Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Molecular Models02:00

Molecular Models

44.2K
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.
44.2K
Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.4K
VSEPR Theory for Determination of Electron Pair Geometries
46.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Molecular mechanisms and biotechnology applications of CRISPR-Cas12a.

Nature reviews. Molecular cell biology·2026
Same author

Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry.

Journal of chemical theory and computation·2026
Same author

Orthosteric and allosteric effects of anti-CRISPR II-C1 inhibition on <i>Geo</i> Cas9 from integrated structural biophysics.

bioRxiv : the preprint server for biology·2026
Same author

Deep learning and cryogenic electron microscopy modeling for gene editing dynamics.

Current opinion in structural biology·2026
Same author

Computation and deep-learning-driven advances in CRISPR genome editing.

Nature structural & molecular biology·2026
Same author

Design Rules for Expanding PAM Compatibility in CRISPR-Cas9 from the VQR, VRER and EQR variants.

The journal of physical chemistry. B·2025

相关实验视频

Updated: Feb 28, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

图表神经网络用于分子动力学模拟.

Mohd Ahsan1, Chinmai Pindi1, Souvik Sinha1

  • 1Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA 52512, United States.

Current opinion in structural biology
|February 26, 2026
PubMed
概括

图形神经网络 (GNN) 通过使用数据驱动方法来增强分子动力学 (MD) 模拟. 这些网络提高了准确性,使得模拟速度更快,并有助于分析复杂的生物分子数据.

更多相关视频

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

2.0K
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.3K

相关实验视频

Last Updated: Feb 28, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

2.0K
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.3K

科学领域:

  • 计算化学是一种计算化学.
  • 生物物理学的生物物理.
  • 机器学习是机器学习.

背景情况:

  • 分子动力学 (MD) 模拟对于理解生物分子系统至关重要.
  • 传统的基于物理的MD方法在准确性和时间尺度上存在局限性.
  • 数据驱动的方法提供了补充策略来增强MD模拟.

研究的目的:

  • 探索图形神经网络 (GNN) 在推进分子动力学 (MD) 模拟中的应用.
  • 突出GNN如何整合化学和结构信息以提高准确性.
  • 展示GNN在加速生物分子发现方面的潜力.

主要方法:

  • 代表原子及其相互作用作为GNN输入的图形.
  • 在量子力学数据上训练神经网络力场.
  • 利用GNN来预测原子力和发现集体变量.
  • 应用注意力机制和可转移嵌入物用于轨迹分析.

主要成果:

  • 无线神经网络 (GNN) 能够准确地预测神经网络的力场,并有效地预测原子力的作用.
  • 集体变量的自动发现促进了模拟中的增强采样.
  • GNN提供了对高维分子轨迹的可解释的见解.
  • 在蛋白质-DNA组装和神秘口袋发现方面展示了成功的应用.

结论:

  • 图形神经网络 (GNN) 代表了一个强大的,数据驱动的模型,用于分子动力学 (MD) 模拟.
  • GNN显著提高了MD的准确性,效率和分析能力.
  • 纳米基因网络的整合有望加速生物分子科学领域的机械学和翻译学发现.