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

Structural Classification of Joints01:20

Structural Classification of Joints

8.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
8.0K

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 Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.

Journal of chemical theory and computation·2026
Same author

Dequalinium-based bitopic ligands uncover distinct pharmacological modulation of muscarinic receptors.

Biochemical pharmacology·2026
Same author

Experimentally validated deep learning control of protein aggregation.

Communications chemistry·2026
Same author

A multi-scenario evaluation of adaptive Fuzzy Logic Algorithms for intelligent traffic signal management in Urban intersections.

Scientific reports·2026
Same author

Wavefront estimation through structured detection in laser scanning microscopy.

Biomedical optics express·2026
Same author

PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.

International journal of molecular sciences·2026

相关实验视频

Updated: May 2, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

通过基于结构的深度学习来解决对接姿势选择:最近的进展,挑战和机会.

Serena Vittorio1, Filippo Lunghini2, Pietro Morerio3

  • 1Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy.

Computational and structural biotechnology journal
|June 3, 2024
PubMed
概括

深度学习 (DL) 方法在改善药物发现的分子对接精度方面表现有前途. 与传统的评分函数相比,这些先进的算法可以更好地识别正确的结合姿势.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.分子对接是分子对接.姿势选择 姿势选择评分功能 评分功能 评分功能

更多相关视频

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.4K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.5K

相关实验视频

Last Updated: May 2, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

12.4K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.5K

科学领域:

  • 计算化学是一种计算化学.
  • 结构生物学是结构生物学.
  • 药物发现 药物发现

背景情况:

  • 分子对接对于预测药物发现中的联体-标相互作用至关重要.
  • 当前的评分函数往往难以准确识别本地绑定姿势.
  • 正确的姿势选择对于成功的药物优化至关重要.

研究的目的:

  • 审查深度学习 (DL) 对分子对接姿势选择的最新进展.
  • 为了比较DL方法与经典评分函数的性能.
  • 推出两种基于DL的新姿势选择器.

主要方法:

  • 基于DL的姿势选择方法的文献综述.
  • 经典评分函数和DL方法的比较分析.
  • 开发和介绍了两种新的基于DL的姿势选择器.

主要成果:

  • 深度学习算法显示了提高姿势选择准确性的潜力.
  • 在分子对接中,DL方法为传统的评分函数提供了一个有希望的替代方案.
  • 提出的基于DL的新型姿势选择器显示了竞争性表现.

结论:

  • 深度学习正在通过改进结合姿势预测来推进分子对接.
  • 基于DL的姿势选择对于有效的药物发现和优化至关重要.
  • 对DL应用的进一步研究可以克服当前对接精度的限制.