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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Drug Discovery: Overview01:26

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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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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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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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相关实验视频

Updated: Jun 20, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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有效的深度模型整体框架用于药物向相互作用预测.

Jinhang Wei1, Yangbin Zhu1, Linlin Zhuo1

  • 1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.

The journal of physical chemistry letters
|July 22, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了EADTN,这是一种用于预测药物向相互作用 (DTI) 的整体模型,可以提高准确性并减少虚假阴性. 欧洲药物发现网络提高了药物发现的可靠性和可解释性,优于现有的方法.

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科学领域:

  • 计算化学是一种计算化学.
  • 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:

  • 准确预测药物向相互作用 (DTI) 对于有效的药物开发至关重要.
  • 现有的用于DTI预测的深度学习模型面临着性能和错误负面的挑战.

研究的目的:

  • 提出EADTN,一种用于增强DTI预测的新型组合模型.
  • 提高DTI预测模型的可靠性和可解释性.
  • 通过实验性药物标相互作用识别来验证模型的有效性.

主要方法:

  • 开发了EADTN,一个集成模型,具有创新的特征适应,用于局部重量提取.
  • 实现了集群增强的参数微调,以减轻虚假负数.
  • 利用Shapley基于价值的分析来识别关键的药物亚结构,并提高模型的解释性.

主要成果:

  • 与最先进的模型相比,EADTN在各种数据集中表现出卓越的性能.
  • 该模型成功地确定了NQO1目标和药物SIRT-IN-1和LY2183240.0之间的潜在相互作用.
  • 湿实验室实验验证实了预测的药物向相互作用,证实了EADTN的可靠性.

结论:

  • 在DTI预测方面,EADTN提供了显著的进步,提高了准确性并减少了虚假负面.
  • 该模型的可解释性特征有助于理解药物机制和亚结构的重要性.
  • 在药物重新定位和加速药物发现管道的应用中,EADTN显示出前景.