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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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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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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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Protein-Drug Binding: Determination Methods01:22

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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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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-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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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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MMPD-DTA:将多模式深度学习与口袋药物图表集成,用于药物目标绑定亲和度预测.

Guishen Wang1, Hangchen Zhang1, Mengting Shao2

  • 1College of Computer Science and Engineering, Changchun University of Technology, North Yuanda Street No. 3000, Jilin 130012, China.

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概括
此摘要是机器生成的。

通过新的MMPD-DTA模型,预测药物标结合亲和力 (DTA) 得到了改进. 它使用多模式深度学习来整合全球和本地目标和药物信息,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.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 准确预测药物标结合亲和力 (DTA) 对于有效的药物发现至关重要.
  • 现有的方法很难完全捕获全球和本地信息,并有效地模拟口袋功能.
  • 在原子和全球层面上了解目标药物相互作用是改善DTA预测的关键.

研究的目的:

  • 提出一种新的多式联网深度学习模型,MMPD-DTA,用于增强DTA预测.
  • 解决在DTA预测中考虑全球/本地信息和建模口袋特征的局限性.
  • 开发一个综合多种数据模式的模型,以进行全面的目标药物相互作用分析.

主要方法:

  • 开发了MMPD-DTA模型,集成了来自目标,口袋和药物的图形和序列数据.
  • 引入了一种新的口袋药物 (PD) 图表,以建模目标和药物之间的原子相互作用.
  • 使用GraphSAGE用于PD图表学习,用于目标序列的变压器,以及用于药物图表的图形同型网络,其次是MLP预测.

主要成果:

  • 与基线方法相比,MMPD-DTA在三个真实数据集上表现出更高的性能.
  • 废弃性研究验证了MMPD-DTA模型中的单个成分的贡献和有效性.
  • 该模型成功地集成了多式联接表示,用于准确的结合亲和力预测.

结论:

  • MMPD-DTA模型在预测药物标结合亲缘关系方面取得了重大进展.
  • 整合多式联络数据和新型的PD图有效捕捉复杂的目标药物相互作用.
  • 拟议的方法为计算药物发现研究提供了一个强大的框架.