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metadata.artigo.dc.title: Discrete fourier transform based multivariate image analysis: application to modeling aromatase inhibitory activity
metadata.artigo.dc.creator: Barigye, Stephen J.
Freitas, Matheus P.
Ausina, Priscila
Zancan, Patricia
Sola-Penna, Mauro
Castillo-Garit, Juan A.
metadata.artigo.dc.subject: Multivariate image analysis
Discrete Fourier transform
Aromatase inhibitory activity 2018
metadata.artigo.dc.identifier.citation: BARIGYE, S. J. et al. Discrete fourier transform based multivariate image analysis: application to modeling aromatase inhibitory activity. ACS Combinatorial Science, [S.l.], v. 20, n. 2, p. 75 – 81, 2018.
metadata.artigo.dc.description.abstract: We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.
metadata.artigo.dc.language: en_US
Appears in Collections:DQI - Artigos publicados em periódicos

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