Pharmacophore modeling and parallel screening for PPAR ligands
Pharmacophore modeling and parallel screening for PPAR ligands
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DOI：
10.1007/s10822-007-9140-0
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发表时间：
2007-10-01
影响因子：
3.5
通讯作者：
Langer, Thierry
中科院分区：
文献类型：
--
作者：
Markt, Patrick;Schuster, Daniela;Langer, Thierry
我们描述了PPARs药效团模型的生成和验证，以及通过对基于结构的模型的大型数据库筛选PPAR配体的并行筛选方法的大规模验证。针对48个PPAR模型筛选了357个PPAR配体的大型测试集，以确定PPAR-alpha、PPAR-delta和PPAR-gamma激动剂的最佳模型。之后，使用357个PPAR配体和47个基于结构的PPARs模型进行平行筛选，这些模型被整合到包含内部药效团数据库的1537个模型中，以评估PPAR假设内的PPAR配体富集。出于这些目的，我们将1537个数据库模型分类为181个蛋白质靶标，并开发了一个评分，对每个配体检索到的靶标进行排名。因此，我们试图找出平行筛选的概念是否能够预测一组化合物的正确药理学靶点。PPAR靶点比其他任何靶点都更经常地排在第一位。这证实了平行筛选预测一组化合物的药理学活性靶标的能力。
We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.