NDLI: QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors.

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QSAR-modeling of toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Toropov, A. A. Toropova, A. P. Benfenati, E.
Description	Country affiliation: Uzbekistan Author Affiliation: Toropov AA ( Institute of Geology and Geophysics, Khodzhibaev St. 49, 100041, Tashkent, Uzbekistan. aatoropov@yahoo.com)
Abstract	Quantitative structure-activity relationships (QSAR) for toxicity toward rats (pLD50) have been built by means of optimal descriptors. Comparison of the optimal descriptors calculated using the International Chemical Identifier (InChI) with the optimal descriptors calculated using the simplified molecular input line entry system (SMILES) has shown that the InChI-based models give more accurate prediction for the above-mentioned toxicity of organometallic compounds. These models were obtained by means of the balance of correlation: one subset of the training set (subtraining set) plays role of the training; the second subset (calibration set) plays role of the preliminary check of the models. It has been shown that the balance of correlations is a more robust predictor for the toxicity than the classic scheme (training set-test set: without the calibration set). Three splits into the subtraining set, calibration set, and test set were examined.
File Format	HTM / HTML
ISSN	13811991
Issue Number	1
Volume Number	14
e-ISSN	1573501X
Journal	Molecular Diversity
Language	English
Publisher	Springer
Publisher Date	2010-02-01
Publisher Place	Netherlands
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Molecular Biology Algorithms Models, Chemical Organometallic Compounds Chemistry Toxicity Animals Calibration Computer Simulation Lethal Dose 50 Monte Carlo Method Quantitative Structure-activity Relationship Rats Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Medicine Drug Discovery Molecular Biology Physical and Theoretical Chemistry Information Systems Catalysis Inorganic Chemistry

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