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相关概念视频

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Molecular Models02:00

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

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Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
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Theories of Dissolution: Diffusion Layer Model01:15

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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
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Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
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The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.
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相关实验视频

Updated: Jul 16, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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两个为一个:粗粒度分子动力学的扩散模型和力场.

Marloes Arts1, Victor Garcia Satorras2, Chin-Wei Huang2

  • 1Department of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen 2100, Denmark.

Journal of chemical theory and computation
|September 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于使用扩散模型学习粗粒度 (CG) 力场,从而简化训练. 这种方法准确地模拟了蛋白质动态和折叠事件,而不需要力数据.

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科学领域:

  • 计算生物学 计算生物学
  • 生物物理学的生物物理.
  • 机器学习 机器学习

背景情况:

  • 粗粒度 (CG) 分子动力学对于模拟大型生物系统至关重要.
  • 准确的CG力场是必不可少的,但发展起来具有挑战性.
  • 当前的方法通常需要在训练期间进行复杂的力输入.

研究的目的:

  • 开发一种用于学习CG力场的新方法,而不需要输入力.
  • 为了利用基于分数的生成模型,特别是扩散模型,用于CG力场学习.
  • 为了证明这种方法在模拟生物系统中的有效性.

主要方法:

  • 从分子动力学模拟中对蛋白质结构进行扩散生成模型的训练.
  • 使用训练模型的得分函数作为近似的CG力场.
  • 应用学习的力场来模拟CG分子动力学.

主要成果:

  • 学习得分函数有效地接近一个CG力场.
  • 该方法使CG分子动力学模拟能够在没有明确的力输入的情况下进行.
  • 在模拟高达56个氨基酸的蛋白质系统中提高了性能.
  • 准确地复制CG平衡分布.
  • 在全原子模拟中观察到的蛋白质折叠动态的保存.

结论:

  • 基于分数的扩散模型提供了一种简化和有效的方法来学习CG力场.
  • 这种方法提升了CG分子动力学对生物过程的模拟能力.
  • 这种方法有望有效地研究更大,更复杂的生物系统.