A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.
A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.
复制标题
一种新颖的基于结构的多模式 QSAR 方法为磷酸二酯酶抑制剂提供了预测模型。
DOI：
10.1021/ci900283j
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发表时间：
2010
影响因子：
5.6
通讯作者：
Zheng,Weifan
中科院分区：
文献类型：
--
作者：
Dong,Xialan;Ebalunode,JerryO;Cho,SungJin;Zheng,Weifan
定量构效关系（QSAR）方法旨在为新分子的发现建立定量预测模型。它已广泛应用于药物发现的药物化学中。自 Hansch 的开创性工作以来，已经开发了许多 QSAR 技术，并且更多技术仍在开发中。受Hopfinger的受体依赖性QSAR（RD-QSAR）形式和处理多模式问题的Lukacova-Balaz方案的启发，我们启动了专注于基于结构的多模式QSAR（SBMM QSAR）方法的研究，其中目标蛋白的结构用于表征配体，并用改进的Lukacova-Balaz方案系统地处理配体结合的多模式问题。所有配体分子首先对接至目标结合袋以获得一组对齐的配体姿势。采用基于结构的药效团概念来表征结合袋。具体来说，我们将结合口袋表示为由药效团特征标记的几何网格。配体的每个位姿也表示为标记网格，其中每个网格点根据附近配体原子的原子类型进行标记。这些标记的网格或三维 (3D) 图（受体图 (R-图) 和配体图 (L-图)）相互比较，以导出配体每个姿势的描述符，从而生成多模式构效关系 (SAR) 表。采用迭代偏最小二乘法 (PLS) 来构建 QSAR 模型。当我们应用这种方法分析PDE-4抑制剂时，已经开发了预测模型，获得了具有良好训练相关性（r2= 0.65−0.66）以及测试相关性（R2= 0.64−0.65）的模型。与其他 4 种 QSAR 技术的比较分析表明，就测试集的预测能力而言，这种新方法提供了更好的模型。
Quantitative structure−activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch’s seminal work, and more are still being developed. Motivated by Hopfinger’s receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova−Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova−Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure−activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r2= 0.65−0.66), as well as test correlation (R2= 0.64−0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.