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相关概念视频

Drug-Receptor Interactions01:29

Drug-Receptor Interactions

4.8K
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....
4.8K
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

2.7K
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...
2.7K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

930
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...
930
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
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.4K
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...
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相关实验视频

Updated: May 29, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

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

Published on: May 27, 2021

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一个基于多视图特征的可解释深度学习框架,用于药物相互作用预测.

Zihui Cheng1, Zhaojing Wang2,3, Xianfang Tang1,4

  • 1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Sunshine Avenue, Wuhan, 430200, China.

Interdisciplinary sciences, computational life sciences
|February 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了MI-DDI,这是一个新的深度学习框架,通过整合多视图功能来预测药物相互作用 (DDI). MI-DDI提高了预测准确性和可解释性,优于现有方法.

关键词:
药物相互作用 药物相互作用可以解释的预测预测.多视图功能提取多视图功能提取

更多相关视频

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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相关实验视频

Last Updated: May 29, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

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

Published on: May 27, 2021

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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
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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.
  • 药理学 药理学是指药理学的学科.
  • 医学中的人工智能

背景情况:

  • 药物相互作用 (DDI) 存在重大风险,需要准确的预测方法.
  • 当前的计算DDI预测模型通常依赖于有限的单视图功能,阻碍了性能和可解释性.
  • 在多视图基于特征的DDI预测的可解释性研究中存在一个差距.

研究的目的:

  • 开发一个基于多视图特征的可解释的深度学习框架,用于增强DDI预测.
  • 通过整合各种分子特征来提高计算DDI预测的准确性和可解释性.
  • 解决当前DDI预测模型中单视图方法的局限性.

主要方法:

  • 使用传递信息的神经网络 (MPNN) 来从分子图中提取原子视图特征.
  • 利用变压器编码器从药物SMILES字符串中学习子结构视图嵌入.
  • 集成的原子和子结构特征成为一个整体的药物嵌入矩阵,用于多视图深度学习框架 (MI-DDI).
  • 开发了一个交互模块,用于可解释的DDI预测和重量矩阵构建.

主要成果:

  • 在BIOSNAP和DrugBank数据集上,MI-DDI表现优于现有的基准标准,平均改善率分别为3%和1%.
  • 实验证实了原子视图信息对于DDI预测准确性的重要性.
  • 拟议的交互模块有效地学习了对于精确和可解释的DDI预测至关重要的信息.

结论:

  • 通过利用多视图功能来提高准确性和可解释性,MI-DDI在DDI预测方面取得了重大进展.
  • 该框架为了解药物相互作用提供了一个可操作的途径,这对临床安全至关重要.
  • 这些发现突显了多视角深度学习在制药研究和药物安全方面的潜力.