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

38.1K
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.
38.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45

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

Linking biochemical and cellular efficacy of MERS coronavirus main protease inhibitors.

ACS pharmacology & translational science·2026
Same author

Bridging between Structure-Based and Data-Driven Affinity Prediction.

Journal of chemical information and modeling·2026
Same author

Context-dependent peptide recognition shapes tyrosine kinase substrate specificity beyond consensus motifs.

bioRxiv : the preprint server for biology·2026
Same author

Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.

Nature communications·2026
Same author

Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.

Journal of chemical information and modeling·2026
Same author

A Hidden Binding Pocket in the β- ketoacyl-ACP Synthase FabB.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jun 16, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.2K

机器学习的分子力学力场来自大规模的量子化学数据.

Kenichiro Takaba1,2, Anika J Friedman3, Chapin E Cavender4

  • 1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York NY 10065 USA john.chodera@choderalab.org wangyq@wangyq.net.

Chemical science
|August 16, 2024
PubMed
概括

一个新的机器学习分子力学 (MM) 力场,espaloma-0.3,提供精确的生物分子模拟. 这种先进的模型,在广泛的量子化学数据上训练,增强了药物发现和蛋白质建模.

更多相关视频

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

相关实验视频

Last Updated: Jun 16, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.2K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

科学领域:

  • 计算化学是一种计算化学.
  • 生物分子建模模型
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 分子力学 (MM) 力场对于生物分子模拟和药物设计至关重要.
  • 传统的基于规则的MM力场在准确性和可扩展性方面存在局限性.
  • 开发可靠和可扩展的力场对于推进计算化学至关重要.

研究的目的:

  • 引入一个通用和可扩展的机器学习MM力场,espaloma-0.3.3.
  • 使用图形神经网络克服传统MM力场的局限性.
  • 提供一个框架,系统地构建更准确和可扩展的力场.

主要方法:

  • 开发了一个端到端可分化的框架,利用图形神经网络.
  • 在一个大型量子化学数据集 (1.1M+能量/力计算) 上训练了espaloma-0.3力场.
  • 验证了力场对小分子,和核酸的性能.

主要成果:

  • 埃斯帕洛马-0.3准确地复制了药物发现相关领域的量子化学能量特性.
  • 强场维持了小分子的量子化学能量最小化的几何形状.
  • 实现了和蛋白质的稳定模拟,并准确地预测了结合的自由能量.

结论:

  • 机器学习的力场espaloma-0.3显示出对准确的生物分子模拟具有重大前景.
  • 这种方法允许系统地开发可扩展和准确的力场,用于新的化学领域.
  • 埃斯帕洛马-0.3推进了计算机辅助药物设计和计算化学研究.