QSPR modeling bioconcentration factor (BCF) by balance of correlations

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

doi:10.1016/j.ejmech.2009.01.023

Cited by 27 publications

(12 citation statements)

References 35 publications

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“…Generally, SMILES elements represented by means of two or three symbols (e.g., 'Cl', 'Br', '%12', [32][33][34] etc.) can be also used to build up QSAR model, [25][26][27][28][29][30] but SMILES examined in this work have not such elements. BOND, NOSP, and PAIR are global descriptors extracting from SMILES.…”

Section: Descriptorsmentioning

confidence: 97%

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: Descriptorsmentioning

confidence: 97%

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

Toropova

Toropov

Benfenati

et al. 2015

Bioorganic & Medicinal Chemistry

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“…The second model is characterized by n =20, r 2 =0.795, s =0.617 (log K ow >7). The statistical characteristics of the log BCF model from Toropov et al 18. are n =105, r 2 =0.805, s =0.528.…”

Section: Resultsmentioning

confidence: 99%

“…The Bioconcentration Factor ( BCF ) represents an important ecological characteristic of substances which can be considered as industrial pollutants 15. Recently, the CORAL models calculated with SMILES for BCF were examined 16–18. The aim of the present study is to compare the models for BCF calculated with: (i) SMILES, (ii) molecular graph, and (iii) the “hybrid” model which is calculated with representation of the molecular structure by SMILES together with molecular graph 14…”

Section: Introductionmentioning

confidence: 99%

CORAL: Monte Carlo Method as a Tool for the Prediction of the Bioconcentration Factor of Industrial Pollutants

Toropova

Toropov

Martyanov³

et al. 2012

Molecular Informatics

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The CORAL software (http://www.insilico.eu/coral/) has been evaluated for application in QSAR modeling of the bioconcentration factor in fish (logBCF). The data used include 237 organic substances (industrial pollutants). Six random splits of the data into sub-training (30-50 %), calibration (20-30 %), test (13-30 %), and validation sets (7-25 %) have been carried out. The following numbers display the average statistical characteristics of the models for the external validation set: correlation coefficient r(2) =0.880±0.017 and standard error of estimation s=0.559±0.131. The best models were obtained with a combined representation of the molecular structure by SMILES together with hydrogen suppressed graph.

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“…Similarly, based on the predicted activity values of both the training and test sets, values of r m 2 (overall) [27] were calculated. The parameter r m 2 has been used by different groups of authors to check external predictability of QSAR models [28]. …”

Section: Methodsmentioning

confidence: 99%

Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols

Mitra

2011

Sci. Pharm.

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An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico.

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QSPR modeling bioconcentration factor (BCF) by balance of correlations

Cited by 27 publications

References 35 publications

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

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

CORAL: Monte Carlo Method as a Tool for the Prediction of the Bioconcentration Factor of Industrial Pollutants

Chemometric QSAR Modeling and In Silico Design of Antioxidant NO Donor Phenols

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