Hemmateenejad Bahram, Mehdipour Ahmad R, Popelier Paul L A
Department of Chemistry, Shiraz University, Shiraz, Iran.
Chem Biol Drug Des. 2008 Dec;72(6):551-63. doi: 10.1111/j.1747-0285.2008.00731.x.
Quantum topological molecular similarity produces a two-dimensional array of descriptors for each molecule while a three-dimensional array is obtained by placing the descriptor data matrices of a set of molecules beside each other. Here, we use the multiway data analysis method called molecular maps (MOLMAP) of atom-level properties in a new way. We transferred the three-dimensional array of quantum topological molecular similarity descriptors into new two-dimensional parameters using Kohonen networks, followed by partial least squares. Six different data sets were analyzed by the proposed procedure, which were previously analyzed (Eur. J. Med. Chem. 2006 41 862) by partial least squares applied to unfolded data. They include: (i) the pK(a) of imidazoles, (ii) the ability of a set of indole derivatives to displace [(3)H] flunitrazepam from binding to bovine cortical membranes, (iii) the inhibitory effect of a set of benzimidazoles on the influenza virus, (iv) the interaction of amides with liver alcohol dehydrogenase, (v) inhibition of carbonic anhydrase by sulfonamides and (vi) the toxicity of a set of chlorophenols. Overall, the results showed better statistical results compared with simple unfolding. Furthermore, variable important in projection plots confirmed previous findings about active centers and even in some cases showed more accurate results.
量子拓扑分子相似性为每个分子生成一个二维描述符数组，而通过将一组分子的描述符数据矩阵并排放置可获得三维数组。在此，我们以一种新的方式使用了称为分子图谱（MOLMAP）的原子级属性多向数据分析方法。我们使用Kohonen网络将量子拓扑分子相似性描述符的三维数组转换为新的二维参数，随后进行偏最小二乘法。通过所提出的程序分析了六个不同的数据集，这些数据集之前通过应用于展开数据的偏最小二乘法进行了分析（《欧洲医药化学杂志》2006年第41卷，第862页）。它们包括：（i）咪唑的pK(a)；（ii）一组吲哚衍生物从与牛脑皮层膜结合中置换[(3)H]氟硝西泮的能力；（iii）一组苯并咪唑对流感病毒的抑制作用；（iv）酰胺与肝醇脱氢酶的相互作用；（v）磺胺类药物对碳酸酐酶的抑制作用；以及（vi）一组氯酚类的毒性。总体而言，结果显示与简单展开相比具有更好的统计结果。此外，投影图中的变量重要性证实了先前关于活性中心的发现，甚至在某些情况下显示出更准确的结果。
