QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors

Kar, Supratik; Roy, Kunal

doi:10.1016/j.jhazmat.2009.12.038

Cited by 94 publications

(42 citation statements)

References 33 publications

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“…The presence of the electron acceptor substituent around the P=X group increases the inhibitory potential of the PHA derivatives [14]. Obtained results are in accordance with previous findings of QSTR modeling of toxicity of organic molecules to Daphnia magna [4]. Obtained PLS models suggests that higher lipophilicity and electrophilicity, and hydrogen bond donor groups are responsible for greater toxicity.…”

Section: Resultssupporting

confidence: 88%

“…Molecular descriptors describe: chemical properties (electrophilicity, hydrogen bonding), physicalchemical properties (lipophilicity, polar surface area), 2D structure (topological, information, connectivity, information indices, 2D frequency fingerprints), 3D structure (RDF, WHIM, GETAWEY, geometrical descriptors). Correlation of toxicity of molecule and molecular descriptors is most often expressed by linear equation calculated by multiple linear regression (MLR), or partial least squares (PLS) [4]. Computational neural networks (CNN) is usually performed if there is an assumption about a nonlinear and a highly complex relationship between the structure and the observed toxicity [5].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…As discussed later, individual components may contribute to the 'total toxicity' of the mixture even if individually, they do not reach a toxic concentration. A number of reports, dealing with QSAR octanol-water partition coefficients calculation of several compounds, have been published in the literature [6][7][8].…”

Section: Experimental Predictedmentioning

confidence: 99%

Quantitative structure activity relationship (QSAR) study of octanol-water partition coefficients of some of environmental toxic of petroleum substances

Shahpar¹,

Esmaeilpoor

2017

Asain J. Green Chem.

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Life and its extraction fuels climate change. We performed studies upon an extended series of petroleum hydrocarbons, with octanol-water partition coefficients (log Kow), by using the quantitative structure-activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors, resulting in the best-fit models. The partial least squares PLS (PLS) was utilized to construct the linear QSAR model. The best GA-PLS model contains 27 selected descriptors in 10 latent variables space. The R2 and RMSE for training and test sets were (0.827, 0.088) and (0.716, 0.185), respectively. Inspection of the results reveals a higher R2 and lowers the RMSE value parameter for the data set GA-PLS. The GA-PLS linear model has good statistical quality with low prediction error. This is the first research on the QSAR which uses GA-PLS for the presiction octanol-water partition coefficients of some of the environmental toxic of the petroleum substances. KEYWORDS Petroleum substances Genetic algorithm QSAR Graphical AbstractQuantitative structure activity relationship (QSAR) study… 117

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“…[44] The number of hydrogen bonding donors, derived here from the sum of nitrogen atoms in the molecule, impacts upon the hydrogen bonding potential of the drugs and their site of action. Here, the descriptor HDon_N (see Table 1 and Table 2) is also related to the toxic myopathy induced by drugs or toxins.…”

Section: The Role Of Molecular Descriptorsmentioning

confidence: 99%

In Silico Models to Discriminate Compounds Inducing and Noninducing Toxic Myopathy

Yan

2011

Molecular Informatics

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Toxic myopathy is a muscular disease in which the muscle fibers do not function and which results in muscular weakness. Some drugs, such as lipid-lowering drugs and antihistamines, can cause toxic myopathy. In this work, a dataset containing 232 chemical compounds inducing toxic myopathy (IM-compounds) and 117 drugs not inducing toxic myopathy (notIM-compounds) was collected. The dataset was split into a training set (containing 270 compounds) and a test set (containing 79 compounds). A Kohonen's self-organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate IM-compounds and notIM-compounds. Polarizibity related descriptors, electronegativity related descriptors, atom charges related descriptors, H-bonding related descriptor, atom identity and molecular shape descriptors were used to build models. Using the SOM method, classification accuracies of 88.4 % for the training set and 88.2 % for the test set were achieved; using the SVM method, classification accuracies of 95.6 % for the training set and 86.1 % for the test set were achieved. In addition, extended connectivity fingerprints (ECFP_4) were calculated and analyzed to find important substructures of molecules relating to toxic myopathy.

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QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors

Cited by 94 publications

References 33 publications

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

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

Quantitative structure activity relationship (QSAR) study of octanol-water partition coefficients of some of environmental toxic of petroleum substances

In Silico Models to Discriminate Compounds Inducing and Noninducing Toxic Myopathy

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