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相关概念视频

Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

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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...
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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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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,...
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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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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: 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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Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
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相关实验视频

Updated: Jan 14, 2026

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
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MDL-HTI:一种多模式深度学习方法,用于预测草药-目标相互作用.

Lianzhong Zhang1, Xiumin Shi2, Xiaohong Deng3

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.

Interdisciplinary sciences, computational life sciences
|October 28, 2025
PubMed
概括

我们开发了MDL-HTI,这是中国传统医学 (TCM) 的新计算框架,集成图形学习和生物数据来预测草药向相互作用 (HTIs). 这种方法增强了对TCM药理学的理解,并加速了药物发现.

关键词:
草药-目标相互作用预测预测.不同质的图形是不同的图形.多模式深度学习 (deep learning) 是一种多模式深度学习.网络药理学 网络药理学传统的中国医学是传统的中国医学.

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科学领域:

  • 计算药理学是一种计算药理学.
  • 生物信息学是一种生物信息学.
  • 传统中医 (TCM) 研究的研究.

背景情况:

  • 传统中医 (TCM) 提供了独特的治疗原则和庞大的药用资源,吸引了全球医疗的兴趣.
  • 了解草药向相互作用 (HTIs) 对于阐明TCM的药理机制和治疗效果至关重要.
  • 目前用于识别HTIs的方法有限,并没有充分利用现有的生物数据.

研究的目的:

  • 开发一种新的计算框架,MDL-HTI,以更有效地预测草药点相互作用 (HTIs).
  • 将异质图形学习与多式生物数据相结合,以加强HTI识别.
  • 克服当前方法在利用全面的生物信息中对TCM研究的局限性.

主要方法:

  • 拟议的MDL-HTI,一个框架,结合异构图形学习 (使用多视图异构关系嵌入 - MV-HRE) 和生物多式联络信息网络.
  • MV-HRE从图表,元路径和社区中提取结构模式.
  • 一个具有自我注意力的关系预测网络动态地融合了用于HTI识别的特征.

主要成果:

  • 在预测HTIs方面,MDL-HTI显著超过了最先进的基线方法.
  • 案例研究验证证实了MDL-HTI的有效性,作为识别潜在的草药向相互作用的工具.
  • 该模型展示了复杂的草药-目标关系的强大预测能力.

结论:

  • 通过整合拓学习和多式生物数据,MDL-HTI为TCM药理学建立了一个新的计算范式.
  • 该框架为阐明TCM机制和发现多目标草药提供了一个强大的平台.
  • 在精密医学中,MDL-HTI具有潜在的应用,可以降低实验成本并改善治疗结果.