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

Ligand Binding Sites02:40

Ligand Binding Sites

13.2K
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...
13.2K
Ligand-Gated Ion Channel Receptor: Gating Mechanism01:30

Ligand-Gated Ion Channel Receptor: Gating Mechanism

2.6K
Ligand-gated ion channels are transmembrane proteins that play a vital role in intercellular communication and functions of the nervous system. They allow the influx of ions across the membrane once the neurotransmitter binds, allowing the subsequent transmission of electrical excitation across the neurons. Other ligand-gated ion channels, like the γ-aminobutyric acid (GABA) receptor, permit anions like chloride into the cells on the binding of the GABA molecule. Their entry into the cell...
2.6K
Ligand-gated Ion Channels01:19

Ligand-gated Ion Channels

12.7K
Ligand-gated ion channels are transmembrane proteins with a channel for ions to pass through and a binding site for a ligand. The channel opens only when a ligand attaches to the binding site.
Three Subfamilies of Ligand-gated Ion Channels
Ligand-gated ion channels fall into three subfamilies. The 'Cys-loop' includes the nicotinic acetylcholine receptors, γ-aminobutyric acid (GABA), glycine, and 5-hydroxytryptamine receptors. The second one is the 'Pore-loop' channels that...
12.7K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.4K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.4K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

3.4K
3.4K
The Two-State Receptor Model01:29

The Two-State Receptor Model

2.4K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
2.4K

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

Evolving Landscape of Glioblastoma Research: Integrating Therapeutic Advances and Diagnostic Frontiers.

Brain sciences·2026
Same author

A review of recent efforts directed toward the discovery of small molecule modulators of KCNT1 channels.

Future medicinal chemistry·2026
Same author

Efficient Regioselective Synthesis of Benzimidazoles and Azabenzimidazoles to Enable the Rapid Development of Structure-Activity Relationships for Activation of SLACK Potassium Channels.

Synlett : accounts and rapid communications in synthetic organic chemistry·2026
Same author

Cell-Surface PCNA Is Co-Expressed with Biomarkers of Stemness and Immunosuppression in Glioblastoma.

Cancers·2025
Same author

Evaluating Leucine, Isoleucine, and Valine Ratios in Mixed Cortical Cell Cultures Following Cortical Trauma: An In Vitro Assessment.

International journal of translational medicine (Basel, Switzerland)·2025
Same author

Developing inhibitors of the guanosine triphosphate hydrolysis accelerating activity of Regulator of G protein Signaling-14.

The Journal of biological chemistry·2025

相关实验视频

Updated: Sep 11, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.5K

I-GAT:可解释的图表注意网络用于连接物优化.

Ezek Mathew1, Kyle A Emmitte2, Jin Liu2

  • 1Department of Microbiology and Immunology, The University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States.

ACS omega
|August 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个机器学习 (ML) 模型,使用可解释图表注意力 (I-GAT) 网络来预测药物联体的选择性和功效,实现高精度并为药物优化提供可解释的见解.

更多相关视频

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
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.5K

相关实验视频

Last Updated: Sep 11, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.5K
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
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.5K

科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 药品化学 药品化学 是一个

背景情况:

  • 在药物发现中,设计有选择性和强效的配体至关重要,但具有挑战性.
  • 机器学习 (ML) 为连接体设计提供先进的计算解决方案.
  • 现有的ML模型往往缺乏可解释性,阻碍了优化工作.

研究的目的:

  • 开发一种复合ML模型,以高精度预测配体选择性和功效.
  • 为了提高ML模型的可解释性,用于指导连接体优化.
  • 创建一个有效和有针对性的药物发现框架.

主要方法:

  • 编制了一组数据集,包含了757个甲基胺基质受体亚型2 (mGlu2) 和亚型3 (mGlu3) 的负基调节器 (NAMs) 的配体.
  • 开发了一个复合ML模型,集成图形架构和转移学习 (第一阶段).
  • 采用了注意力机制和注意力梯度,用于模型解释性和连接体修饰 (第二和第三阶段).

主要成果:

  • 在预测配体NAM选择性方面达到97%以上的准确性,在强度方面达到78%以上的准确性.
  • 该模型提供了原子层次的解释性,阐明了ML决策过程.
  • 通过智能修饰成功设计了一种具有预测优越性质的新型联体.

结论:

  • 开发的可解释图表注意力 (I-GAT) 模型为连接体设计提供了高预测准确性和原子级可解释性.
  • 这种方法可以通过为优化连接体提供强大的框架来加速药物发现.
  • 该模型的性能与最先进的ML模型相匹配或超过,并且可以适应其他目标.