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Gramatica, P.; Cassani, S.; Roy, P. P.; Kovarich, S.; Yap, C. W.; Papa, E. QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae. Molecular Informatics 2012, 31, 817–835.

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Gramatica, P.; Cassani, S.; Roy, P. P.; Kovarich, S.; Yap, C. W.; Papa, E. QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae. Molecular Informatics 2012, 31, 817–835.

QDB archive DOI: 10.15152/QDB.182   DOWNLOAD

QsarDB content

Property pEC50: 72-h Algal toxicity as log(1/EC50) [-log(mol/L)] i

Compounds: 35 | Models: 4 | Predictions: 12

Tab2.1: Model with descriptors from DRAGON i

Regression model (regression)

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Name Type n

R2

σ

Training set i training 35 0.824 0.421
Testing set (inside AD) i testing 344 N/A N/A
Testing set (outside AD) testing 25 N/A N/A
Tab2.2: Model with descriptors from PaDEL i

Regression model (regression)

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Name Type n

R2

σ

Training set i training 35 0.820 0.425
Testing set (inside AD) testing 344 N/A N/A
Testing set (outside AD) testing 25 N/A N/A
Tab2.3: Model from QSPR-THESAURUS i

Regression model (regression)

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Name Type n

R2

σ

Training set i training 35 0.804 0.445
Testing set (inside AD) testing 328 N/A N/A
Testing set (outside AD) testing 41 N/A N/A
Tab2.4: CONSENSUS model i

Regression model ensemble (regression)

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Name Type n

R2

σ

Training set i training 35 0.865 0.373
Testing set (inside AD) testing 337 N/A N/A
Testing set (outside AD) testing 32 N/A N/A

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Title: Gramatica, P.; Cassani, S.; Roy, P. P.; Kovarich, S.; Yap, C. W.; Papa, E. QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae. Molecular Informatics 2012, 31, 817–835.
Abstract: A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.
URI: http://hdl.handle.net/10967/182
http://dx.doi.org/10.15152/QDB.182
Date: 2016-08-29


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    Uni. Insubria (Italy), QSAR Research Unit in Environmental Chemistry and Ecotoxicology

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