Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity

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.