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相关概念视频

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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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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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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Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

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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...
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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

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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.
In...
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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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相关实验视频

Updated: Jun 24, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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知识图的卷积网络与对药物重新定位的启发式搜索.

Xiang Du1,2, Xinliang Sun1, Min Li1

  • 1School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.

Journal of chemical information and modeling
|June 5, 2024
PubMed
概括

药物重新定位通过将现有药物重新定位为新用途来加速药物发现. 我们的KGCNH模型有效地使用生物医学知识图表和启发式搜索预测药物疾病关联,优于现有方法.

科学领域:

  • 生物医学信息学是生物医学信息学.
  • 计算生物学是一种计算生物学.
  • 药物发现 药物发现

背景情况:

  • 药物重新定位为传统药物开发提供了具有成本效益和更安全的替代方案.
  • 生物医学知识图集各种数据,为先进的药物重新定位策略提供了机会.
  • 现有的方法可能无法充分利用复杂的生物知识图中的语义和拓信息.

研究的目的:

  • 提出一个新的知识图卷积网络与启发式搜索 (KGCNH) 预测药物疾病关联.
  • 从生物医学知识图表中增强语义和拓信息的利用.
  • 提高药物重新定位预测的准确性和稳定性.

主要方法:

  • 开发了一个关系意识的注意力机制,以基于关系来权衡邻近实体.
  • 实现基于Gumbel-Softmax的启发式搜索模块,以探索最佳的药物和疾病嵌入.
  • 利用邻近聚合用于实体嵌入,并使用基于特征的增强视图用于模型规范化.

主要成果:

  • 与现有方法相比,KGCNH在两个基准数据集上表现出优异的性能.
  • 涉及和奎胺的案例研究验证了KGCNH识别实际药物疾病关联的能力.
  • 该模型有效地捕捉了生物医学知识图的语义和拓特征.

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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
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Last Updated: Jun 24, 2025

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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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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

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结论:

  • 通过利用生物医学知识图表,KGCNH为药物重新定位提供了有效的框架.
  • 建议的启发式搜索和注意力机制提高了药物疾病关联的预测.
  • 这种方法具有显著的潜力,可以加速对现有药物的新疗法指示的识别.