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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: 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
The Two-State Receptor Model01:29

The Two-State Receptor Model

1.9K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
1.9K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

918
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...
918
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.7K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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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...
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相关实验视频

Updated: May 25, 2025

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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KGRDR:基于知识图和图规范化集成的深度学习模型,用于药物重新定位.

Huimin Luo1,2, Hui Yang1,2, Ge Zhang1,2

  • 1School of Computer and Information Engineering, Henan University, Kaifeng, China.

Frontiers in pharmacology
|February 26, 2025
PubMed
概括

这项研究介绍了KGRDR,这是一种用于预测药物与疾病相互作用的新型深度学习框架. 通过整合多相似性和知识图学习,KGRDR增强了药物重新定位,以更准确地发现治疗选择.

关键词:
生物医学知识图表药物重新定位 药物重新定位药物与疾病相互作用的预测和预测功能融合功能融合功能多重相似性的核聚变.

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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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.
  • 医学中的人工智能

背景情况:

  • 传统药物发现是昂贵和耗时的.
  • 药物重新定位提供了一个更快,更便宜的替代方案.
  • 预测新的药物疾病相互作用对于优化药物开发至关重要.

研究的目的:

  • 提出一个新的深度学习框架,KGRDR,用于预测潜在的药物-疾病相互作用.
  • 通过计算方法加速识别新的治疗选择.
  • 降低与药物开发相关的成本和风险.

主要方法:

  • 开发了一个深度学习框架 (KGRDR),集成多相似信息和知识图学习.
  • 应用了图形规范化的方法来融合药物和疾病相似性特征.
  • 从生物医学知识图 (KG) 中学习拓特征.
  • 使用基于注意力的特征融合方法.
  • 采用图形卷积网络来预测药物与疾病的关联.

主要成果:

  • 与现有的最先进的药物疾病预测方法相比,KGRDR表现优越.
  • 该框架有效地整合了各种相似性和拓特征.
  • 案例研究验证了KGRDR在识别新药与疾病相互作用方面的能力.

结论:

  • KGRDR是一种有效的深度学习框架,用于预测药物与疾病的相互作用.
  • 拟议的方法提高了药物重新定位的效率和准确性.
  • 韩国GRDR显示出加速发现新疗法应用的巨大潜力.