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首页> 外文期刊>Journal of Medicinal Chemistry >Generation of Predictive Pharmacophore Models for CCR5 Antagonists: Study with Piperidine- and Piperazine-Based Compounds as a New Class of HIV-1 Entry Inhibitors
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Generation of Predictive Pharmacophore Models for CCR5 Antagonists: Study with Piperidine- and Piperazine-Based Compounds as a New Class of HIV-1 Entry Inhibitors

机译：CCR5拮抗剂的预测药理模型的生成：以哌啶和哌嗪为基础的化合物作为新型HIV-1进入抑制剂的研究

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Predictive pharmacophore models were developed for a large series of piperidine- and piperazine-based CCR5 antagonists as anti-HIV-1 agents reported by Schering-Plough Research Institute in recent years. The pharmacophore models were generated using a training set consisting of 25 carefully selected antagonists based on well documented criteria. The activity spread, expressed in K_j, of training set molecules was from 0.1 to 1300 nM. The most predictive pharmacophore model (hypothesis 1), consisting of five features, namely , two hydrogen bond acceptors and three hydrophobic, had a correlation (r) of 0.920 and a root mean square of 0.879, and the cost difference between null cost and fixed cot was 44.46 bits. The model was cross-validated by randomizing the data using the CatScramble technique. The results confirmed that the pharmacophore models generated from the test set were not due to chance correlation. The best model (hypothesis 1) was validated using test set molecules (total of 78) and performed well in classifying active and inactive molecules correctly. The model was further validated by mapping onto it a diverse set of six CCR5 antagonists identified by five different pharmaceutical companies. The best model correctly predicted these compounds as being highly active. These multiple validation approaches provide confidence in the utility of the predictive pharmacophore model developed in this study as a 3D query tool in virtual screening to retrieve new chemical entities as potent CCR5 antagonists. The model can also be used in predicting biological activities of compounds prior to undertaking their costly synthesis.
机译：先灵-雅研究所最近几年报告，针对作为抗HIV-1剂的一系列哌啶和哌嗪类CCR5拮抗剂，开发了预测药效团模型。使用训练集生成药效团模型，该训练集基于充分记录的标准，由25种精心选择的拮抗剂组成。训练集分子以K_j表示的活性扩散为0.1至1300nM。最具预测性的药效团模型（假设1）由五个特征（即两个氢键受体和三个疏水性）组成，相关系数（r）为0.920，均方根值为0.879，零成本和固定成本之间的成本差异婴儿床是44.46位。通过使用CatScramble技术对数据进行随机化，对模型进行交叉验证。结果证实，从测试集生成的药效团模型不是由于偶然相关性造成的。最佳模型（假设1）已使用测试集分子（总共78个）进行了验证，并且在正确分类有活性和无活性分子方面表现良好。通过将五种不同制药公司鉴定出的六种CCR5拮抗剂的多样化映射到模型上，进一步验证了该模型。最佳模型正确地预测了这些化合物具有很高的活性。这些多重验证方法使人们对本研究中开发的预测药效团模型作为虚拟筛选中的3D查询工具的效用充满信心，以检索作为有效CCR5拮抗剂的新化学实体。该模型还可用于在进行昂贵的合成之前预测化合物的生物活性。

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