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Int J Mol Sci. 2014 Oct 9;15(10):18162-74. doi: 10.3390/ijms151018162.

Towards global QSAR model building for acute toxicity: Munro database case study.

Author information

1
Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden. swapnil.chavan@lnu.se.
2
Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden. ian.nicholls@lnu.se.
3
Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden. bjorn.karlsson@lnu.se.
4
Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden. annika.rosengren@lnu.se.
5
Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy. davide.ballabio@unimib.it.
6
Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy. viviana.consonni@unimib.it.
7
Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy. roberto.todeschini@unimib.it.

Abstract

A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.

PMID:
25302621
PMCID:
PMC4227209
DOI:
10.3390/ijms151018162
[Indexed for MEDLINE]
Free PMC Article

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