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.

中文翻译：

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基于离散傅立叶变换的多元图像分析：在芳香酶抑制活性建模中的应用

我们最近通过离散傅立叶变换（DFT）的应用，将以前依赖于比对的多元图像分析推广到了定量结构-活性关系（MIA-QSAR）方法，从而使其可用于非一致且结构多样的化合物数据集。在这里，我们报告这种方法在筛选具有治疗意义的分子实体方面的首次实际应用，并以人芳香酶抑制活性为例。我们基于二维（2D）DFT MIA-QSAR描述符开发了整体分类模型，通过该模型我们筛选了NCI多样性集V（1593种化合物），并获得了34种可能具有芳香酶抑制活性的化合物。这些化合物被对接到芳香化酶的活性位点，并选择了10种最有希望的化合物进行体外实验验证。在这些化合物中，7419（非甾体）和89201（甾体）表现出令人满意的抗增殖和芳香酶抑制活性。获得的结果表明2D-DFT MIA-QSAR方法可能在基于配体的虚拟筛选具有治疗作用的新分子实体中有用。