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从 1,2,4,5-四噁烷类似物看抗疟药物设计中的化学计量学方法。

Chemometric methods in antimalarial drug design from 1,2,4,5-tetraoxanes analogues.

机构信息

Centro de Ciências Naturais e Humanas, Universidade Federal ABC , Santo André, Brazil.

Laboratório de Química Teórica e Computacional, Faculdade de Química, Instituto de Ciências Naturais e Exatas, Universidade Federal do Pará , Belém, Brazil.

出版信息

SAR QSAR Environ Res. 2020 Sep;31(9):677-695. doi: 10.1080/1062936X.2020.1803961. Epub 2020 Aug 28.

DOI:10.1080/1062936X.2020.1803961
PMID:32854545
Abstract

A set of 23 steroidal 1,2,4,5-tetraoxane analogues were studied using quantum-chemical method (B3LYP/6-31 G*) and multivariate analyses (PCA, HCA, KNN and SIMCA) in order to calculate the properties and correlate them with antimalarial activity (log ) against clone D-6 from Sierra Leone. PCA results indicated 99.94% of the total variance and it was possible to divide the compounds into two classes: less and more active. Descriptors responsible for separating were: highest occupied molecular orbital energy (HOMO), bond length (O1-O2), Mulliken electronegativity (χ) and Bond information content (BIC0). We use HCA, KNN and SIMCA to explain relationships between molecular properties and biological activity of a training set and to predict antimalarial activity (log ) of 13 compounds (#24-36) with unknown biological activity. We apply molecular docking simulations to identify intermolecular interactions with a selected biological target. The results obtained in multivariate analysis aided in the understanding of the activity of the new compound's design (#24-36). Thus, through chemometric analyses and docking molecular study, we propose theoretical synthetic routes for the most promising compounds 28, 30, 32 and 36 that can proceed to synthesis steps and in vitro and in vivo assays.

摘要

为了计算性质并将其与针对来自塞拉利昂的克隆 D-6 的抗疟活性(log )相关联,我们使用量子化学方法(B3LYP/6-31G*)和多元分析(PCA、HCA、KNN 和 SIMCA)研究了一组 23 种甾体 1,2,4,5-四噁烷类似物。PCA 结果表明总方差的 99.94%,并且有可能将化合物分为两类:活性较低和较高。负责分离的描述符是:最高占据分子轨道能量(HOMO)、键长(O1-O2)、Mulliken 电负性(χ)和键信息含量(BIC0)。我们使用 HCA、KNN 和 SIMCA 来解释训练集的分子性质和生物活性之间的关系,并预测 13 种具有未知生物活性的化合物(#24-36)的抗疟活性(log )。我们应用分子对接模拟来识别与选定生物靶标的分子间相互作用。多元分析获得的结果有助于理解新化合物设计的活性。因此,通过化学计量分析和分子对接研究,我们为最有前途的化合物 28、30、32 和 36 提出了理论合成路线,这些化合物可以进行合成步骤以及体外和体内测定。

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