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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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ChemFlow_py:用于对接和重新对接的灵活工具包.

Luca Monari1, Katia Galentino1, Marco Cecchini2

  • 1Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.

Journal of computer-aided molecular design
|August 24, 2023
PubMed
概括

提高药物发现的虚拟查是关键. 通过结合多种方法,共识评分可以提高分子对接的准确性,特别是对于新型蛋白质点. 这样可以优化虚拟高通量选 (vHTS).

关键词:
达成共识的得分得分方法停靠对接 停靠对接重新进行得分.基于结构的药物发现.虚拟高通量选 虚拟高通量选

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科学领域:

  • 计算化学和化学信息学
  • 药物的发现和开发.
  • 生物信息学和计算生物学

背景情况:

  • 准确的虚拟查工具在药物发现中至关重要但具有挑战性.
  • 分子对接提供了效率,但往往缺乏准确性,性能因蛋白质目标而异.
  • 选择合适的对接程序和评分功能对于新目标至关重要.

研究的目的:

  • 开发一个自动化工具,ChemFlow_py,用于比较对接和恢复协议的性能.
  • 评估各种rescoring策略的有效性,包括共识评分,用于虚拟选.
  • 使用开源工具包优化虚拟高吞吐量选 (vHTS) 性能.

主要方法:

  • 开发了ChemFlow_py,这是一个用于对接和重定位的自动化工作流.
  • 从DUD-E数据集中利用了四个蛋白质系统,每一个都有100个已知的活性和3000个诱.
  • 对比了多个重评分策略,重点关注共识评分的表现.

主要成果:

  • 与个人方法相比,共识排名显著改善了平均对接结果.
  • 达成共识的评分在有限的化学信息可用于目标时表现出特别的相关性.
  • ChemFlow_py提供了一个免费的工具包来提高vHTS的性能.

结论:

  • 共识评分是提高药物发现中分子对接的准确性的一种有价值的策略.
  • 开发的ChemFlow_py工具包为优化虚拟选协议提供了一个实用的解决方案.
  • 这些发现强调了强大的计算工具对于识别潜在药物候选者的重要性.