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Roy, P. P.; Kovarich, S.; Gramatica, P. QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. Journal of Computational Chemistry 2011, 32, 2386–2396.

QsarDB Repository

Roy, P. P.; Kovarich, S.; Gramatica, P. QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. Journal of Computational Chemistry 2011, 32, 2386–2396.

QDB archive DOI: 10.15152/QDB.183   DOWNLOAD

QsarDB content

Property pk(OH): Degradation by OH radicals as -logk(OH) [log(s/cm3)] i

Compounds: 460 | Models: 8 | Predictions: 14

Eq.(i): Full model, descriptors from DRAGON, Q47-19-49-478 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 460 0.824 0.428
Tab1.D1: Model with descripors from DRAGON, split by K-ANN, Q47-19-49-478 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 191 0.867 0.358
Validation set external validation 269 0.796 0.474
Tab1.D2: Model with descripors from DRAGON, split by Random selection by response, Q47-19-49-478 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 230 0.826 0.421
Validation set external validation 230 0.820 0.439
Tab1.D3: Model with descripors from DRAGON, split by K-means clustering, Q47-19-49-478 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 230 0.836 0.432
Validation set external validation 230 0.809 0.431
Eq.(ii): Full model, descriptors from QSPR-THESAURUS, Q47-19-49-479 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 460 0.806 0.449
Tab1.O1: Model with descriptors from QSPR-THESAURUS, split by K-ANN, Q47-19-49-479 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 191 0.847 0.384
Validation set external validation 269 0.778 0.493
Tab1.O2: Model with descriptors from QSPR-THESAURUS, split by Random selection by response, Q47-19-49-479 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 230 0.814 0.436
Validation set external validation 230 0.797 0.467
Tab1.O3: Model with descriptors from QSPR-THESAURUS, split by K-means clustering, Q47-19-49-479 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 230 0.813 0.461
Validation set external validation 230 0.799 0.440

Citing

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

  • Roy, P. P.; Kovarich, S.; Gramatica, P. QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. J. Comput. Chem. 2011, 32, 2386–2396. http://dx.doi.org/10.1002/jcc.21820

  • Kahn, I. QDB archive #183. QsarDB repository, 2016. http://dx.doi.org/10.15152/QDB.183

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Title: Roy, P. P.; Kovarich, S.; Gramatica, P. QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. Journal of Computational Chemistry 2011, 32, 2386–2396.
Abstract: The crucial importance of the three central OECD principles for quantitative structure-activity relationship (QSAR) model validation is highlighted in a case study of tropospheric degradation of volatile organic compounds (VOCs) by OH, applied to two CADASTER chemical classes (PBDEs and (benzo-)triazoles). The application of any QSAR model to chemicals without experimental data largely depends on model reproducibility by the user. The reproducibility of an unambiguous algorithm (OECD Principle 2) is guaranteed by redeveloping MLR models based on both updated version of DRAGON software for molecular descriptors calculation and some freely available online descriptors. The Genetic Algorithm has confirmed its ability to always select the most informative descriptors independently on the input pool of variables. The ability of the GA-selected descriptors to model chemicals not used in model development is verified by three different splittings (random by response, K-ANN and K-means clustering), thus ensuring the external predictivity of the new models, independently of the training/prediction set composition (OECD Principle 5). The relevance of checking the structural applicability domain becomes very evident on comparing the predictions for CADASTER chemicals, using the new models proposed herein, with those obtained by EPI Suite.
URI: http://hdl.handle.net/10967/183
http://dx.doi.org/10.15152/QDB.183
Date: 2016-09-05


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