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相关概念视频

Free Energy01:21

Free Energy

51.7K
Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
51.7K
An Introduction to Free Energy01:05

An Introduction to Free Energy

10.8K
How can we compare the energy that releases from one reaction to that of another reaction? We use a measurement of free energy to quantitate these energy transfers. Scientists call this free energy Gibbs free energy (abbreviated with the letter G) after Josiah Willard Gibbs, the scientist who developed the measurement. According to the second law of thermodynamics, all energy transfers involve losing some energy in an unusable form such as heat, resulting in entropy. Gibbs free energy...
10.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

292
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
292
Work and Energy for Variable Forces01:10

Work and Energy for Variable Forces

5.6K
When an object is acted upon by a variable force, the amount of work done and the change in energy of the object can be more complex to calculate compared to when a constant force is applied. Work is the product of force and displacement, while energy is the capacity of a system to do work. When a constant force is applied to an object, the work done can be calculated as the product of the force and the distance moved in the direction of the force. However, when a variable force is applied, the...
5.6K
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

24.7K
The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
24.7K
Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

13.4K
The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
13.4K

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相关实验视频

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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将机器学习集成到自由能源扰乱工作流程中

Donald J M van Pinxteren1, Willem Jespers1

  • 1Department of Medicinal Chemistry, Photopharmacology and Imaging, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.

Journal of chemical information and modeling
|September 17, 2025
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 通过提高效率和准确性来增强药物设计的自由能量干扰 (FEP) 方法. 整合ML,深度学习 (DL) 和主动学习 (AL) 加快了蛋白质-连接体结合亲和力预测.

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Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
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Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 在科学领域的机器学习.

背景情况:

  • 自由能量扰动 (FEP) 是一种非常准确的方法,用于预测基于结构的药物设计中的蛋白质-连接体结合亲缘关系.
  • 然而,FEP的广泛采用受到大量计算成本和复杂的实施要求的阻碍.

研究的目的:

  • 本综述探讨了机器学习 (ML) 的整合,包括主动学习 (AL) 和深度学习 (DL),以增强FEP工作流程.
  • 其目标是提高药物发现中FEP应用的效率,可访问性,准确性和精度.

主要方法:

  • 该审查审查了在三个关键FEP领域的ML应用:采样策略,协议优化和力量场开发.
  • 积极学习 (AL) 指导分子选择,以减少虚拟选中的FEP计算.
  • 像AlphaFold这样的深度学习 (DL) 模型为FEP自动化准确的蛋白质 - 连接体复杂结构生成.

主要成果:

  • 机器学习集成显著减少了与FEP计算相关的计算负担和复杂性.
  • 深度学习 (DL) 方法简化了准确的蛋白质 - 连接体复杂结构的生成,绕过了传统的对接.
  • 机器学习衍生的神经网络潜力 (NNP) 提供了增强的力场精度,尽管计算需求增加.

结论:

  • 结合人类专业知识和ML工具的混合方法是加速基于FEP的药物发现的最有效策略.
  • 未来ML和FEP的跨学科发展将扩大计算机辅助药物设计在制药和材料科学中的影响.