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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.7K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
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...
12.4K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

117
Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
117
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.7K
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.
In...
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

932
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
932

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相关实验视频

Updated: Jun 2, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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基于功率图和word2vec的药物标结合亲和力预测.

Jing Hu1,2,3, Shuo Hu4, Minghao Xia4

  • 1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China. hujing@wust.edu.cn.

BMC medical genomics
|January 13, 2025
PubMed
概括

准确的药物向亲和力预测对于药物开发至关重要. 一个新的模型,WPGraphDTA,使用图形神经网络和Word2vec来改进药物向相互作用的预测,减少开发时间和成本.

关键词:
药物重定位是为了重新定位药物.药物向的亲和力 药物向的亲和力图表神经网络的神经网络电力图表上的权力图表.在Word2vec中使用.

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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.
  • 机器学习在药物发现中的作用

背景情况:

  • 药物和蛋白质标相互作用,影响生理和代谢功能.
  • 准确预测药物向相互作用对于有效的药物开发至关重要.
  • 机器学习方法越来越多地被用于加速药物发现和降低成本.

研究的目的:

  • 提出一种名为WPGraphDTA的新型药物向亲和力预测模型.
  • 利用功率图和Word2vec技术来提高预测准确度.
  • 减少与药物重定位和新药开发相关的时间和费用.

主要方法:

  • 利用图形神经网络在功率图模块中提取药物分子特征.
  • 使用Word2vec方法获得蛋白质特征.
  • 整合药物和蛋白质特征,然后通过三个完全连接的层进行处理,以预测亲和力.

主要成果:

  • 拟议的WPGraphDTA模型在基准数据集上显示出强大的预测性能.
  • 基于回归的药物向 afinity 预测证明更代表结合能力.
  • 该模型在戴维斯和基巴数据集上取得了良好的预测性能.

结论:

  • 通过结合图形神经网络和Word2vec.,WPGraphDTA有效地预测药物标亲和力.
  • 该模型的表现表明它有可能简化药物开发管道.
  • 使用WPGraphDTA准确的亲和力预测可以显著减少药物开发时间表和成本.