Bak Andrzej, Polanski Jaroslaw
Department of Organic Chemistry, Institute of Chemistry, University of Silesia, PL-40-006 Katowice, Poland.
J Chem Inf Model. 2007 Jul-Aug;47(4):1469-80. doi: 10.1021/ci700025m. Epub 2007 Jun 14.
In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.
在本文中，我们提出了一种基于自组织映射的与受体无关的4D-QSAR方法（SOM-4D-QSAR），并特别关注其药效团映射能力。我们使用一种新颖的随机程序来验证该方法对大量生成的4D-QSAR模型的预测能力。这项系统研究是针对一系列结合芳香酶的苯甲酸、偶氮染料和类固醇进行的。我们表明，结合IVE-PLS的4D-QSAR方法提供了一种非常稳定且具有预测性的建模技术。该方法使我们能够识别对类固醇的纤维-染料亲和力和芳香酶结合活性贡献最大的分子基序。然而，该方法对苯甲酸系列的效果似乎要差得多，在该系列中，其效果受到与单一构象体严格相关的电子效应的限制。
