Journal
ANALYTICA CHIMICA ACTA
Volume 572, Issue 2, Pages 272-282Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2006.05.027
Keywords
classification; skin sensitization; linear discriminant analysis; support vector machine
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The support vector machine (SVM), recently developed from machine learning community, was used to develop a nonlinear binary classification model of skin sensitization for a diverse set of 131 organic compounds. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a diverse set of molecular descriptors calculated from molecular structures alone. These six descriptors could reflect the mechanic relevance to skin sensitization and were used as inputs of the SVM model. The nonlinear model developed from SVM algorithm outperformed LDA, which indicated that SVM model was more reliable in the recognition of skin sensitizers. The proposed method is very useful for the classification of skin sensitizers, and can also be extended in other QSAR investigation. (c) 2006 Elsevier B.V. All rights reserved.
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