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相关概念视频

Structure-Activity Relationships and Drug Design01:28

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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 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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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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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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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.
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一个有效的多任务学习框架用于药物重新使用,基于图形表示学习.

Shengwei Ye1, Weizhong Zhao1, Xianjun Shen1

  • 1Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China.

Methods (San Diego, Calif.)
|July 29, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的基于图形的多任务学习框架,用于预测药物-疾病关联 (DDAs) 的药物重用. 该方法有效地解决了数据稀缺问题,提高了药物发现效率.

关键词:
药物重新定位是药物重新定位.药物与疾病的关联预测预测图表 卷积网络 卷积网络不同质的信息网络 不同质的信息网络多任务学习是多任务学习.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 药物重新用途通过确定现有药物的新用途来加速药物发现.
  • 传统的药物疾病关联 (DDA) 预测方法在稀疏的数据上扎,限制了它们的有效性.
  • 提高DDA预测对于有效的药物重定位至关重要.

研究的目的:

  • 提出一种新的多任务学习框架,以提高药物与疾病相关性 (DDA) 的预测.
  • 通过先进的计算方法,提高药物再利用的准确性和效率.
  • 解决现有的药物重定向预测模型中数据稀缺的挑战.

主要方法:

  • 构建了一个整合多个生物数据集的异质信息网络.
  • 采用图形卷积网络 (GCNs) 来学习低维节点表示.
  • 利用多任务学习框架与辅助任务来增强DDA预测.

主要成果:

  • 拟议的框架在识别药物疾病关联方面表现出显著的有效性.
  • 该方法成功地解决了已知的药物疾病关联的稀疏性问题.
  • 实验结果验证了与现有方法相比,优越的预测性能.

结论:

  • 基于图形表示学习的新型多任务学习框架对药物重用有效.
  • 这种方法为克服DDA预测中的数据稀疏性提供了一个有希望的解决方案.
  • 这些发现有助于通过更准确,更有效的重定位策略推动药物发现.