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Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure

Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure

Ester Papa, Alessandro Sangion, Olivier Taboureau, Paola Gramatica
Copyright: © 2018 |Volume: 3 |Issue: 2 |Pages: 12
ISSN: 2379-7487|EISSN: 2379-7479|EISBN13: 9781522547242|DOI: 10.4018/IJQSPR.2018070104
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MLA

Papa, Ester, et al. "Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure." IJQSPR vol.3, no.2 2018: pp.49-60. http://doi.org/10.4018/IJQSPR.2018070104

APA

Papa, E., Sangion, A., Taboureau, O., & Gramatica, P. (2018). Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 3(2), 49-60. http://doi.org/10.4018/IJQSPR.2018070104

Chicago

Papa, Ester, et al. "Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure," International Journal of Quantitative Structure-Property Relationships (IJQSPR) 3, no.2: 49-60. http://doi.org/10.4018/IJQSPR.2018070104

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Abstract

In this article, Quantitative Structure Activity Relationships (QSAR) were generated to link the structure of over 120 heterogeneous drugs to rat hepatotoxicity. Existing studies, performed on the same data set, could not highlight relevant structure-activity relationships, and suggested models for the prediction of hepatotoxicity based on genomic data. Binary activity responses were used for the development of classification QSARs using theoretical molecular descriptors calculated with the software PaDEL-Descriptor. A statistically powerful QSAR based on six descriptors was generated by using k-Nearest Neighbour (k-NN) method and by applying the Genetic Algorithm (GA) as variable selection procedure. The new k-NN QSAR outperforms published models by providing better accuracy and less false negatives. This model is a valid alternative to approaches based on genomic descriptors, which cannot be used in virtual screening of new compounds (pre- or post-synthesis) without experimental data.

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