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相关概念视频

Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

368
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...
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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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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....
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Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

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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...
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Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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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...
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Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
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相关实验视频

Updated: Jun 28, 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

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一种网络增强方法,以识别假药药物相互作用.

Huan Wang, Ziwen Cui, Yinguang Yang

    IEEE/ACM transactions on computational biology and bioinformatics
    |April 18, 2024
    PubMed
    概括

    这项研究介绍了ANSM,一种通过识别和减少虚假的DDI来提高药物相互作用 (DDI) 网络准确性的新方法. ANSM增强了图形神经网络预测,以获得更安全的医疗保健.

    科学领域:

    • 药理学和化学信息学
    • 医疗保健中的人工智能
    • 网络科学 网络科学

    背景情况:

    • 准确的药物相互作用 (DDI) 检测对于医疗安全和药物监管至关重要.
    • 目前用于DDI预测的图形神经网络 (GNN) 模型受到DDI网络中的虚假链接的阻碍.
    • 数据错误和不正确的药物信息可能会损害基于GNN的DDI预测的准确性.

    研究的目的:

    • 提出ANSM,一种网络增强方法,用于识别和减弱DDI网络中的虚假链接.
    • 通过完善底层的DDI网络结构,提高基于GNN的DDI预测的准确性.
    • 为了应对影响计算DDI预测可靠性的数据不准确性的挑战.

    主要方法:

    • 在ANSM中集成了一个局部结构特征的特征提取器,一个使用网络信息来改进特征的网络优化器,以及一个用于识别虚假链接的歧视分类器.
    • 功能提取器捕获药物节点对之间的关系.
    • 网络优化器增强特征提取并减轻虚假DDI链接的影响,然后对潜在的虚假链接进行分类.

    主要成果:

    • 实验结果表明,ANSM有效地识别了虚假的药物相互作用.
    • 与现有的最先进的方法相比,拟议的方法在虚假DDI检测方面表现出更高的性能.

    更多相关视频

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

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    A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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    相关实验视频

    Last Updated: Jun 28, 2025

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

    Diagonal Method to Measure Synergy Among Any Number of Drugs

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

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

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

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  • ANSM成功地提高了DDI网络的准确性和可靠性.
  • 结论:

    • ANSM为净化DDI网络提供了强大的解决方案,从而提高了计算DDI预测的准确性.
    • 通过解决虚假链接,ANSM有助于更可靠的基于GNN的DDI预测模型,以提高患者安全.
    • 该方法在确保用于制药研究和临床实践的DDI数据完整性方面取得了重大进展.