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相关概念视频

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
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

96
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
96
Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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...
12.9K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

223
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...
223
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

561
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
561
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.0K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.0K

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相关实验视频

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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TeM-DTBA:使用多种模式与拉索特征选择的时间效率高的药物标结合亲和力预测.

Tanya Liyaqat1, Tanvir Ahmad2, Chandni Saxena3

  • 1Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India. tanyaliyaqat791@gmail.com.

Journal of computer-aided molecular design
|September 30, 2023
PubMed
概括

这项研究引入了一种新的,时间效率高的方法来预测药物标结合亲和力,加速药物发现. 与现有方法相比,这种方法提高了准确性并减少了计算时间.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.药物发现 药物发现药物标结合 afinity 预测 药物标结合 afinity 预测多种模式多种方式.

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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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:

  • 药物发现依赖于理解药物向相互作用 (DTI).
  • 由于湿实验室实验的成本和时间限制,计算方法,特别是深度学习,越来越多地用于DTI预测.
  • 当前的计算DTI预测通常将问题视为二进制分类,忽视定量绑定亲和力,并且可以是计算密集的.

研究的目的:

  • 开发一种新的,时间效率高的计算方法,用于预测药物标结合亲和力.
  • 通过结合定量绑定亲和度和优化计算效率来解决现有方法的局限性.
  • 加速虚拟查和药物重新定位过程.

主要方法:

  • 引入时间效率高的多模式药物标结合亲和力 (TeM-DTBA) 方法.
  • 多模式数据的融合,包括复合结构和目标序列.
  • 应用拉索特征选择以减少维度和提高训练速度.

主要成果:

  • TeM-DTBA方法在基准数据集 (KIBA和戴维斯) 上显示出卓越的性能.
  • 在KIBA数据集中达到18.8%的平均平方误差,在戴维斯数据集中达到23.19%.
  • 由于特征选择,模型培训时间减少了50%以上.

结论:

  • TeM-DTBA方法准确地预测了药物标的结合亲和力.
  • 这种方法在精度和计算效率上比最先进的方法显著提高.
  • 这种方法可以加速药物发现管道,包括虚拟查和药物重新定位.