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相关概念视频

Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.0K
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....
5.0K
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

2.8K
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...
2.8K
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

8.3K
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...
8.3K
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

331
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...
331
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

3.7K
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...
3.7K
Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

2.4K
Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
2.4K

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相关实验视频

Updated: Jun 11, 2025

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

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一种基于知识图的方法,用于用对比学习来预测药物相互作用.

Jian Zhong, Haochen Zhao, Qichang Zhao

    IEEE/ACM transactions on computational biology and bioinformatics
    |October 9, 2024
    PubMed
    概括

    本研究介绍了KG-CLDDI,这是一种新的方法,通过整合知识图和对比学习特征来提高药物相互作用 (DDI) 预测的准确性. KG-CLDDI显著提高了预测性能,特别是在诱导环境中,以获得更安全的药物治疗.

    科学领域:

    • 药理学和生物信息学 药理学和生物信息学
    • 人工智能在医学中的应用

    背景情况:

    • 准确预测药物相互作用 (DDI) 对于提高药物治疗的安全性和有效性至关重要.
    • 目前使用生物医学知识图的DDI预测模型在特征提取精细化方面存在局限性.

    研究的目的:

    • 开发一种基于知识图的先进方法,用于多种类型的DDI预测.
    • 通过对比学习来提高药物-药物对嵌入的质量.

    主要方法:

    • 开发了KG-CLDDI,结合了药物知识聚合和知识和DDI图表的拓聚合特征.
    • 实现了对比式学习模块,用于高质量的嵌入,具有横向倒置和脱落.

    主要成果:

    • 在传导和感应设置中,KG-CLDDI在最先进的模型中表现出优越的性能.
    • 在诱导设置中,KG-CLDDI实现了AUC和AUPR分别提高了17.49%和24.97%.
    • 剥离分析和案例研究证实了该方法的有效性.

    结论:

    • 在DDI预测准确度方面,KG-CLDDI提供了显著的进步.
    • 这些发现突显了KG-CLDDI在改善临床决策支持和药物开发方面的潜力.

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    Diagonal Method to Measure Synergy Among Any Number of Drugs
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    Diagonal Method to Measure Synergy Among Any Number of Drugs
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