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Oliveira, K. M. G.; Takahata, Y. QSAR Modeling of Nucleosides Against Amastigotes of Leishmania donovani Using Logistic Regression and Classification Tree. QSAR Comb. Sci. 2008, 27, 1020–1027.

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Oliveira, K. M. G.; Takahata, Y. QSAR Modeling of Nucleosides Against Amastigotes of Leishmania donovani Using Logistic Regression and Classification Tree. QSAR Comb. Sci. 2008, 27, 1020–1027.

QDB archive DOI: 10.15152/QDB.169   DOWNLOAD

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Property Activity: Activity against Leishmania donovani

Eq.3: QSAR model for nucleosides

Logistic regression (classification)

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Name Type n Accuracy
Training set training 21 0.905
Validation set external validation 14 0.571
Fig.4: Classification tree for nucleosides

Decision tree (classification)

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Name Type n Accuracy
Training set training 21 0.952
Validation set external validation 14 0.857

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When using this QDB archive, please cite (see details) it together with the original article:

  • Piir, G. Data for: QSAR Modeling of Nucleosides Against Amastigotes of Leishmania donovani Using Logistic Regression and Classification Tree. QsarDB repository, QDB.169. 2015. http://dx.doi.org/10.15152/QDB.169

  • Oliveira, K. M. G.; Takahata, Y. QSAR Modeling of Nucleosides Against Amastigotes of Leishmania donovani Using Logistic Regression and Classification Tree. QSAR Comb. Sci. 2008, 27, 1020–1027. http://dx.doi.org/10.1002/qsar.200710172

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dc.date.accessioned 2015-09-14T08:07:48Z
dc.date.available 2015-09-14T08:07:48Z
dc.date.issued 2015-09-14
dc.identifier.uri http://hdl.handle.net/10967/169
dc.identifier.uri http://dx.doi.org/10.15152/QDB.169
dc.description.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-1), 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.
dc.publisher Geven Piir
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Oliveira, K. M. G.; Takahata, Y. QSAR Modeling of Nucleosides Against Amastigotes of Leishmania donovani Using Logistic Regression and Classification Tree. QSAR Comb. Sci. 2008, 27, 1020–1027.
qdb.property.endpoint 6. Other (Nucleoside activity against Leishmania donovani) en_US
qdb.property.species Leishmania donovani en_US
qdb.descriptor.application DRAGON 3.0 en_US
qdb.descriptor.application AMSOL 7.1 en_US
qdb.prediction.application GRETL 1.6.0 en_US
qdb.prediction.application ORANGE en_US
bibtex.entry article en_US
bibtex.entry.author Oliveira, K. M. G.
bibtex.entry.author Takahata, Y.
bibtex.entry.doi 10.1002/qsar.200710172 en_US
bibtex.entry.journal QSAR Comb. Sci. en_US
bibtex.entry.month Aug
bibtex.entry.number 8 en_US
bibtex.entry.pages 1020–1027 en_US
bibtex.entry.title QSAR Modeling of Nucleosides Against Amastigotes of Leishmania donovani Using Logistic Regression and Classification Tree en_US
bibtex.entry.volume 27 en_US
bibtex.entry.year 2008
qdb.model.type Logistic regression (classification) en_US
qdb.model.type Decision tree (classification) en_US


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