AUTHORS: Vesna Rastija, Dejan Agić, Kristian Brlas, Vijay Masand
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ABSTRACT: Pesticides are toxic chemicals aimed for the destroying pest on crops. 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 two-dimensional (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. A good number of molecular descriptors were calculated followed by extensive objective and subjective feature selection to avoid redundant descriptors. For model building, the dataset was divided into training (80%) and test (20%) sets. A QSAR model built using three easily interpretable descriptors was subjected to extensive internal and external validation. The QSAR model is statistically robust with R2 = 0.872, Q2 = 0.844, CCCex = 0.845. The analysis revealed that lipophilicity, frequency of occurrence of hydrogen within 3 Å from phosphorus, and the presence of two benzene rings with –CH2– group as linker have good correlation with the toxicity of the pesticides.
KEYWORDS: pesticides, toxicity, molecular descriptors, free software, plugin, regression analyses, lipophilicity
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