Golbraikh Alexander, Tropsha Alexander
Laboratory for Molecular Modeling, Division of Medicinal Chemistry, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7360, USA.
J Chem Inf Comput Sci. 2003 Jan-Feb;43(1):144-54. doi: 10.1021/ci025516b.
Topological descriptors of chemical structures (such as molecular connectivity indices) are widely used in Quantitative Structure-Activity Relationships (QSAR) studies. Unfortunately, these descriptors lack the ability to discriminate between stereoisomers, which limits their application in QSAR. To circumvent this problem, we recently introduced chirality descriptors derived from molecular graphs and applied them in QSAR studies of ecdysteroids (Golbraikh A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001,41, 147-158). In this paper, we extend our earlier work by applying chirality descriptors to four data sets containing chiral compounds. All models were derived with the k-nearest neighbors (kNN) QSAR method developed in our laboratory (Zheng, W.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2000, 40, 185-194). They were validated using the same training and test sets that were employed in various, mostly 3D-QSAR, investigations published by other authors. We show that for all data sets 2D-QSAR models that use a combination of chirality descriptors with conventional (chirality insensitive) topological descriptors afford better or similar predictive ability as compared to models generated with 3D-QSAR approaches. The results presented in this paper reassure that 2D-QSAR modeling provides a powerful alternative to 3D-QSAR.
化学结构的拓扑描述符(如分子连接性指数)在定量构效关系(QSAR)研究中被广泛应用。不幸的是,这些描述符缺乏区分立体异构体的能力,这限制了它们在QSAR中的应用。为了解决这个问题,我们最近引入了从分子图衍生的手性描述符,并将其应用于蜕皮甾类的QSAR研究中(戈尔布赖赫A.;邦切夫,D.;特罗普沙,A.《化学信息与计算机科学杂志》2001年,41卷,147 - 158页)。在本文中,我们通过将手性描述符应用于四个包含手性化合物的数据集来扩展我们早期的工作。所有模型均采用我们实验室开发的k近邻(kNN)QSAR方法推导得出(郑,W.;特罗普沙,A.《化学信息与计算机科学杂志》2000年,40卷,185 - 194页)。它们使用与其他作者发表的各种研究(大多为3D - QSAR研究)中所采用的相同训练集和测试集进行验证。我们表明,对于所有数据集,与使用3D - QSAR方法生成的模型相比,结合手性描述符与传统(不对手性敏感)拓扑描述符的2D - QSAR模型具有更好或相似的预测能力。本文给出的结果证实了2D - QSAR建模为3D - QSAR提供了一种强大的替代方法。