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相关概念视频

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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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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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.
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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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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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Drug-Receptor Bonds01:25

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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.
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相关实验视频

Updated: Sep 9, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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MVSGDR:用于药物重新定位的多视图堆叠图形卷积网络

Guosheng Gu1, Haowei Wu1, Haojie Han1

  • 1School of Computer Science and Technology, Guangdong University of Technology, Waihuan West Road 100, Guangzhou, 510006 Guangdong, China.

Briefings in bioinformatics
|September 2, 2025
PubMed
概括

这项研究引入了一种新的药物重新定位 (DR) 框架,即MVSGDR,以改善药物与疾病的相关性预测. MVSGDR有效地增强了特征表示和分析关系,优于现有的计算方法.

关键词:
药物重新定位药物与疾病的关联图形神经网络多视角学习负采样

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科学领域:

  • 计算生物学
  • 药理学
  • 网络科学

背景情况:

  • 药物重新定位 (DR) 是一种具有成本效益的药物开发策略.
  • 目前的计算 DR 方法很难将本地基层模式与全球网络语义结合起来.
  • 现有方法通常依赖于数据增强来解决药物疾病关联 (DDA) 中的信息缺口.

研究的目的:

  • 提出一个新的DR框架,多视图堆叠图形卷积网络 (MVSGDR),以克服当前计算DR方法的局限性.
  • 提高药物疾病关联 (DDA) 的预测准确度.

主要方法:

  • 开发了MVSGDR,一个包含三项创新的新型DR框架.
  • 多视图堆叠模块通过多跳社区交互的层次聚合来进行深度智能的功能增强.
  • 使用METIS分区的子图对DDA进行宽度分析的双层子图转换模块.
  • 通过合成负样本减轻样本不平衡的负样本平衡策略.

主要成果:

  • 在四个基准数据集的广泛十倍交叉验证实验中,MVSGDR表现出卓越的性能.
  • 与现有的DR方法相比,观察到具有统计意义的改善.
  • 案例研究成功地通过文献证据确定了以前未报告的DDA.

结论:

  • MVSGDR为药物重新定位提供了一个强大的新框架.
  • 提出的方法有效地将本地化基层结构模式与全球网络语义相结合.
  • MVSGDR显示出发现现有药物的新疗法的巨大潜力.