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Classifying class I and class II compounds by hydrophobicity and hydrogen bonding descriptors
Author(s) -
Ren S.
Publication year - 2002
Publication title -
environmental toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.813
H-Index - 77
eISSN - 1522-7278
pISSN - 1520-4081
DOI - 10.1002/tox.10074
Subject(s) - linear discriminant analysis , homo/lumo , quantitative structure–activity relationship , hydrogen bond , biological system , discriminant , discriminant function analysis , function (biology) , pattern recognition (psychology) , chemistry , class (philosophy) , multivariate statistics , artificial intelligence , mechanism (biology) , mathematics , computational chemistry , computer science , stereochemistry , statistics , molecule , physics , organic chemistry , biology , quantum mechanics , evolutionary biology
The successful development of quantitative structure‐activity relationships (QSARs) and the prediction of toxicity based on QSARs depend on the correct classification of the mechanism of toxic action of chemical compounds. The toxicity mechanism of a compound can be determined by a few existing methods, such as studying the chemical structure of the compound for certain substructures and assigning a mechanism accordingly. However, these methods are less reliable for compounds with complex structures and are usually complicated. A novel descriptor‐based method employing a multivariate statistical technique—discriminant analysis—was developed in this study for the classification of class I and class II compounds. The discriminating variables used in discriminant analysis were the hydrophobicity descriptor log( K ow ) and the hydrogen bonding descriptors E LUMO , E HOMO , Q + , and Q − . Because only numerical values of these descriptors are required for this method, which can be calculated, no additional experimental work was required for the toxicity mechanism classification of new compounds. A nonlinear discriminant function was generated that could not be expressed explicitly. Assessing the predicting ability of the discriminant function by the cross‐validation method showed that a low total error rate, 4.2% was achieved. © 2002 Wiley Periodicals, Inc. Environ Toxicol 17: 415–423, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/tox.10074

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