Rücker Christoph, Scarsi Marco, Meringer Markus
Biocenter, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.
Bioorg Med Chem. 2006 Aug 1;14(15):5178-95. doi: 10.1016/j.bmc.2006.04.005. Epub 2006 May 2.
Multilinear QSAR models are developed for the largest and most diverse set of PPARgamma agonists treated hitherto. Binding of these small molecules to the human nuclear receptor PPARgamma is described by models that are built on simple 2D molecular descriptors and nevertheless are of good quality and predictive power (e.g., 144 compounds, 10 descriptors, r2=0.79, r2(cv)=0.76). The models presented are thoroughly validated by crossvalidation, randomization experiments, bootstrapping, and training set/test set partitioning. They may therefore be helpful in the design of new antidiabetic drug candidates. For gene transactivation, the functional activity of the agonists, a corresponding model for a similarly diverse compound set is of somewhat lower statistical quality.
针对迄今为止所处理的最大且最多样化的一组PPARγ激动剂,开发了多线性定量构效关系(QSAR)模型。这些小分子与人类核受体PPARγ的结合通过基于简单二维分子描述符构建的模型来描述,然而这些模型质量良好且具有预测能力(例如,144种化合物,10个描述符,r2 = 0.79,r2(cv)= 0.76)。所呈现的模型通过交叉验证、随机化实验、自抽样法以及训练集/测试集划分进行了全面验证。因此,它们可能有助于新型抗糖尿病候选药物的设计。对于激动剂的功能活性即基因反式激活,针对类似多样化化合物集的相应模型在统计质量上略低。