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

12.4K
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...
12.4K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

4.7K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
4.7K
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

3.6K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
3.6K
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
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.1K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
7.1K
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

2.6K
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...
2.6K

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

Higher-Order Dynamic Disentangled Intent Sensing and Bidirectional Joint Updating Framework for NcRNA-Drug Resistance Association Prediction.

Journal of chemical information and modeling·2026
Same authorSame journal

STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.

Interdisciplinary sciences, computational life sciences·2026
Same author

SpaVGMC: A Unified Representation Learning Framework via Structural and Semantic Alignment in Spatial Transcriptomics.

Journal of chemical information and modeling·2026
Same author

MHNNMDA: multi-stage hypergraph neural network for predicting miRNA-disease association types.

Journal of computer-aided molecular design·2026
Same author

Prediction of multicategory miRNA-disease associations based on bidirectional hypergraph attention network and gated convolutional strategy.

Journal of computer-aided molecular design·2026
Same author

Two-Stage Multi-View Graph Spectral Clustering for Single-Cell RNA-Seq Data.

Current genomics·2026

相关实验视频

Updated: May 13, 2025

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.3K

IHDFN-DTI:可解释的混合深度特征融合网络用于药物向相互作用预测.

Yuanyuan Zhang1, Qihao Wang2, Ci'ao Zhang2

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266000, China. yyzhang1217@163.com.

Interdisciplinary sciences, computational life sciences
|May 12, 2025
PubMed
概括

本研究介绍了一种可解释的混合深度功能融合网络 (IHDFN),用于高效的药物向相互作用 (DTI) 预测. IHDFN增强了特征提取和融合,在DTI任务中表现优于现有的方法.

关键词:
药物目标相互作用混合深度特征融合混合动力可解释模块可以解释模块.蛋白质特征提取 提取

更多相关视频

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.6K
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

4.1K

相关实验视频

Last Updated: May 13, 2025

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.3K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.6K
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

4.1K

科学领域:

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

背景情况:

  • 传统的药物发现是昂贵和耗时的.
  • 计算药物向相互作用 (DTI) 预测提供了效率和降低成本.
  • 现有的DTI方法难以从蛋白质序列中有效地提取和融合特征.

研究的目的:

  • 开发一个可解释的混合深度特征融合网络 (IHDFN),以改进DTI预测.
  • 为了应对结合浅层和深层蛋白质特征的挑战,并提高特征融合复杂性.
  • 改善药物特征表示和模型稳定性.

主要方法:

  • 混合深度特征提取模块用于使用两个不同的视图的蛋白质序列.
  • 星网融合模型用于高效的浅层和深层功能集成.
  • 图形卷积网络 (GCN) 具有药物特征的剩余连接和层规范化.
  • 整合多式药物和蛋白质特征的注意力机制.

主要成果:

  • 在三个数据集上,IHDFN表现出了卓越的性能和稳定性.
  • 提出的方法有效地结合了浅层和深层蛋白质特征.
  • 实现了增强的特征表示和融合复杂性.
  • 在DTI预测中的可解释性是通过注意力机制实现的.

结论:

  • IHDFN为药物向相互作用预测提供了一个有希望和有效的解决方案.
  • 该模型能够整合多种特征并提供可解释性,这是一个重大进步.
  • 这些发现强调了IHDFN在加速药物发现管道中的潜力.