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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)]

Compounds: 449 | Models: 1 | Predictions: 1

Tab2.Model_1: P. promelas LC50 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 449 0.750 0.627

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

Compounds: 643 | Models: 1 | Predictions: 1

Tab2.Model_2: logKoc i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 643 0.794 0.543

Property M3.GHLI: Global half-life index i

Compounds: 250 | Models: 1 | Predictions: 1

Tab2.Model_3: Global half-life index i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 250 0.848 0.688

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

Compounds: 129 | Models: 1 | Predictions: 1

Tab2.Model_4: EDC estrogen receptor binding i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 129 0.759 0.876

Property M5.logHLn: Biotransformation half-lives in fish

Compounds: 632 | Models: 1 | Predictions: 1

Tab2.Model_5: Metab. biotransf. fish i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 632 0.749 0.581

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

Compounds: 30 | Models: 1 | Predictions: 1

Tab2.Model_6: Esters P. promelas LC50 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 30 0.883 0.262

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

Compounds: 29 | Models: 1 | Predictions: 1

Tab2.Model_7: Esters D. magna EC50 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 29 0.863 0.394

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

Compounds: 48 | Models: 1 | Predictions: 1

Tab2.Model_8: NitroPAH TA100 without S9 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 48 0.828 0.719

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

Compounds: 30 | Models: 1 | Predictions: 1

Tab2.Model_9: BFR logKoa i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 30 0.967 0.258

Property M10.MP: Melting Point [°C]

Compounds: 25 | Models: 1 | Predictions: 1

Tab2.Model_10: BFR melting point i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 25 0.815 20.993

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

Compounds: 34 | Models: 1 | Predictions: 1

Tab2.Model_11: BFR vapor pressure i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 34 0.985 0.171

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

Compounds: 56 | Models: 1 | Predictions: 1

Tab2.Model_12: PFC inal. mouse LC50 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 56 0.786 0.711

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

Compounds: 52 | Models: 1 | Predictions: 1

Tab2.Model_13: PFC inal. rat LC50 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 52 0.787 0.785

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

Compounds: 50 | Models: 1 | Predictions: 1

Tab2.Model_14: PFC oral rat LD50 i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 50 0.890 0.410

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

Compounds: 20 | Models: 1 | Predictions: 1

Tab2.Model_15: PFC solubility in water i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 20 0.851 0.686

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

Compounds: 35 | Models: 1 | Predictions: 1

Tab2.Model_16: PFC vapor pressure i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 35 0.931 0.733

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

Compounds: 64 | Models: 1 | Predictions: 1

Tab2.Model_17: (B)TAZ logKow i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 64 0.859 0.665

Property M18.MP: Melting point [°C ]

Compounds: 56 | Models: 1 | Predictions: 1

Tab2.Model_18: (B)TAZ melting point i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 56 0.810 27.386

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

Compounds: 49 | Models: 1 | Predictions: 1

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 49 0.831 0.512

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

Compounds: 33 | Models: 1 | Predictions: 1

Tab2.Model_20: (B)TAZ vapor pressure i

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 33 0.798 0.801

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

Compounds: 35 | Models: 1 | Predictions: 1

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 35 0.821 0.425

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

Compounds: 97 | Models: 1 | Predictions: 1

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 97 0.729 0.420

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

Compounds: 75 | Models: 1 | Predictions: 1

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 75 0.759 0.518

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Title: 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.
Abstract: A 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.
URI: http://hdl.handle.net/10967/177
http://dx.doi.org/10.15152/QDB.177
Date: 2016-02-26


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2014JCC1036.qdb.zip Models from QSARINS software application/x-zip 605.2Kb View/Open
Tab2.Model_1.pdf QMRF PDF 95.02Kb View/Open
Tab2.Model_2.pdf QMRF PDF 93.36Kb View/Open
Tab2.Model_3.pdf QMRF PDF 92.47Kb View/Open
Tab2.Model_12.pdf QMRF PDF 93.97Kb View/Open
Tab2.Model_21.pdf QMRF PDF 96.32Kb View/Open
Tab2.Model_22.pdf QMRF PDF 96.64Kb View/Open
Tab2.Model_23.pdf QMRF PDF 97.54Kb View/Open
Tab2.Model_4.pdf QMRF PDF 50.80Kb View/Open
Tab2.Model_6.pdf QMRF PDF 49.19Kb View/Open
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Tab2.Model_8.pdf QMRF PDF 51.56Kb View/Open
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Tab2.Model_13.pdf QMRF PDF 52.66Kb View/Open
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