Acute intravenous toxicity to mice calculations on the basis local regression models in superoverlapping clusters (LRMSC)

   
Raevsky O.A.1 , Liplavskaya E.A.1, Worth A.P.2, Grigorev V.U.3

1. Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds of Russian Academy of Sciences
2. 2Institute for Health and Consumer Protection, European Commission
3. Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds of Russian Academy of Sciences
Section: Experimental/Clinical Study
DOI: 10.18097/PBMC20125805489      PubMed Id: 23289291
Year: 2012  Volume: 58  Issue: 5  Pages: 489-500
Modeling of quantitative structure - activity relationships between physicochemical descriptors of organic chemicals and their acute intravenous toxicity in mice have been presented. This approach includes three steps: structure-similarity chemicals selection for every chemical-of-interest (clusterization); construction of quantitative structure - toxicity models for every cluster (without including of chemical-of-interest); application of obtained QSAR equations for chemical-of-interest toxicity estimation. This approach has been applied for acute intravenous toxicity calculations of 10241 organic chemicals. For 7759 chemicals which has enough quantity of structural neighbours with the Tanimoto index (Tc) on the level 0.30 and over, a standard deviation of calculation vs. experimental log(1/LD50) values is equal to 0.51 at the estimation of experimental determination on the level 0.50. The results of calculations isn't so good for remain chemicals (~24%). It is connect with absence of sufficient number of structure similarity neighbours. It's assumed this QSAR approach can be useful for activity and toxicity prediction of chemicals large sets.
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Keywords: QSAR, toxicity, structural similarity, HYBOT, DRAGON, clusterization, regression models
Citation:

Raevsky, O. A., Liplavskaya, E. A., Worth, A. P., Grigorev, V. U. (2012). Acute intravenous toxicity to mice calculations on the basis local regression models in superoverlapping clusters (LRMSC). Biomeditsinskaya Khimiya, 58(5), 489-500.
This paper is also available as the English translation: 10.1134/S1990750811040081
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