation set 2 external validation 150 0.675 0.781

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dc.date.accessioned 2014-07-01T10:33:41Z
dc.date.available 2014-07-01T10:33:41Z
dc.date.issued 2014-07-01
dc.identifier.uri http://hdl.handle.net/10967/115
dc.identifier.uri http://dx.doi.org/10.15152/QDB.115
dc.description.abstract The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (logP) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.
dc.publisher Geven Piir
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Piir, G.; Sild, S.; Roncaglioni, A.; Benfenati, E.; Maran, U. QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects. SAR QSAR Environ. Res. 2010, 21, 7-8, 711-729.
qdb.property.endpoint 2. Environmental fate parameters 2.4. Bioconcentration en_US
qdb.descriptor.application ALOGPS 2.1 en_US
qdb.descriptor.application CDK 1.0.1 en_US
qdb.descriptor.application CODESSA PRO 1.0 en_US
qdb.prediction.application CODESSA PRO 1.0 en_US
bibtex.entry article en_US
bibtex.entry.author Piir, G.
bibtex.entry.author Sild, S.
bibtex.entry.author Roncaglioni, A.
bibtex.entry.author Benfenati, E.
bibtex.entry.author Maran, U.
bibtex.entry.doi 10.1080/1062936X.2010.528596 en_US
bibtex.entry.eprint http://www.tandfonline.com/doi/pdf/10.1080/1062936X.2010.528596
bibtex.entry.journal SAR QSAR Environ. Res. en_US
bibtex.entry.note PMID: 21120758
bibtex.entry.number 7-8 en_US
bibtex.entry.pages 711-729 en_US
bibtex.entry.title QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects en_US
bibtex.entry.volume 21 en_US
bibtex.entry.year 2010
qdb.model.type Regression model (regression) en_US


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