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Type: Artigo de periódico
Title: QSAR modeling of nucleosides against amastigotes of Leishmania donovani using logistic regression and classification tree
Author: Oliveira, KMG
Takahata, Y
Abstract: We employed two classification methods; first, a logistic regression, second, classification tree, to classify nucleoside activities against Leishmania donovani using a training set of 21 compounds. The compounds are classified either active or inactive. The model was validated using a test set of 14 compounds. Two descriptors, Mor26v and Gap(HOMO, HOMO-I), were selected. The logistic regression resulted classification accuracy of 90.5% for the training set, 67% for the test set after Applicability Domain analysis was performed. The method of classification tree resulted classification accuracy of 95% for the training set, 86% for the test set. It was shown that the lowest energy conformation can be used to build a QSAR model through examination of the whole conformations that lie above the lowest energy conformation in the energy window of 13 kcal/mol. The selected descriptor Mor26v distinguishes differences in molecular chirality, while Gap(HOMO, HOMO-1) distinguishes differences in electronic structures.
Country: Alemanha
Editor: Wiley-v C H Verlag Gmbh
Rights: fechado
Identifier DOI: 10.1002/qsar.200710172
Date Issue: 2008
Appears in Collections:Unicamp - Artigos e Outros Documentos

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