Crippen G M
College of Pharmacy, University of Michigan, Ann Arbor 48109-1065, USA.
J Med Chem. 1997 Sep 26;40(20):3161-72. doi: 10.1021/jm970211n.
EGSITE2 represents a substantial advance in a long series of methods for calculating receptor site models given only specific binding data. Compared to our most recently reported technique, EGSITE [Schnitker et al. J. Comput.-Aided Mol. Des. 1997, 11, 93-110] the user no longer has to simplify the structures of the molecules in the training set by clustering the atoms into a few superatoms. The only remaining source of subjectivity is the user's choice of compounds for the training set, which can be surprisingly few in number. Then EGSITE2 automatically produces typically several different models that explain the observed binding without outliers. The models are remarkably simple but have substantial predictive power for any sort of test compound, with an estimation of the uncertainty of the prediction. Validation of the method is reported for four standard test cases: triazines and pyrimidines binding to dihydrofolate reductase, steroids binding to corticosteroid-binding globulin and to testosterone-binding globulin, and peptides binding to angiotensin-converting enzyme.
EGSITE2代表了仅根据特定结合数据计算受体位点模型的一系列方法中的重大进展。与我们最近报道的技术EGSITE [施尼特克等人,《计算机辅助分子设计杂志》,1997年,11卷,93 - 110页]相比,用户不再需要通过将原子聚类成几个超级原子来简化训练集中分子的结构。唯一剩下的主观因素是用户对训练集化合物的选择,其数量可能少得出奇。然后EGSITE2会自动生成通常几个不同的模型,这些模型能够解释观察到的结合情况且没有异常值。这些模型非常简单,但对任何类型的测试化合物都具有强大的预测能力,并能估计预测的不确定性。本文报道了该方法在四个标准测试案例中的验证情况:三嗪和嘧啶与二氢叶酸还原酶的结合、类固醇与皮质类固醇结合球蛋白和睾酮结合球蛋白的结合,以及肽与血管紧张素转换酶的结合。