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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Variable-selection approaches to generate QSAR models for a set of antichagasic semicarbazones and analogues

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Autor(es):
Scotti, Marcus Tullius [1, 2] ; Scotti, Luciana [3] ; Ishiki, Hamilton Mitsugu [2, 4] ; Peron, Leticia M. [2] ; de Rezende, Leandro [2] ; do Amaral, Antonia Tavares [2]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Fed Paraiba, Dept Engn & Environm, Campus 4, BR-58297000 Rio Tinto, PB - Brazil
[2] Univ Sao Paulo, Inst Quim, Dept Quim Fundamental, Ave Prof Lineu Prestes 748, BR-05508000 Sao Paulo - Brazil
[3] Univ Fed Paraiba, Ctr Biotechnol, BR-58051970 Joao Pessoa, Paraiba - Brazil
[4] Univ Oeste Paulista, UNOESTE, BR-19050920 Presidente Prudente, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS; v. 154, p. 137-149, MAY 15 2016.
Citações Web of Science: 12
Resumo

Quantitative structure-activity relationship (QSAR) models were proposed to correlate structural features or property descriptors of compounds with their corresponding biological activities. Because of the huge number of descriptors that encode different structural features used to generate valid QSAR models, variable selection becomes a fundamental step in building predictive and interpretative models. In this study, we applied a combined approach using multiple linear regression (MLR) and partial least-squares regression (PLS) to generate robust QSAR models with only a few descriptors applied to a set of cruzain inhibitors, namely, 61 semicarbazones and analogues, taken from the literature. From the 4885 descriptors generated by the Dragon program, we selected only five descriptors, applying the ``Best-First{''} algorithm and PLS, followed by analysis of frequency and, finally, the genetic algorithm with MLR. The most significant QSAR equation encodes important steric and electronic structural features, which helps to identify in the set, structures that increase or decrease the pIC(50) values measured against cruzain. (C) 2016 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 13/07937-8 - Redoxoma
Beneficiário:Ohara Augusto
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs