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相关概念视频

Molecular Models02:00

Molecular Models

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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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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
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用基础神经网络模型加速分子动力学模拟,使用多个时间步骤和蒸.

Côme Cattin1, Thomas Plé1, Olivier Adjoua1

  • 1Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 75005 Paris, France.

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概括
此摘要是机器生成的。

我们开发了一种蒸的多时间步骤 (DMTS) 策略,以加速分子动力学模拟. 该方法使用双层神经网络,实现显著的加快速度,同时保持对蛋白质等复杂系统的模拟精度.

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科学领域:

  • 计算化学计算化学
  • 分子动力学模拟模型
  • 机器学习在科学中的应用

背景情况:

  • 分子动力学 (MD) 模拟对于理解分子行为至关重要.
  • 神经网络潜能 (NNP) 提供高精度,但在计算上昂贵.
  • 加快MD模拟对于处理更大,更复杂的系统至关重要.

研究的目的:

  • 为加速MD模拟引入一种新的蒸多时间步骤 (DMTS) 策略.
  • 为了提高模拟效率,利用基础神经网络模型.
  • 为了保持模拟的准确性,同时显著降低计算成本.

主要方法:

  • 为MD模拟开发了一种双层神经网络架构.
  • 采用蒸工艺,从准确的NNP中创建更快,更低可信度的模型.
  • 将蒸模型集成到类似于可逆参考系统传播算法 (RESPA) 的框架中.
  • 利用主动学习来增强模拟稳定性,特别是对于化蛋白质.

主要成果:

  • 在MD模拟中实现了显著的加快速度:对同质系统近4倍,对大型溶解蛋白3倍.
  • 证明蒸模型 (3.5 Å截止值) 准确地捕获快速变化的力,主要是结合相互作用.
  • 保存了模拟系统的静态和动态特性,证实了方法的准确性.
  • 能够每3-6 fs评估昂贵的NNP,这与标准的1 fs时间步骤相比大幅增加.

结论:

  • 通过使用神经网络潜力,DMTS策略有效地加速了分子动力学模拟.
  • 这种方法保持了高精度,与标准方法相比,同时提供了显著的性能提升.
  • DMTS减少了神经网络潜力和经典力场之间的计算性能差距.
  • 该策略是多功能性的,适用于各种神经网络潜力和分子系统.