The determination of toxicities of sulphonylurea and phenylurea herbicides with quantitative structure–toxicity relationship (QSTR) studies

Can, Alper; Yildiz, İ̇lkay; Güvendik, Gülin

doi:10.1016/j.etap.2013.02.001

Cited by 18 publications

(12 citation statements)

References 9 publications

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“…Can et al [69] used a single parameter (R 2 ) while Devillers [71] and Gough et al [67] have used the root mean square error (RMS). Thus, the comparison was limited to the results obtained for statistics of the internal validation.…”

Section: Comparison With Different Modelsmentioning

confidence: 99%

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Artificial neural network-based equation to predict the toxicity of herbicides on rats

Hamadache

Hanini

Benkortbi

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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The use of herbicides is increasing around the world. The benefits achieved by the use of these herbicides are indisputable. Despite their importance in agriculture, herbicides can be dangerous to the environment and the human health, depending on their toxicity, and the degree of contamination. Also, it is essential and evident that the risk assessment of herbicides is an important task in the environmental protection. The objective of this work was to investigate and implement an Artificial Neural Network (ANN) model for the prediction of acute oral toxicity of 77 herbicides to rats. Internal and external validations of the model showed high 2 and 2 ̅̅̅ values, in the range 0.782 -0.997 for the training and the test. In addition, the major contribution of the current work was to develop artificial neural network-based equation to predict the toxicity of 13 other herbicides; the mathematical equation using the weights of the network gave very significant results, leading to an R 2 value of 0.959. The agreement between calculated and experimental values of acute toxicity confirmed the ability of ANN-based equation to predict the toxicity for herbicides that have not been tested as well as new herbicides.

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Section: Comparison With Different Modelsmentioning

confidence: 99%

“…In addition, it should be noted that the most of these QSAR models were obtained with structurally similar chemicals such as amide herbicides [67,68], sulphonylurea and phenylurea herbicides [69,70] or organophosphorus pesticides [71].…”

Section: Comparison With Different Modelsmentioning

confidence: 99%

Artificial neural network-based equation to predict the toxicity of herbicides on rats

Hamadache

Hanini

Benkortbi

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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“…19,20 These herbicides exert their action either by diverting the flow of electron in photosynthesis from its normal pathway or inhibit acetolactose-synthatase enzyme. Sulphonylureas, phenylureas and chloroacetanilides represent important families of aniline based herbicides which have registered their potency on commercial scale.…”

Section: Introductionmentioning

confidence: 99%

pH and temperature responsive redox behavior of biologically important aniline derivatives

et al. 2015

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“…Values <i 1 ,i 2 ,i 3 > are repeated as periodic law (PL) states. Order relations (10) should repeat at intervals equal to period size and are equivalent to: (11) As relations (11) are valid only for minima, others more general are desired for all p values. The differences D(p) = P(p+1)-P(p) are calculated assigning every value to phenylurea p: (12) Instead of D(p), R(p) = P(p+1)/P(p) can be taken allocating them to urea p. If PL were general, elements in the same group in analogous positions in different periods would satisfy: either (13) and either (14) However, results show that this is not the case so that PL is not general existing some anomalies; e.g., D(p) variation vs. group number (cf.…”

mentioning

confidence: 99%

“…2-6). Can et al (2013) determined sulphonylurea and phenylurea-herbicide toxicities with quantitative structure-toxicity relationships (QSTRs). …”

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confidence: 99%

Molecular Clustering of Phenylurea Herbicides: Comparison with Sulphonylureas, Pesticides and Persistent Organic Pollutants

Torrens

Castellano

2014

ETET

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ABSTRACT. Chromatographic retention times of phenylurea herbicides are modelled by structure-property relationships. Properties are hydration free energy and dipole. Bioplastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among evolutionary indeterminacy, morphological determination and natural selection principles. Classification algorithms are proposed based on information entropy and production. Phenylureas are classified by Cl2, O2 and N2 presence; their different behaviour depends on the number of Cl atoms. When applying procedures to moderate-sized sets, excessive results appear compatible with data and suffer a combinatorial explosion; however, the equipartition conjecture allows a selection criterion resulting from classification between hierarchical trees. Information entropy permits classifying compounds and agrees with principal component analyses. Phenylureas periodic table shows that those in the same group present similar properties; phenylureas also in the same period, maximum resemblance. Classification extends to phenylureas, sulphonylureas, pesticides and persistent organic pollutants.

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The determination of toxicities of sulphonylurea and phenylurea herbicides with quantitative structure–toxicity relationship (QSTR) studies

Cited by 18 publications

References 9 publications

Artificial neural network-based equation to predict the toxicity of herbicides on rats

Artificial neural network-based equation to predict the toxicity of herbicides on rats

pH and temperature responsive redox behavior of biologically important aniline derivatives

Molecular Clustering of Phenylurea Herbicides: Comparison with Sulphonylureas, Pesticides and Persistent Organic Pollutants

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