Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors
Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors
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DOI：
10.1007/s10822-011-9425-1
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发表时间：
2011-04-01
影响因子：
3.5
通讯作者：
Deharo, Eric
中科院分区：
文献类型：
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作者：
Caballero, Julio;Quiliano, Miguel;Deharo, Eric
我们对3-氟-4-（吡咯并[2，1-f][1，2，4]三嗪-4-基氧基）苯胺（FPTA）、3-氟-4-（1H-吡咯并[2，3-B]吡啶-4-基氧基）苯胺（FPPA）和4-（4-氨基-2-氟苯氧基）-2-吡啶胺（AFPP）衍生物与c-Met激酶的复合物进行了对接，研究了这些抑制剂的取向和优选活性构象。该研究对选定的103种结构和活性均存在差异的化合物进行了研究。对接有助于分析有助于所研究的化合物的高抑制活性的分子特征。此外，预测的c-Met激酶抑制剂的生物活性，测量为IC 50值，通过使用定量结构-活性关系（QSAR）方法：比较分子相似性分析（CoMSIA）和多元线性回归（MLR）与拓扑向量。最好的CoMSIA模型包括空间，静电，疏水和氢键供体字段;此外，我们发现了一个预测模型，其中包含二维自相关描述符，GETAWAY描述符（GETAWAY：几何，拓扑和原子重量组装），基于片段的极性表面积（PSA），和MlogP。统计参数：交叉验证相关系数和拟合相关系数，验证了所获得的76种化合物的预测模型的质量。此外，这些模型充分预测了未包括在训练集中的25种化合物。
We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC50 values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.