WSEAS Transactions on Biology and Biomedicine

Print ISSN: 1109-9518
E-ISSN: 2224-2902

Volume 14, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Application a New PyMOL Plugin in Quantitative Structure-Toxicity Relationship Study of Pesticides

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


[1] W. Battaglin, J. Fairchild, Potential toxicity of pesticides measured in midwestern streams to aquatic organisms, Water Science and Technology, Vol.45, No.9, 2002, pp. 95-102.

[2] S. Cassani, S. Kovarich, E. Papa, P. P. Roy, M. Rahmberg, S, Nilsson, U. Sahlin, N. Jeliazkova, N. Kochev, O. Pukalov, I. Tetko, S. Brandmaier, M. K. Durjava, B. Kolar, W. Peijnenburg, P. Gramatica, Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction, ATLA, Vol. 41, 2013, pp. 49-64.

[3] R. Todeschini, V. Consonni, Molecular Descriptors for Chemoinformatics, WileyVCH: Weinheim2009.

[4] EC Proposal for a Regulation of the European Parliament and of the Council concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency and amending Directive 1999/45/EC and Regulation (EC) on Persistent Organic Pollutants (Brussels, Belgium), 2003.

[5] S. Kar, K. Roy, QSAR Modeling of Toxicity of Diverse Organic Chemicals to Daphnia magna Using 2D and 3D Descriptors, Journal of Hazardous Materials, Vol.177, 2010, pp. 344- 351.

[6] D. V. Eldred, P. C. Jurs, Prediction of Acute Mammalian Toxicity of Organophosphorus Pesticide Compounds from Molecular Structure. SAR and QSAR Environmental Research, Vol.10, 1999; 10, pp. 75–99.

[7] A. R. Katritzky, V. Lobanov, M. Karelson, CODESSA Reference Manual. University of Florida, Gainesville, 1996.

[8] Dragon Professional Version 5.5, Talete srl, (, 2007.

[9] PyMOL, Schrödinger, LLC. (, 2007.

[10] PyDescriptor, content/uploads/2016/06/PyDescriptors- Tutorial.pdf, 2016.

[11] C.W. Yap, PaDEL-descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints, Journal of Computational Chemistry, Vol.32, 2011, pp. 1466-1474.

[12] P. Gramatica, N. Chirico, E. Papa, S. Cassani, S. Kovarich, QSARINS: A New Software for the Development, Analysis, and Validation of QSAR MLR Models, Journal of Computational Chemistry, Vol.34, 2013, pp. 2121-2132.

[13] P. Gramatica, Principles of QSAR Models Validation: Internal and External. QSAR and Combinatorial Science, Vol, 26, 2007, pp. 694- 701.

[14] R. Todeschini, V. Consonni, The K correlation index: theory development and its application in chemometrics. Chemometrics and intelligent laboratory systems, Vol. 46, 1999, pp. 13-29.

[15] A. Tropsha, Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, Vol. 29, 2010, pp. 476- 488.

[16] O. G. Kolumbin, L. N. Ognichenko, A. G. Artemenko, P. G. Polischuk, М.A. Kulinskyb, Е. N. Мuratov, V. E. Kuz’min, V. A. Bobeica, Nonexperimental Screening of the Water Solubility, Lipophilicity, Bioavailability, Mutagenicity and Toxicity of Various Pesticides with QSAR Models Aid, Chemistry Journal of Moldova. General, Industrial and Ecological Chemistry. Vol.8., No.1, pp. 95- 100.

[17] K. Gholivand, L. Asadi, A. A. E. Valmoozi, M. Hodaii, M. Sharifi, H. M. Kashani, H. R. Mahzouni, M. Ghadamyari, A. A. Kalate, E. Davari, S. Salehi, M. Bonsaii, Phosphorhydrazide inhibitors: toxicological profile and antimicrobial evaluation assay, molecular modeling and QSAR study, RSC Advances, Vol.6, 2016, pp. 24175-24189.

[18] A.-M. Petrescu, G. Ilia, Computational toxicity evaluation of organophosphorus pesticides. Ecoterra, Vol. 14, 2015, pp. 90-96.

[19] S. A. Senior, M. D. Madbouly, A.M. El Massry, QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors. Chemosphere Vol.85, No.1, 2011, pp. 7-12.

WSEAS Transactions on Biology and Biomedicine, ISSN / E-ISSN: 1109-9518 / 2224-2902, Volume 14, 2017, Art. #15, pp. 105-111

Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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