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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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Drug Biotransformation: Overview01:16

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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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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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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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Drug Metabolism: Phase II Reactions01:14

Drug Metabolism: Phase II Reactions

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Phase II reactions are essential for the detoxification and elimination of drugs from the body. These reactions involve the conjugation of parent drugs or their phase I metabolites with endogenous molecules, resulting in more hydrophilic drug conjugates. The primary conjugation reactions in this phase are sulfation and glucuronidation. Both sulfation and glucuronidation typically produce biologically inactive metabolites. However, in some cases involving prodrugs, active metabolites may be...
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Principles of Drug Action01:24

Principles of Drug Action

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Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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HeteroKGRep:基于异质知识图的药物重新定位.

Ribot Fleury T Ceskoutsé1, Alain Bertrand Bomgni2,3, David R Gnimpieba Zanfack4

  • 1Ecole Nationale Supérieure Polytechnique, University of Yaounde I, P.O. Box. 8390, Yaoundé, Cameroon.

Knowledge-based systems
|November 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了HeteroKGRep,一种使用多种生物医学数据的新型药物重新定位模型. 它有效地确定了现有药物的新治疗用途,提高了药物发现效率.

关键词:
生物医学异质图表异质图表深度学习是一种深度学习.药物重用是为了改变药物的用途.

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 计算药理学是一种计算药理学.
  • 药物发现 药物发现

背景情况:

  • 药物开发是漫长而昂贵的,专利复杂性阻碍了创新.
  • 现有的药物重新定位模型通常依赖于有限的,均的数据源.
  • 异质生物医学知识图为药物重新定位提供了更丰富的数据景观.

研究的目的:

  • 提出 HeteroKGRep,一种新的药物重新定位模型,利用异构的生物医学知识图.
  • 克服以前依赖于均数据的模型的局限性.
  • 加强对现有药物的新疗法应用的发现.

主要方法:

  • HeteroKGRep采用多步框架,包括相似度图生成和SMOTE过量采样.
  • 它使用异质图神经网络来生成节点序列.
  • 药物和疾病嵌入被提取用于预测重新利用机会.

主要成果:

  • HeteroKGRep以99%的精度,95%的AUC ROC和94%的平均精度实现了最先进的性能.
  • 该模型有效地利用多种知识源来丰富表达式学习.
  • 与现有的均质方法相比,它表现出优越的性能.

结论:

  • HeteroKGRep为以知识为导向的药物重新定位建立了一个有希望的新范式.
  • 该模型可以发现新的药物疾病关联,补充新的药物开发.
  • 在异质图表中利用多模式生物医学数据可以增强药物重定向策略.