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Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study
Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences.ORCID iD: 0000-0003-4158-4148
Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences. Uppsala University. (BMC ; BBCL)ORCID iD: 0000-0002-0407-6542
Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences. (CCBG ; PPL ; BMC)ORCID iD: 0000-0002-7392-0591
Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences.
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2014 (English)In: International Journal of Molecular Sciences, ISSN 1422-0067, E-ISSN 1422-0067, Vol. 15, no 10, 18162-18174 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 15, no 10, 18162-18174 p.
Keyword [en]
k-nearest neighbor (k-NN), Munro database, genetic algorithm (GA), acute toxicity (LD50)
National Category
Biochemistry and Molecular Biology
Research subject
Chemistry, Organic Chemistry
Identifiers
URN: urn:nbn:se:lnu:diva-39262DOI: 10.3390/ijms151018162ISI: 000344457200032OAI: oai:DiVA.org:lnu-39262DiVA: diva2:782306
Available from: 2015-01-20 Created: 2015-01-20 Last updated: 2017-02-17Bibliographically approved

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Chavan, SwapnilNicholls, Ian A.Karlsson, Björn C. G.Rosengren, Annika M.
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CiteExportLink to record
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