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相关概念视频

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Positive inotropic agents are commonly used as the first line of treatment for heart failure. One such agent is digoxin, derived from the genus Digitalis, which has been known for centuries but effectively utilized since 1785. However, these cardiac glycosides can have potentially toxic effects due to their mechanism of action, which involves inhibiting Na+/K+-ATPase and increasing contractility. Digoxin is absorbed orally and distributed in various tissues, including the CNS. It has a long...
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相关实验视频

Updated: Jul 1, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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使用基于注意力的图形神经网络预测分子的心脏毒性.

Tuan Vinh1, Loc Nguyen2, Quang H Trinh3

  • 1Department of Chemistry, Emory University, 201 Dowman Drive, Atlanta, Georgia 30322-1007, United States.

Journal of chemical information and modeling
|March 4, 2024
PubMed
概括
此摘要是机器生成的。

药物发现面临毒性挑战,阻碍了新药的开发. 我们基于注意力的图形神经网络有效预测心脏毒性,改善药物安全性评估.

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科学领域:

  • 药物的发现和开发.
  • 计算毒理学计算毒理学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 毒性问题严重阻碍了药物开发,导致高失败率和成本增加.
  • 药物诱导的心脏毒性是一种严重的不良影响,对癌症治疗特别有问题.
  • 预测心脏毒性的现有计算方法在性能和解释性方面存在局限性.

研究的目的:

  • 开发一个更有效的计算框架来预测分子心脏毒性.
  • 提高药物发现中的心脏毒性评估的准确性和可解释性.
  • 为研究人员提供一个用户友好的工具来评估潜在的药物心脏毒性.

主要方法:

  • 使用基于注意力的图形神经网络 (GNN) 架构.
  • 开发了一种用于分子心脏毒性预测的新型计算框架.
  • 根据现有的计算方法验证模型性能.

主要成果:

  • 提出的基于注意力的GNN框架在预测心脏毒性方面表现优越,与其他方法相比.
  • 实验结果证实了开发模型的稳定性和可靠性.
  • 该框架成功识别了潜在的心脏毒性分子.

结论:

  • 开发的计算框架为预测药物诱导的心脏毒性提供了更有效的解决方案.
  • 这种方法可以帮助在开发管道的早期降低候选药物的风险.
  • 已经创建了一个可访问的在线网络服务器,以促进研究人员使用这个预测模型.