QSAR modelling toxicity toward rats of inorganic substances by means of CORAL

Toropova, Alla P.; Toropov, Andrey A.; Benfenati, Emilio; Gini, Giuseppina

doi:10.2478/s11532-010-0116-x

Cited by 19 publications

(13 citation statements)

References 37 publications

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“…37 There are QSAR models for toxicity in rat described in the literature. 36,[39][40][41][42] The comparison of statistical quality of models for toxicity in rat which are calculated with Eqs. 5-14 and models described in 36 indicates a paradoxical situation: correlation coefficients for Eqs.…”

Section: Resultsmentioning

confidence: 99%

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QSAR model as a random event: A case of rat toxicity

Toropova

Toropov

Benfenati

et al. 2015

Bioorganic & Medicinal Chemistry

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Section: Resultsmentioning

confidence: 99%

“…QSAR model for toxicity in rat of inorganic substances is characterized by n = 10, r 2 = 0.80. 41 The comparison of models which are calculated by means of Eqs. 5-14 with the above-mentioned QSAR models taken in the literature 36,[39][40][41] indicates that suggested approach can be useful for QSAR analysis of toxicity in rat.…”

Section: Resultsmentioning

confidence: 99%

QSAR model as a random event: A case of rat toxicity

Toropova

Toropov

Benfenati

et al. 2015

Bioorganic & Medicinal Chemistry

Self Cite

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“…'[Subtraining -Calibration -Test] system; and (3) balance of correlations with ideal slopes (BCIS). The Monte Carlo optimisation utilising the three methods is described in references [14][15][16][17][18][19][20] and accordingly is not depicted here.…”

Section: Descriptors and Methods Of Monte Carlo Optimisationmentioning

confidence: 99%

Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1)

Begum

Achary

2015

SAR and QSAR in Environmental Research

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Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.

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“…[12][13][14][15][16][17] The simplified molecular input line entry system (SMILES) is the representation of the molecular structure for the CORAL software which is building up QSPR/QSAR models by the Monte Carlo technique. [17] The aim of this study is evaluation of the predictive potential of QSPR models for the logK OH values for the series of reactions.…”

Section: Introductionmentioning

confidence: 99%

Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

et al. 2012

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The rate constants (K OH ) of reactions between 78 organic aromatic pollutants and hydroxyl radical were examined. Simplified molecular input line entry system was used as representation of the molecular structure of the pollutants. Quantitative structure-property relationships was developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the subtraining, calibration, and test sets. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r 2 ) for the test sets of the four random splits are 0.75, 0.91, 0.84, and 0.80. Using the Monte Carlo method CORAL software generated the optimal descriptors for one-variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The current data were compared to previous results and discussed.

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QSAR modelling toxicity toward rats of inorganic substances by means of CORAL

Abstract: Abstract:© Versita Sp. z o.o. =0.7815, s=0.240, F=32 (validation set).

Cited by 19 publications

References 37 publications

QSAR model as a random event: A case of rat toxicity

QSAR model as a random event: A case of rat toxicity

Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1)

Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

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