Abstract
Structure-Toxicity Relationships have been studied for a set of 42 organophosphorous pesticides (OPs) through multiple linear regression (MLR) and artificial neural networks (ANN). A model with three descriptors, including: total lipophilicity [log (P)], widths radicals R1 [(LR1)] and R2 [(LR2)] has achieved good results in phase Training and phase prediction of toxicity [log LD50 (lethal dose 50, Oral rat)]. The linear model (MLR: n=40, r²=0.86, s=40 and q2 = 0.66) and non-linear model with a configuration [3-6-1] (ANN: r²=0.95, s=0.73 and q2 = 0.17) have proved very successful and complementary. The selected descriptors indicate the importance of lipophilicity and widths radicals R1 and R2 in the contribution of the toxicity of pesticides derived from OPs used in this study. This information is relevant for the design of a new model of non-toxic pesticides OPs.
Highlights
The pesticide is a limit generic for a variety of classes such as chemical herbicides, fungicides and nematicides
Establishment of multiple linear regression (MLR) models Given the large number of 63 descriptors used to code each molecule, we subjected our data to Stepwise stepwise selection [14, 15, 16], in order to highlight the most relevant descriptors
To show that our model is not due to chance, we applied the experiment of changing the column of the dependent variable randomly so that each molecule does not find its true activity but the activity of a another molecule, without touching the columns of the independent variables. The result of this test on the sample of 42 molecules shows that the statistical quality of our model decreases very remarkably, it goes from r = 0.86, s = 0.40 and F = 32.18 at r = 0.46, s = 0.60 and F = 0.07. This result clearly indicates that the descriptors selected for this study describe well the activity of the series of organophosphorus compounds
Summary
The pesticide is a limit generic for a variety of classes such as chemical herbicides, fungicides and nematicides. The objectives of our study are to provide additional information on the behaviour of organophosphorus compounds and set in the future the necessary criteria for designing a model for a new generation of organophosphorous pesticides. The use of quantitative relationships structures QSAR activities currently has considerable attention [1] for pharmaceutical needs [2], as well as the study of the toxicological mechanisms of chemical environmental pollutants (products Endocrine disrupting phytochemicals) [3]. A large number of families of compounds have already been the subject of such research.
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