Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.
Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.
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DOI：
10.2174/1875397300903010054
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发表时间：
2009-12-31
期刊：
影响因子：
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通讯作者：
Zheng, Weifan
中科院分区：
文献类型：
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作者：
Adekoya, Adetokunbo;Dong, Xialan;Zheng, Weifan
磷酸二酯酶-4(PDE-4)是多种疾病的重要药物靶点，包括慢性阻塞性肺疾病(COPD)和神经退行性疾病。在这篇文章中，我们描述了一种新的基于多构象结构的药效团关键字(MC-SBPPK)方法用于改进的QSAR(定量构效关系)模型。与我们以前的工作类似，这种方法基于分子的药效团特征与目标结合口袋的匹配来计算分子描述符。因此，这些描述符是特定于PDE4的，并且与正在研究的问题最相关。此外，这项工作扩展了我们以前的SBPPK QSAR方法，在回归分析中显式地包括了PDE-4抑制剂的多种构象，从而解决了分子柔性的问题。根据Lukacova-Balaz格式，将包含多个构象的非线性回归问题转化为线性方程，并用迭代偏最小二乘法(IPLS)求解。用该方法对35种PDE-4抑制剂进行了分析，并建立了预测模型。基于对训练集和测试集的预测统计，这些新模型比传统的基于配体的QSAR技术以及我们之前工作中报道的SBPPK方法获得的模型更稳健和更具预测性。因此，多个预测模型被添加到PDE4抑制剂的QSAR模型集合中。总的来说，这些模型将有助于发现针对PDE-4酶的新候选药物。
Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.