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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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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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G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
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Prodrugs01:30

Prodrugs

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Prodrugs are a class of pharmaceutical compounds that undergo a biotransformation process within the body to be converted into a pharmacologically active drug. Prodrugs are designed to improve the therapeutic properties of the parent drug, such as enhancing bioavailability, increasing stability, or reducing toxicity. The concept of prodrugs revolves around modifying the chemical structure of the original drug to make it more effective or convenient for administration.
Prodrugs help overcome...
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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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EKGDR:一种基于端到端知识图的方法,用于计算药物重定位.

Javad Tayebi1, Bagher BabaAli1

  • 1School of Mathematics, Statistics and Computer Science, University of Tehran, Tehran 141556455, Iran.

Journal of chemical information and modeling
|March 14, 2024
PubMed
概括

药物重定向通过为现有药物寻找新的用途来加速新疗法. EKGDR是一种新的知识图方法,显著提高了药物与疾病相互作用预测的准确性.

科学领域:

  • 计算机化药物发现.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 传统的新药开发是漫长的,昂贵的,并且失败率很高.
  • 药物再利用提供了一种更有效和更具成本效益的替代方案,通过确定现有药物的新用途.
  • 计算药物设计的挑战包括数据异质性和有限的已知药物-疾病相互作用.

研究的目的:

  • 引入EKGDR,一个端到端的基于知识图的计算药物重定位方法.
  • 解决药物重用中的数据异质性和有限相互作用数据挑战.
  • 为了改善药物疾病相互作用的预测.

主要方法:

  • 利用药物知识图集药物相互作用,分类和分子描述符.
  • 使用图形神经网络来端到端嵌入知识图.
  • 通过在多跳路径上汇总关系信息来学习药物-疾病相互作用的意图.

主要成果:

  • 在预测药物与疾病相互作用方面,EKGDR取得了卓越的表现.
  • 实现了AUROC的0.9475,AUPRC的0.9490,和召回@200的0.8315.
  • 通过确定阿尔茨海默氏症和帕金森病的候选药物来证明其有效性.

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结论:

  • EKGDR代表了计算药物重新利用的重大进步.
  • 知识图和图形神经网络方法有效预测药物-疾病相互作用.
  • EKGDR显示出加速发现现有药物的新疗法应用的前景.