Department of Chemistry, McGill University , 801 Sherbrooke Street West, Montréal, QC H3A 0B8, Canada.
Department of Chemistry, Federal University of Lavras , P.O. Box 3037, 37200-000 Lavras-MG Brazil.
ACS Comb Sci. 2018 Feb 12;20(2):75-81. doi: 10.1021/acscombsci.7b00155. Epub 2018 Jan 22.
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.
我们最近通过应用离散傅里叶变换 (DFT) 将以前依赖于配准的多元图像分析应用于定量构效关系 (MIA-QSAR) 方法进行了推广，从而使其能够应用于非一致和结构多样化的化合物数据集。在这里，我们报告了该方法在筛选具有治疗意义的分子实体中的首次实际应用，以人芳香酶抑制活性为例。我们基于二维 (2D) DFT MIA-QSAR 描述符开发了一个集成分类模型，使用该模型对 NCI 多样性集 V（1593 种化合物）进行了筛选，并获得了 34 种可能具有芳香酶抑制活性的化合物。这些化合物被对接进入芳香酶活性部位，选择了 10 种最有前途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。研究结果表明，2D-DFT MIA-QSAR 方法可能有助于基于配体的新型治疗用途分子实体的虚拟筛选。
