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首页> 外文学位 >Discovering drug candidates in virtual chemical libraries: A novel graph-based method for virtual screening.
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Discovering drug candidates in virtual chemical libraries: A novel graph-based method for virtual screening.

机译：在虚拟化学库中发现候选药物：一种基于图形的新型虚拟筛选方法。

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摘要

The virtual screening of chemical libraries for drug candidates is an important technology used by researchers to find novel drugs. The chemical libraries of interest may be very large so it is important that virtual screening methods be fast. Some of the best methods for ligand-based virtual screening, pharmacophore fingerprint methods, require a conformational search for each of the molecules in the screening library. This requirement for a conformational search makes it time-consuming to screen libraries containing millions of compounds because it may take more than a minute to screen a single molecule on a single processor.; We were interested in developing a faster approach to ligand-based virtual screening that would be able to quickly screen large libraries. We developed a novel graph-based method, the subgraph fingerprint (SPrint) method, that does not require a conformational search to be performed for each molecule. In this method each molecule is represented by a feature vector where the features correspond to subgraphs present in a particular type of molecular graph. In this graph representation the vertices of the graph represent atoms and the edge between two vertices represents the maximum distance that the two atoms can be apart. Statistical models of activity built from these feature vectors are used to screen chemical libraries for drug candidates. We show that our method is accurate; fast, interpretable, and may be able to find novel structures that have different chemical scaffolds from known inhibitors. In particular, we show that the SPrint method is able to screen chemical libraries at a rate greater than 60 molecules per second on a single processor and is as accurate as a leading pharmacophore fingerprint method on a benchmark data set of estrogen receptor inhibitors.; We applied the SPrint method to the problem of finding inhibitors to the cancer target checkpoint kinase-1 (Chk1), by screening a library of 1.1 million commercially available compounds. In addition to the SPrint method we also used two external methods, a ligand-based similarity method called Rocs and a structure-based docking method called GLIDE. We discuss the results from this virtual screening study and propose several molecules for experimental testing.
机译：化学筛选候选药物的化学库是研究人员用于发现新药的一项重要技术。感兴趣的化学文库可能非常大，因此快速进行虚拟筛选非常重要。一些基于配体的虚拟筛选的最佳方法，即药效团指纹法，要求对筛选文库中的每个分子进行构象搜索。构象搜索的这种要求使得筛选包含数百万个化合物的文库非常耗时，因为在单个处理器上筛选单个分子可能需要一分钟以上的时间。我们对开发一种更快的基于配体的虚拟筛选方法感兴趣，该方法能够快速筛选大型文库。我们开发了一种新颖的基于图的方法，即子图指纹（SPrint）方法，该方法不需要对每个分子进行构象搜索。在此方法中，每个分子都由特征向量表示，其中特征对应于特定类型的分子图中存在的子图。在此图形表示中，图形的顶点表示原子，两个顶点之间的边表示两个原子可以分开的最大距离。从这些特征向量构建的活动统计模型用于筛选候选药物的化学库。我们证明我们的方法是准确的；快速，可解释且可能能够找到具有与已知抑制剂不同化学骨架的新颖结构。特别是，我们表明SPrint方法能够在单个处理器上以大于60个分子/秒的速度筛选化学文库，并且与雌激素受体抑制剂的基准数据集上领先的药效团指纹法一样准确。通过筛选110万种可商购化合物的库，我们将SPrint方法应用于寻找癌症靶点检查点激酶1（Chk1）抑制剂的问题。除了SPrint方法外，我们还使用了两种外部方法，一种称为Rocs的基于配体的相似性方法，一种称为GLIDE的基于结构的对接方法。我们讨论了这项虚拟筛选研究的结果，并提出了几种用于实验测试的分子。

著录项

  • 作者

    Langham, James J.;

  • 作者单位

    University of California, Santa Cruz.;

  • 授予单位 University of California, Santa Cruz.;
  • 学科 Chemistry Biochemistry.; Chemistry Pharmaceutical.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 207 p.
  • 总页数 207
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物化学;药物化学;
  • 关键词

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