QSAR modeling of nucleosides against amastigotes of Leishmania donovani using logistic regression and classification tree

Autor(es)

Oliveira, Kesley M. G. ^[1] ; Takahata, Yuji ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Chem, BR-13084862 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: QSAR & COMBINATORIAL SCIENCE; v. 27, n. 8, p. 1020-1027, AUG 2008.
• Citações Web of Science: 3

Resumo

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. (AU)

• Processo FAPESP: 98/16485-1 - Aplicação das técnicas de SAR a compostos com atividade anti-Leishmaniose
• Beneficiário: Kesley Moraes Godinho de Oliveira
• Modalidade de apoio: Bolsas no Brasil - Doutorado