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相关概念视频

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
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

254
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...
254
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

8.1K
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...
8.1K
Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

2.3K
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...
2.3K
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 Interactions01:29

Drug-Receptor Interactions

4.6K
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....
4.6K

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相关实验视频

Updated: May 9, 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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通过双层特征聚合和基于知识图的深度神经网络进行协同药物组合预测.

Ying Zuo, Yan Zhang, Li Wang

    IEEE journal of biomedical and health informatics
    |May 6, 2025
    PubMed
    概括

    这项研究引入了LGSyn,这是一种用于预测癌症治疗中协同作用的药物组合的新框架. LGSyn集成了本地和全球的生物特征,在准确性和稳定性方面超过现有方法.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 药理学 药理学是指药理学的学科.
    • 生物信息学是一种生物信息学.

    背景情况:

    • 确定协同作用的药物组合对于有效的癌症治疗至关重要,但在计算上具有挑战性.
    • 现有的方法往往缺乏全面的数据整合,忽视了复杂的生物相互作用.
    • 需要结合固有的药物/细胞系特性和更广泛的生物关系,以准确地预测协同作用.

    研究的目的:

    • 开发一个新的计算框架,LGSyn,用于预测协同药物组合.
    • 整合各种生物数据,包括本地和全球特征,以提高预测准确度.
    • 用最先进的模型来评估 LGSyn 的性能.

    主要方法:

    • LGSyn集成了局部特征 (分子指纹,描述符,基因表达) 和全球特征 (药物-蛋白质,蛋白质-细胞系,蛋白质-蛋白质,细胞系-组织相互作用).
    • 为了有效地结合本地和全球信息,开发了三种融合策略.
    • 一个深度神经网络被用来训练和协同预测,使用融合的功能.

    主要成果:

    • 拟议的LGSyn框架在预测药物协同作用方面表现出卓越的准确性和稳定性.
    • LGSyn的性能超过了当前最先进的计算方法.
    • 实验结果验证了整合多视角生物数据的有效性.

    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 Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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    相关实验视频

    Last Updated: May 9, 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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    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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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

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    结论:

    • 通过利用全面的生物知识,LGSyn提供了一种强大而有效的方法来预测协同药物组合.
    • 地方和全球特征的整合显著提高了协同预测的准确性和稳定性.
    • 开发的框架有可能推进癌症治疗策略.