Gramatica, P.; Cassani, S.; Chirico, N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J. Comput. Chem. 2014, 35, 1036–1044.

QsarDB Repository

Gramatica, P.; Cassani, S.; Chirico, N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J. Comput. Chem. 2014, 35, 1036–1044.

QDB archive DOI: 10.15152/QDB.177   DOWNLOAD

QsarDB content

Property M1.pLC50: 96-h Fathead minnow toxicity as log(1/LC50) [-log(mol/L)]

Tab2.Model_1: P. promelas LC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining4490.7500.627

Property M2.logKoc: Soil sorption coefficient as log(Koc) i

Tab2.Model_2: logKoc i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining6430.7940.543

Property M3.GHLI: Global half-life index i

Tab2.Model_3: Global half-life index i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining2500.8480.688

Property M4.logRBA: Estrogen receptor relative binding affinity as log(RBA)

Tab2.Model_4: EDC estrogen receptor binding i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1290.7590.876

Property M5.logHLn: Biotransformation half-lives in fish

Tab2.Model_5: Metab. biotransf. fish i

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining6320.7490.581

Property M6.pLC50: Fathead minnow toxicity as log(1/LC50) [-log(mmol/L)]

Tab2.Model_6: Esters P. promelas LC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining300.8830.262

Property M7.pEC50: Daphnia toxicity as log(1/LC50) [-log(mmol/L)]

Tab2.Model_7: Esters D. magna EC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining290.8630.394

Property M8.logTA100: The mutagenicity potency in TA100 (without the S9 activation system) as log(TA100)

Tab2.Model_8: NitroPAH TA100 without S9 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining480.8280.719

Property M9.logKoa: Octanol-air partition coefficient as log(Koa)

Tab2.Model_9: BFR logKoa i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining300.9670.258

Property M10.MP: Melting Point [°C]

Tab2.Model_10: BFR melting point i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining250.81520.993

Property M11.pPL: Subcooled liquid vapour pressure as log(1/PL) [-log(Pa)] i

Tab2.Model_11: BFR vapor pressure i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining340.9850.171

Property M12.pLC50: Acute inhalation toxicity in mice as log(1/LC50) [-log(mmol/m^3)] i

Tab2.Model_12: PFC inal. mouse LC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining560.7860.711

Property M13.pLC50: Acute inhalation toxicity in rat as log(1/LC50) [-log(mmol/m^3)] i

Tab2.Model_13: PFC inal. rat LC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining520.7870.785

Property M14.pLD50: Acute oral toxicity in rat as log(1/LD50) [-log(mmol/kg)] i

Tab2.Model_14: PFC oral rat LD50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining500.8900.410

Property M15.logSw: Water solubility as log(Sw) [log(mg/L)]

Tab2.Model_15: PFC solubility in water i

Regression model (regression)   QMRF

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NameTypen

R2

σ

Training settraining200.8510.686

Property M16.logVP: Vapor pressure as log(VP) [log(mm Hg)]

Tab2.Model_16: PFC vapor pressure i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining350.9310.733

Property M17.logKow: The octanol-water partition coefficient as log(Kow)

Tab2.Model_17: (B)TAZ logKow i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining640.8590.665

Property M18.MP: Melting point [°C]

Tab2.Model_18: (B)TAZ melting point i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining560.81027.386

Property M19.logSw: Water solubility as log(Sw) [log(mg/L)]

Tab2.Model_19: (B)TAZ solubility in water i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining490.8310.512

Property M20.logVP: Vapor pressure as log(VP) [log(mm Hg)]

Tab2.Model_20: (B)TAZ vapor pressure i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining330.7980.801

Property M21.pEC50: 72-h Algal toxicity as log(1/EC50) [-log(mol/L)]

Tab2.Model_21: (B)TAZ P. subcapitata EC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining350.8210.425

Property M22.pEC50: Daphnia toxicity as log(1/EC50) [-log(mol/L)]

Tab2.Model_22: (B)TAZ D.magna EC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining970.7290.420

Property M23.pLC50: 96-h Rainbow trout toxicity as log(1/LC50) [-log(mol/L)]

Tab2.Model_23: (B)TAZ O. mykiss LC50 i

Regression model (regression)   QMRF

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining750.7590.518

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Piir, G. Data for: QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. QsarDB repository, QDB.177. 2016. http://dx.doi.org/10.15152/QDB.177

  • Gramatica, P.; Cassani, S.; Chirico, N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J. Comput. Chem. 2014, 35, 1036–1044. http://dx.doi.org/10.1002/jcc.23576

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dc.date.accessioned2016-02-26T10:05:38Z
dc.date.available2016-02-26T10:05:38Z
dc.date.issued2016-02-26
dc.identifier.urihttp://hdl.handle.net/10967/177
dc.identifier.urihttp://dx.doi.org/10.15152/QDB.177
dc.description.abstractA database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.
dc.publisherGeven Piir
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleGramatica, P.; Cassani, S.; Chirico, N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J. Comput. Chem. 2014, 35, 1036–1044.
qdb.property.endpoint1. Physical Chemical Properties 1.1. Melting pointen_US
qdb.property.endpoint1. Physical Chemical Properties 1.3. Water solubilityen_US
qdb.property.endpoint1. Physical Chemical Properties 1.4. Vapour pressureen_US
qdb.property.endpoint1. Physical Chemical Properties 1.6. Octanol-water partition coefficient (Kow)en_US
qdb.property.endpoint1. Physical Chemical Properties 1.8. Octanol-air partition coefficient (Koa)en_US
qdb.property.endpoint2. Environmental fate parameters 2.6. Partition coefficient. Organic carbon-sorption partition coefficient.en_US
qdb.property.endpoint3. Ecotoxic effects 3.1. Short-term toxicity to Daphniaen_US
qdb.property.endpoint3. Ecotoxic effects 3.2. Short-term toxicity to algaeen_US
qdb.property.endpoint3. Ecotoxic effects 3.3. Acute toxicity to fishen_US
qdb.property.endpoint4. Human health effects 4.1. Acute Inhalation toxicityen_US
qdb.property.endpoint4. Human health effects 4.2. Acute Oral toxicityen_US
qdb.property.endpoint4. Human health effects 4.10. Mutagenicityen_US
qdb.property.endpoint4. Human health effects 4.18. Endocrine Activityen_US
qdb.property.endpoint6. Other (Global half-life index)en_US
qdb.property.endpoint6. Other (Biotransformation half-lives in fish)
qdb.property.speciesPimephales promelas (Fathead minnow)en_US
qdb.property.speciesRattus norvegicus (Rat)en_US
qdb.property.speciesDaphnia magnaen_US
qdb.property.speciesSalmonella typhimuriumen_US
qdb.property.speciesMus musculus (Mouse)en_US
qdb.property.speciesPseudokirchneriella subcapitataen_US
qdb.property.speciesOncorhynchus mykiss (Rainbow trout)en_US
qdb.descriptor.applicationPaDEL-Descriptor 2.18en_US
qdb.prediction.applicationQSARINS 1.2en_US
bibtex.entryarticleen_US
bibtex.entry.authorGramatica, P.
bibtex.entry.authorCassani, S.
bibtex.entry.authorChirico, N.
bibtex.entry.doi10.1002/jcc.23576en_US
bibtex.entry.journalJ. Comput. Chem.en_US
bibtex.entry.monthmar
bibtex.entry.number13en_US
bibtex.entry.pages1036–1044en_US
bibtex.entry.titleQSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINSen_US
bibtex.entry.volume35en_US
bibtex.entry.year2014
qdb.model.typeRegression model (regression)en_US
qdb.model.qmrfTab2.Model_1=Q15-33-0013
qdb.model.qmrfTab2.Model_2=Q17-26-0032
qdb.model.qmrfTab2.Model_3=Q15-66-0018
qdb.model.qmrfTab2.Model_12=Q15-41-0014
qdb.model.qmrfTab2.Model_21=Q15-32-0015
qdb.model.qmrfTab2.Model_22=Q15-31-0011
qdb.model.qmrfTab2.Model_23=Q15-33-0012
qdb.model.qmrfTab2.Model_4
qdb.model.qmrfTab2.Model_6
qdb.model.qmrfTab2.Model_7
qdb.model.qmrfTab2.Model_8
qdb.model.qmrfTab2.Model_9
qdb.model.qmrfTab2.Model_10
qdb.model.qmrfTab2.Model_11
qdb.model.qmrfTab2.Model_13
qdb.model.qmrfTab2.Model_14
qdb.model.qmrfTab2.Model_15
qdb.model.qmrfTab2.Model_16
qdb.model.qmrfTab2.Model_17
qdb.model.qmrfTab2.Model_18
qdb.model.qmrfTab2.Model_19
qdb.model.qmrfTab2.Model_20


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2014JCC1036.qdb.zipModels from QSARINS softwareapplication/zip605.2KbView/Open
Q15-33-0013.pdfQMRFPDF44.82KbView/Open
Q17-26-0032.pdfQMRFPDF45.82KbView/Open
Q15-66-0018.pdfQMRFPDF45.28KbView/Open
Q15-41-0014.pdfQMRFPDF43.53KbView/Open
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Q15-31-0011.pdfQMRFPDF45.17KbView/Open
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Tab2.Model_4.pdfQMRFPDF50.80KbView/Open
Tab2.Model_6.pdfQMRFPDF49.19KbView/Open
Tab2.Model_7.pdfQMRFPDF50.49KbView/Open
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