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Property Biased-Diversity Guided Explorations of Chemical Spaces.

机译:化学空间的属性偏向多样性指导探索。

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摘要

Discovering functionally useful structures and materials by exploring the vastness of chemical space is an exciting undertaking. If done efficiently, one can discover structures that can have therapeutic value (such as drug like organic molecules) or technological value (such as organic light emitting diodes). While mining of chemical space has the potential to generate libraries of functional structures and materials, one can also easily be lost in its vastness (∼1060 theoretically possible small organic molecule ∼500 Da molecular weight or less). We have developed a strategy that allows efficient explorations of vast chemical spaces to generate libraries of functional organic molecules. The method, at its core, applies physical properties and structural diversity biased sampling of chemical space to search for new structures. We demonstrate the soundness and efficiency of this approach by searching through the known and enumerated databases to discover diverse organic molecules with optimum electronic and biophysical properties, and we also compare it to various existing approaches used for molecular search and property optimization. We also show a practical application of this approach by designing libraries of chromophores that emit light in the blue region of the spectrum as well as potential leads for protein and RNA binding.
机译:通过探索广阔的化学空间来发现功能上有用的结构和材料是一项令人兴奋的任务。如果能有效地完成,则可以发现具有治疗价值(例如有机分子之类的药物)或技术价值(例如有机发光二极管)的结构。虽然化学空间的挖掘有可能生成功能结构和材料的库,但也很容易失去它的广阔空间(理论上可能有约1060个有机小分子,分子量约为500 Da或更小)。我们已经开发出一种策略,可以有效地探索广阔的化学空间,以生成功能性有机分子库。该方法的核心是应用化学空间的物理特性和结构多样性偏差采样来寻找新结构。我们通过搜索已知和枚举的数据库来发现具有最佳电子和生物物理特性的各种有机分子,从而证明了这种方法的合理性和效率,并且还将其与用于分子搜索和特性优化的各种现有方法进行了比较。我们还通过设计在光谱的蓝色区域发射光的发色团库以及潜在的蛋白质和RNA结合物来显示该方法的实际应用。

著录项

  • 作者

    Rupakheti, Chetan Raj.;

  • 作者单位

    Duke University.;

  • 授予单位 Duke University.;
  • 学科 Chemistry.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 157 p.
  • 总页数 157
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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