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

Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

10.0K
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...
10.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.7K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.7K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.7K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.7K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

7.3K
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....
7.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

10.0K
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,...
10.0K

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

diPaRIS: Dynamic and Interpretable Protein-RNA Interactions Prediction With U-Shaped Network and Novel Structure Encoding.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

2OMe-LM: predicting 2'-O-methylation sites in human RNA using a pre-trained RNA language model.

Bioinformatics (Oxford, England)·2025
Same author

RNALoc-LM: RNA subcellular localization prediction using pre-trained RNA language model.

Bioinformatics (Oxford, England)·2025
Same author

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization.

IEEE journal of biomedical and health informatics·2024
Same author

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

Bioinformatics (Oxford, England)·2023
Same author

Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.

Briefings in bioinformatics·2022

相关实验视频

Updated: Jan 13, 2026

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

2.1K

动机GT-DTI:基于关键动机的图形转换器模型改善了药物向相互作用预测.

Wen Tian, Min Zeng, Jianxin Wang

    IEEE transactions on neural networks and learning systems
    |January 6, 2026
    PubMed
    概括

    MotifGT-DTI是一个新的模型,通过分析分子结构,准确地预测药物向相互作用. 它提供了可解释的结果,推动了药物发现和重新定位努力.

    科学领域:

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

    背景情况:

    • 药物向相互作用 (DTI) 对于药物发现和重新使用至关重要.
    • 现有的DTI预测方法往往缺乏解释性,并未充分利用分子结构信息.

    研究的目的:

    • 开发一个可解释的DTI预测模型,有效地利用药物和蛋白质分子结构.
    • 提高DTI预测的准确性和概括性,特别是在冷启动场景中.

    主要方法:

    • 拟议的MotifGT-DTI,是一种基于图形变压器 (GT) 的基于图形的新型模型.
    • 通过GT捕获复杂的分子图案,使用药物分子图形图案和蛋白质3D口袋子图.
    • 采用交叉注意的融合1D序列和3D结构蛋白质特征.
    • 连接药物-蛋白质结构关联与二线性注意力网络.

    主要成果:

    • 与最先进的基线相比,MotifGT-DTI在四个公共数据集中实现了更高的准确性.
    • 在三个冷启动场景中,在准确性,概括性和稳定性方面表现出竞争力.
    • 通过可视化成功识别了功能分子动图,并提供了可解释的预测.

    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

    3.6K
    Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
    05:50

    Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

    Published on: September 26, 2025

    1.4K

    相关实验视频

    Last Updated: Jan 13, 2026

    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

    2.1K
    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

    3.6K
    Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
    05:50

    Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

    Published on: September 26, 2025

    1.4K

    结论:

    • 动机GT-DTI代表了可解释的DTI预测的重大进步.
    • 该模型显示出在药物发现和重新利用方面具有强大的实际应用潜力.
    • 该方法有效地利用分子结构,提高DTI预测的准确性和可解释性.