QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors

Senior, Samir A.; Madbouly, Magdy D.; Massry, Abdel-Moneim El

doi:10.1016/j.chemosphere.2011.06.062

Cited by 17 publications

(3 citation statements)

References 22 publications

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“…This experimental determination becomes even more difficult for the organophosphorus compounds [17][18][19][20]. Many of these compounds are hazardous and they can be difficult to handle in air [21][22][23]. Otherwise, vibrational ZPE can be obtained by computing molecular vibrational frequencies using quantum chemistry methods [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A simple way to predict vibrational zero point energy of organophosphorus (III) compounds

Rahal

Bouabdallah

Malek

et al. 2015

Computational and Theoretical Chemistry

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Section: Introductionmentioning

confidence: 99%

A simple way to predict vibrational zero point energy of organophosphorus (III) compounds

Rahal

Bouabdallah

Malek

et al. 2015

Computational and Theoretical Chemistry

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“…Thiophosphates are a very toxic class of organophosphorus compounds, especially if possess reactive functional groups such as: methyl, phosphate ester (P=O type) and unsubstituted phenyl group [15]. QSTR study of some organophosphorus compounds performed by using the quantum chemical and topological descriptors relieved that the sulphur atoms instead of oxygen atoms improved toxicity [16]. Figure 3b shows a structure of minimum toxic compound (6) imazapyr, an imidazolinone herbicide.…”

Section: Resultsmentioning

confidence: 99%

Free and Open Source Chemistry Software in Research of Quantitative Structure-Toxicity Relationship of Pesticides

et al. 2017

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Abstract. Pesticides are toxic chemicals aimed for the destroying pest on crops. Numerous data evidence about toxicity of pesticides on aquatic organisms. Since pesticides with similar properties tend to have similar biological activities, toxicity may be predicted from structure. Their structure feature and properties are encoded my means of molecular descriptors. Molecular descriptors can capture quite simple twodimensional (2D) chemical structures to highly complex three-dimensional (3D) chemical structures. Quantitative structure-toxicity relationship (QSTR) method uses linear regression analyses for correlation toxicity of chemical with their structural feature using molecular descriptors. Molecular descriptors were calculated using open source software PaDEL and in-house built PyMOL plugin (PyDescriptor). PyDescriptor is a new script implemented with the commonly used visualization software PyMOL for calculation of a large and diverse set of easily interpretable molecular descriptors encoding pharmacophoric patterns and atomic fragments. PyDescriptor has several advantages like free and open source, can work on all major platforms (Windows, Linux, MacOS). QSTR method allows prediction of toxicity of pesticides without experimental assay. In the present work, QSTR analysis for toxicity of a dataset of mixtures of 5 classes of pesticides comprising has been performed.

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“…Senior et al, [29] developed a linear QSTR model based on quantum chemical and topological descriptors of some organophosphorus compounds (OP) and their toxicity LD50 as a dermal. Two descriptors gave higher correlation coefficients and a greater affect on the prediction, namely the Heteroatom Corrected Extended Connectivity Randic index (1XHCEC) and the Density Randic index (1XDen).…”

Section: Quantitative Structure Activity Relat-ionship (Qsar) In Toximentioning

confidence: 99%

In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

Deeb

Goodarzi

2012

Current Drug Safety

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Undesirable toxicity is still a major block in the drug discovery process. Obviously, capable techniques that identify poor effects at a very early stage of product development and provide reasonable toxicity estimates for the huge number of untested compounds are needed. In silico techniques are very useful for this purpose, because of their advantage in reducing time and cost.These case studies give the description of in silico validation techniques and applied modeling methods for the prediction of toxicity of chemical compounds. In silico toxicity prediction techniques can be classified into two categories: Molecular Modeling and methods that derive predictions from experimental data.Molecular modeling is a computational approach to mimic the behavior of molecules, from small molecules (e.g. in conformational analysis) to biomolecules. But the same approaches can also be applied for toxicological purposes, if the mechanism is receptor mediated.Quantitative Structure-Toxicity Relationships (QSTRs) models are typical examples for the prediction of toxicity which relates variations in the molecular structures to toxicity.

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QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors

Cited by 17 publications

References 22 publications

A simple way to predict vibrational zero point energy of organophosphorus (III) compounds

A simple way to predict vibrational zero point energy of organophosphorus (III) compounds

Free and Open Source Chemistry Software in Research of Quantitative Structure-Toxicity Relationship of Pesticides

In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

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