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-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

6.2K
Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
6.2K
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

2.9K
An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
2.9K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

352
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
352

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

Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Enhanced immobilization of cadmium, lead, and antimony with improved soil fertility using sulfate-reducing bacteria@nano zero-valent iron-modified biochar: coupled chemisorption and microbial mechanisms.

Frontiers in microbiology·2026
Same author

CNER-Omni: A unified dynamic modality learning framework for Chinese named entity recognition across text and speech.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Data Augmentation for Few-Shot Biomedical NER Using ChatGPT.

Artificial intelligence in medicine·2025
Same author

Traits improvement of wild rice O. rufipogon via multiplex genome editing.

Journal of integrative plant biology·2025
Same author

ADENER: A syntax-augmented grid-tagging model for Adverse Drug Event extraction in social media.

Journal of biomedical informatics·2025
Same journal

Topological skeleton analysis for network-based shape representation in biology and beyond.

iScience·2026
Same journal

Condition-specific neural signatures of reactivation during post-retrieval rest: An EEG study.

iScience·2026
Same journal

Multi-chaotic signal identification employing a causal cross-correlation neural network.

iScience·2026
Same journal

Repeated insertions at positions 261-280 in KPC-2 highlight a ceftazidime-avibactam resistance hotspot.

iScience·2026
Same journal

ROS inhibits microtubule dynamics and cell growth heterogeneity during Arabidopsis sepal morphogenesis.

iScience·2026
Same journal

Type 1 diabetes alters early macrophage-<i>Mycobacterium tuberculosis</i> transcriptional coordination during infection.

iScience·2026
查看所有相关文章

相关实验视频

Updated: Jun 22, 2025

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

1.6K

使用知识图嵌入的药物向相互作用预测.

Nan Li1, Zhihao Yang1, Jian Wang1

  • 1College of Computer Science and Technology, Dalian University of Technology, Dalian, China.

iScience
|July 2, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了TTModel,这是一个新的知识图嵌入方法,用于预测药物向相互作用 (DTI). 通过整合生物医学文本和类型信息,TTModel提高了DTI预测的准确性,优于现有的方法.

关键词:
生物化学 生化学计算数学是指计算数学.计算机科学 计算机科学

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

2.5K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.2K

相关实验视频

Last Updated: Jun 22, 2025

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

1.6K
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
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.2K

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现 药物发现

背景情况:

  • 药物向相互作用 (DTI) 的预测对于药物开发和重新定位至关重要.
  • 目前用于DTI预测的知识图法面临着数据稀疏性和不完整信息的挑战.
  • 知识图中的潜在类型信息通常被当前的DTI预测模型忽视.

研究的目的:

  • 提出TTModel,一种新的知识图嵌入模型,用于改进DTI预测.
  • 通过结合生物医学文本和类型信息来解决现有方法的局限性.
  • 为了提高计算DTI预测的准确性和稳定性.

主要方法:

  • 开发了TTModel,一个专门用于DTI预测的知识图嵌入模型.
  • 利用生物医学文本语义来丰富知识图表表示.
  • 嵌入了隐藏类型信息,以改善节点嵌入的学习.
  • 在两个公共数据集上对DTI预测模型进行了评估.

主要成果:

  • 在DTI预测方面,TTModel显著超过了最先进的方法.
  • 该模型通过有效地从文本语义和类型信息中学习,证明了更好的性能.
  • 实验结果验证了TTModel在基准数据集上的有效性.

结论:

  • TTModel为预测药物向相互作用提供了一种卓越的方法.
  • 整合文本和类型信息对于增强基于知识图的DTI预测至关重要.
  • 拟议的方法在药物发现和开发中推进了计算策略.