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

Toropova, Alla P.; Toropov, Andrey A.; Martyanov, S.E.; Benfenati, Emilio; Gini, Giuseppina; Leszczyńska, Danuta; Leszczyński, Jerzy

doi:10.1002/minf.201200069

Cited by 23 publications

(13 citation statements)

References 30 publications

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“…In this work, to achieve a paramount and consistent depiction of the system, the available data were randomly split three times into four sets, training (≈34 %), calibration (≈34 %), test (≈16 %) and validation (≈16 %), and examined. The training set plays the role of builder of a model; the calibration set plays the role of preliminary critic of the model; the test is a visible estimator of the model, while the validation set is the invisible final estimator of the model …”

Section: Resultsmentioning

confidence: 99%

“…In addition to the previous descriptors, CORAL allows combining SMILES and molecular graphs to create a hybrid descriptor. Hybrid representations using SMILES with the molecular graph may give better models with higher statistical qualities than those models with a unique representation of the molecular structure …”

Section: Resultsmentioning

confidence: 99%

“…The threshold and

N_{normale normalp normalo normalc normalh}

were calculated according to a scheme suggested by Toropova et al . The range of the threshold was 1–5; the range of

N_{normale normalp normalo normalc normalh}

was 1–100.…”

Section: Resultsmentioning

confidence: 99%

“…In other words, the calibration set is the equivalent to the test set in the case of as ingle split. [17b, 21a, 24] The threshold and N epoch were calculated according to a scheme suggested by Toropova et al [20,25] The range of the threshold was 1-5;t he range of N epoch was 1-100. Finally,t he correlation weights are all determined and the coefficients C 0 and C 1 are also given.…”

Section: Ho-1 Qsardescriptorsmentioning

confidence: 99%

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Heme Oxygenase Database (HemeOxDB) and QSAR Analysis of Isoform 1 Inhibitors

et al. 2017

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Due to increasing interest in the field of heme oxygenases (HOs), we built a ligand database called HemeOxDB that includes the entire set of known HO-1 and HO-2 inhibitors, resulting in more than 400 compounds. The HemeOxDB is available online at http://www.researchdsf.unict.it/hemeoxdb/, and having a robust search engine allows end users to build complex queries, sort tabulated results, and generate color-coded two- and three-dimensional graphs. This database will grow to be a tool for the design of potent and selective HO-1 or HO-2 inhibitors. We were also interested in virtually searching for alternative inhibitors, and, for the first time in the field of HOs, a quantitative structure-activity relationship (QSAR) model was built using half-maximal inhibitory concentration (IC ) values of the whole set of known HO-1 inhibitors, taken from the HemeOxDB and employing the Monte Carlo technique. The statistical quality suggested that the model is robust and possesses desirable predictive potential. The screening of US Food and Drug Administration (FDA)-approved drugs, external to our dataset, suggested new predicted inhibitors, opening the way for replacing imidazole groups. The HemeOxDB and the QSAR model reported herein may help in prospectively identifying or repurposing new drugs with optimal structural attributes for HO enzyme inhibition.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The threshold and

N_{normale normalp normalo normalc normalh}

were calculated according to a scheme suggested by Toropova et al . The range of the threshold was 1–5; the range of

N_{normale normalp normalo normalc normalh}

was 1–100.…”

Section: Resultsmentioning

confidence: 99%

Section: Ho-1 Qsardescriptorsmentioning

confidence: 99%

See 2 more Smart Citations

Heme Oxygenase Database (HemeOxDB) and QSAR Analysis of Isoform 1 Inhibitors

et al. 2017

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“…These representations also can be involved into hybrid version of the optimal descriptor where molecular features extracted from e.g. GAO and SMILES play the role of hybrid basis for a QSPR/QSAR predictions [27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Development of the Latest Tools for Building up “Nano-QSAR”: Quantitative Features—Property/Activity Relationships (QFPRs/QFARs)

Toropov

Toropova

Nesměrák

et al. 2016

Practical Aspects of Computational Chemistry IV

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Computational studies of common compounds are already standard ways of their investigations. However, modeling properties of nanomaterials has been always a challenging task. This chapter reveals important differences between approaches applied to these two groups of species. The development of an optimal descriptor provides one of the efficient ways for the computational techniques to estimate endpoints related to nanospecies. Notably, the optimal descriptor can represent a translator of eclectic information into the endpoint prediction. Development of the optimal descriptor could start with consideration of a hybrid of topological indices calculated with the adjacency matrix of the molecular graph and application of additive scheme where a physicochemical parameter is modeled as the summation of contributions of molecular fragments. Further, the optimal descriptor might be advanced by taking into account contributions of various

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The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor

Toropova

Duchowicz

Saavedra

et al. 2020

Molecular Informatics

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We establish a QSPR analysis for the bioconcentration factor of 851 heterogeneous structural compounds. Linear models are proposed via two different approaches: i. the optimal descriptor method implemented in CORAL, and ii. multivariable linear regressions on the best molecular descriptors found with the Replacement Method on 44,216 structural descriptors. Such variables are derived with different freely available softwares, such as PaDEL, PyDescriptor, Mold2, QuBiLs‐MAS and ISIDA/Fragmentor. The same validation set is employed in order to compare the predictive performance between the so‐obtained CORAL and RM based models. Finally, the comparison of several models for the bioconcentration factor confirms the ability of the so‐called index of ideality of correlation to be a criterion of predictive potential in Quantitative Structure‐Property Relationships.

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

Cited by 23 publications

References 30 publications

Heme Oxygenase Database (HemeOxDB) and QSAR Analysis of Isoform 1 Inhibitors

Heme Oxygenase Database (HemeOxDB) and QSAR Analysis of Isoform 1 Inhibitors

Development of the Latest Tools for Building up “Nano-QSAR”: Quantitative Features—Property/Activity Relationships (QFPRs/QFARs)

The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor

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