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首页> 外文期刊>Journal of Medicinal Chemistry >Non-Peptide Angiotensin II Receptor Antagonists: Chemical Feature Based Pharmacophore Identification
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Non-Peptide Angiotensin II Receptor Antagonists: Chemical Feature Based Pharmacophore Identification

机译：非肽血管紧张素II受体拮抗剂：基于化学特征的药理学鉴定

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Chemical features based pharmacophore models were elaborated for angiotensin II receptor subtype 1 (AT_1)antagonists using both a quantitative and a qualitative approach (Catalyst HypoGen and HipHop algorithms, respectively). The training sets for quantitative model generation consisted of 25 selective AT_1 antagonists exhibiting IC_(50) values ranging from 1.5 nM to 150 μM. Additionally,a qualitative pharmacophore hypothesis was derived from multiconformational structure models of the two highly active AT_1 antagonists 4u (IC_(50) = 0.2 nM) and 3k (IC_(50) = 0.7 nM). In the case of the quantitative model, the best pharmacophore hypothesis consisted of a five-features model (Hypo1: seven points, one hydrophobic aromatic, one hydrophobic aliphatic, a hydrogen bond acceptor, a negative ionizable function, and an aromatic plane function). The best qualitative model consisted of seven features (Hypo2: 11 points, two aromatic rings, two hydrogen bond acceptors, a negative ionizable function, and two hydrophobic functions). The obtained pharmacophore models were validated on a wide set of test molecules. They were shown to be able to identify a range of highly potent AT_1 antagonists, among those a number of recently launched drugs and some candidates presently undergoing clinical tests and/or development phases. The results of our study provide confidence for the utility of the selected chemical feature based pharmacophore models to retrieve structurally diverse compounds with desired biological activity by virtual screening.
机译：使用定量和定性方法（分别为Catalyst HypoGen和HipHop算法），针对血管紧张素II受体亚型1（AT_1）拮抗剂拟定了基于化学特征的药效团模型。定量模型生成的训练集由25种选择性AT_1拮抗剂组成，这些拮抗剂表现出的IC_（50）值范围为1.5 nM至150μM。此外，定性药效基团假设是从两种高活性AT_1拮抗剂4u（IC_（50）= 0.2 nM）和3k（IC_（50）= 0.7 nM）的多构象结构模型得出的。在定量模型的情况下，最佳药效基团假说由五种特征模型组成（Hypo1：七个点，一个疏水性芳香族化合物，一个疏水性脂肪族化合物，氢键受体，负离子化功能和芳香族平面功能）。最好的定性模型包括七个特征（Hypo2：11个点，两个芳香环，两个氢键受体，一个负离子化功能和两个疏水功能）。所获得的药效团模型在多种测试分子上得到验证。研究表明，它们能够识别一系列高效的AT_1拮抗剂，包括许多新近投放市场的药物以及目前正在接受临床测试和/或开发阶段的一些候选药物。我们的研究结果为选定的基于化学特征的药效团模型的实用性通过虚拟筛选检索具有所需生物活性的结构多样的化合物提供了信心。

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