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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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Targets for Drug Action: Overview01:26

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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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Drugs Affecting Neurotransmitter Release or Uptake01:21

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Certain drugs can affect how neurotransmitters called catecholamines, are released or taken back up in the adrenergic neuron. They can have different effects on the body's sympathetic transmission. Reserpine, a natural compound found in the Rauwolfia shrub, blocks a transporter called vesicular monoamine transporter (VMAT), which leads to a buildup of catecholamines in the cell and reduces sympathetic transmission. Another drug called guanethidine works in multiple ways, including blocking...
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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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Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
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Drug-Receptor Interactions01:29

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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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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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通过使用多层次的注意力网络,为药物重新定位提供了短暂的链接预测框架.

Chenglin Yang1, Xianlai Chen2, Jincai Huang2

  • 1Big Data Institute, Central South University, Changsha, 410083, China; School of Life Sciences, Central South University, Changsha, 410083, China.

Computers in biology and medicine
|January 20, 2024
PubMed
概括

这项研究引入了一种用于药物重定向的新型元学习框架,增强了医疗知识图表中的几次拍摄链接预测. 多层次关注网络有效地识别新的药物指示,克服数据稀疏性的挑战.

关键词:
药物重新定位是药物重新定位.短时间的链接预测.模型无意识的超级学习多层次的注意力网络.设置变压器的变压器

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科学领域:

  • 计算生物学 计算生物学
  • 药理学 药理学是指药理学的学科.
  • 人工智能的人工智能

背景情况:

  • 药物的重新用途加速了对现有药物的新治疗用途的发现.
  • 医学知识图表中的链接预测模型药物-疾病关系.
  • 由于新药与疾病的联系很罕见,近距离学习至关重要.

研究的目的:

  • 为药物重用开发一个有效的几次射击链接预测框架.
  • 为了应对医疗知识图表中稀少的数据和弱相互作用所带来的挑战.
  • 提高识别新药指示的准确性和效率.

主要方法:

  • 一个元学习框架,集成一个多层次的注意网络 (MLAN).
  • 使用门口机制和图表注意力网络来过噪音并突出显示相关的邻里信息.
  • 采用了一个集成变压器来进行强大的三重级交互学习,并采用了一个模型不可知的元学习策略来进行快速优化.

主要成果:

  • 拟议的基于MLAN的框架在最先进的少数拍摄链接预测方法上表现出了显著的优势.
  • 在专门的几次射击医学链接预测数据集 (COVID19-One,BIOKG-One) 上取得了卓越的性能.
  • 验证了框架在低数据场景中捕获有价值信息的能力.

结论:

  • 统一的meta-learning框架有效地解决了少数注射药物重用方面的挑战.
  • MLAN方法为预测新型药物疾病关系提供了宝贵的见解.
  • 这种方法为通过先进的人工智能技术加速药物发现提供了有希望的方向.