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相关概念视频

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....
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Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
349
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

6.7K
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...
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Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

4.8K
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...
4.8K
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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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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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...
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相关实验视频

Updated: Jan 14, 2026

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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自主监督的基于多视图的图表演示学习,用于药物相互作用预测.

Kuang Du1, Jing Du2, Zhi Wei1

  • 1Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.

Transactions on artificial intelligence
|October 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了自我监督多视图表示学习 (SMG-DDI) 以预测药物相互作用 (DDI). SMG-DDI有效地使用未标记的分子数据,优于目前用于DDI预测的方法.

关键词:
药物相互作用 药物相互作用阶层图表表示学习学习学习.分子结构信息是分子结构信息.自主监督学习学习

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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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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

Published on: May 21, 2018

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相关实验视频

Last Updated: Jan 14, 2026

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

19.4K
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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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

Published on: May 21, 2018

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习在药物发现中的作用

背景情况:

  • 药物相互作用 (DDI) 在多药学中构成重大风险,需要准确的预测方法.
  • 现有的DDI等级图形表示学习因实验数据稀缺和可能与监督方法过度匹配而面临局限性.
  • 监督模型未能利用大量未标记的公共分子数据集,阻碍了性能.

研究的目的:

  • 开发一种新的多视图图形表示学习方法,SMG-DDI,用于增强药物相互作用预测.
  • 通过利用未标记的分子数据集来克服DDI预测中的数据稀缺瓶.
  • 提高DDI预测模型的准确性和通用性.

主要方法:

  • 拟议的自主监督多视图表示学习 (SMG-DDI) 用于DDI预测.
  • 使用预先训练的图形卷积网络 (GCN) 进行互视分子图形表示学习 (原子作为节点,键作为边).
  • 捕获的视内分子相互作用和生成的药物嵌入物用于最终的DDI预测.

主要成果:

  • 与最先进的DDI预测方法相比,SMG-DDI在各种数据集尺度上表现优越.
  • 在小型,中型和大型测试数据集上分别实现了0.83,0.79和0.73的预测准确度.
  • 验证了分子结构信息在预测潜在的药物相互作用方面有显著的帮助.

结论:

  • 通过结合自主监督学习和多视图图表,SMG-DDI有效地解决了DDI预测中的数据限制.
  • 拟议的方法为准确可靠地预测药物相互作用提供了一个有希望的方法.
  • 通过高级图形表示学习利用未标记的分子数据,提高了DDI预测能力.