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

Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
13.4K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.6K
Ligand Binding Sites02:40

Ligand Binding Sites

13.3K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
13.3K

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

Suppression of Ciliogenesis Alleviates Cellular Senescence via AKT Signaling in Gingival Aging.

Aging cell·2026
Same author

20-Hydroxyeicosatetraenoic Acid Ameliorates Nickel Nanoparticle-Induced Epithelial-Mesenchymal Transition by Modulating the FFAR1/NF-kB Pathway.

Chemical research in toxicology·2026
Same author

Integrating network toxicology, molecular simulation and experimental validation to decipher the role of CXCR4 in benzalkonium chloride-induced parakeratosis.

Chemico-biological interactions·2026
Same author

YBX1 takes actions on triggering M2-like polarization of macrophages and stabilizing CXCL8 mRNA to exhibit its metastatic potential in bladder cancer.

International immunopharmacology·2026
Same author

Childhood trauma and impulsive behaviors: the multiple chain mediating effects of neuroticism, stress perception and depression.

BMC psychology·2026
Same author

Efficacy of hc-tNGS for pathogen identification for pediatric cUTIs: a real-world observational study.

Frontiers in cellular and infection microbiology·2026

相关实验视频

Updated: Sep 15, 2025

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

2.9K

SaeGraphDTI:基于序列属性提取和图形神经网络的药物向相互作用预测.

Qiaosheng Zhang1,2, Zhenyu Sun3, Zhaoman Zhong3

  • 1School of Computer Engineering, Jiangsu Ocean University, No. 59, Cangwu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China. zqs@jou.edu.cn.

BMC bioinformatics
|July 16, 2025
PubMed
概括

SaeGraphDTI通过将序列特征与图形神经网络集成来提高药物向相互作用 (DTI) 的预测. 这种方法提高了准确性,加速了药物开发,降低了成本.

关键词:
深度学习是一种深度学习.药物目标相互作用预测预测.图表神经网络的神经网络序列属性提取 序列属性提取

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

587

相关实验视频

Last Updated: Sep 15, 2025

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

2.9K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

587

科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 准确的药物向相互作用 (DTI) 识别对于有效的药物开发至关重要.
  • 目前用于DTI预测的深度学习模型严重依赖于有效的特征提取.
  • 药物和目标网络包含有价值的拓信息,用于增强特征表示.

研究的目的:

  • 开发一种新的深度学习模型,用于预测药物向相互作用 (DTI).
  • 为了利用基于序列的特性和网络拓来改进DTI预测.
  • 引入SaeGraphDTI,一个结合序列属性提取和图形神经网络的模型.

主要方法:

  • 使用序列特征提取器来获得药物和目标序列的特性.
  • 现有的关系网络使用相似关系来增强.
  • 一个图形编码器更新节点信息,接着是用于DTI概率计算的图形解码器.

主要成果:

  • 拟议的SaeGraphDTI模型与最先进的方法相比显示出更高的性能.
  • 该模型在四个公共数据集上的多个关键指标中取得了最佳结果.
  • SaeGraphDTI有效地利用序列属性和网络拓来进行准确的预测.

结论:

  • SaeGraphDTI在预测潜在的药物向相互作用方面表现出强大的能力.
  • 该模型是加速药物开发管道的宝贵工具.
  • 这些发现突出了整合序列和网络信息用于DTI预测的潜力.