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Development and application of computational methods for the design of bioactive molecules.

机译:生物活性分子设计计算方法的开发和应用。

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摘要

Drug development is a time-consuming, expensive area of research that requires the collaboration of different fields of expertise. In this context, computer-aided methods have the potential to critically shorten the times and monetary expense required for preclinical development. Very early in a drug discovery effort, docking programs are used to virtually screen large libraries of compounds searching for a lead compound that can be developed into an efficient and safe drug. One of the critical components of a docking program is the one providing an estimate of the binding affinity of the formed complex: the scoring function. The central goal of this work was to develop methods to predict the binding affinity of potential drugs for application in virtual screening campaigns. Towards this goal, we first examined the current status of docking programs and scoring functions applied to a metalloprotein target relevant for medicinal chemistry, Golgi α-mannosidase II. This triggered a more in-depth analysis of scoring functions, which led us to assemble a set of protein complexes and analyze the performance of different available scoring functions in flexible and solvated proteins. We then set out to develop a scoring function based on molecular mechanical force fields and additional parameters accounting for entropic costs and solvation. We also reached into the development of molecules binding to nucleic acids by developing a hybrid docking/molecular dynamics method to study transition metal complexes binding to G-quadruplexes. Our docking program, FITTED, was prepared for the application to virtual screening campaigns with the implementation of the developed scoring functions and the development of additional tools to manipulate, select and prepare large libraries of ligands. With this goal, we developed a module for FITTED, termed SMART, to prepare ligands prior to the docking. The core of SMART was used to build two other modules, REACTOR and SELECT, that have applications in the preparation and clustering, respectively, of virtual libraries of ligands.
机译:药物开发是一项耗时,昂贵的研究领域,需要不同专业领域的合作。在这种情况下,计算机辅助方法有可能大大缩短临床前开发所需的时间和金钱费用。在药物发现工作的早期,对接程序实际上用于筛选大型化合物库,以寻找可开发为高效安全的药物的先导化合物。对接程序的关键组件之一是对所形成的复合体的结合亲和力进行估计的一种功能:评分功能。这项工作的主要目标是开发一种方法来预测潜在药物在虚拟筛选活动中的结合亲和力。为了实现这一目标,我们首先检查了与药物化学相关的金属蛋白靶标高尔基α-甘露糖苷酶II的对接程序和评分功能的当前状态。这触发了对评分功能的更深入分析,从而导致我们组装了一组蛋白质复合物,并分析了柔性和溶剂化蛋白质中不同可用评分功能的性能。然后,我们着手开发基于分子机械力场和解释熵成本和溶剂化作用的其他参数的评分函数。我们还通过开发杂交对接/分子动力学方法来研究与G-四链体结合的过渡金属配合物,从而开发了与核酸结合的分子。我们的对接程序F ITTED 已准备好用于虚拟筛选活动,并实现了开发的评分功能并开发了其他工具来操纵,选择和准备大型配体库。为此,我们开发了用于F ITTED 的模块,称为S MART ,以在对接之前制备配体。 S MART 的核心用于构建另外两个模块R EACTOR 和S ELECT ,它们分别在准备和聚类中具有应用程序的配体虚拟库。

著录项

  • 作者

    Englebienne, Pablo.;

  • 作者单位

    McGill University (Canada).;

  • 授予单位 McGill University (Canada).;
  • 学科 Chemistry Organic.;Chemistry Pharmaceutical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 239 p.
  • 总页数 239
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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