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相关概念视频

Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

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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...
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An Introduction to Free Energy01:05

An Introduction to Free Energy

8.3K
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...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.8K
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:
12.8K
Free Energy and Equilibrium00:55

Free Energy and Equilibrium

6.1K
The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
The reaction quotient, Q, is a convenient measure of the...
6.1K
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...
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Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

11.1K
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:
 
where R is the gas constant (8.314 J/K·mol), T is the absolute temperature in kelvin, and Q is the reaction quotient. This equation may be used to predict the spontaneity of a process under any given set of conditions.
Reaction Quotient...
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相关实验视频

Updated: Jun 13, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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基于状态函数的校正:一种简单有效的自由能量校正算法,用于大规模相对约束的自由能量计算.

Runduo Liu1, Yijun Lai1, Yufen Yao1

  • 1State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.

The journal of physical chemistry letters
|June 3, 2025
PubMed
概括

一个新的基于状态函数的校正 (SFC) 算法改进了用于药物发现的自由能量扰动 (FEP) 计算. 这种方法有效地纠正大规模相对约束自由能量 (RBFE) 预测中的计算错误,而无需循环识别.

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科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 分子建模分子建模

背景情况:

  • 基于自由能量扰动的相对结合自由能量 (FEP-RBFE) 计算在药物发现中至关重要.
  • 固有的计算错误需要纠正,以提高预测准确度.
  • 传统的基于循环的校正方法面临着可扩展性问题,随着网络规模的增加.

研究的目的:

  • 引入一个高效和可扩展的算法来纠正FEP-RBFE计算.
  • 在大型药物发现屏幕中克服传统方法的计算瓶.
  • 提高FEP-RBFE的准确性和适用性,以优化高吞吐率的.

主要方法:

  • 开发了基于状态函数的校正 (SFC) 算法,利用自由能量的状态函数属性.
  • SFC避免了在扰动图中需要计算上昂贵的循环识别.
  • 算法的计算成本与边缘和分子的数量 (O(P × N)) 线性地变大.

主要成果:

  • 与基于图的方法不同,SFC在不断增加的图形大小中展示了一致的计算效率.
  • 该算法有效地处理大型扰动网络,包括多达5万个分子的网络.
  • 纳入不确定性意识加权进一步提高了校正性能.

结论:

  • SFC提供了一种计算效率高且可扩展的解决方案,用于纠正FEP-RBFE计算.
  • 该方法非常适合用于现代药物发现中的高通量FEP-RBFE应用.
  • 在优化过程中,SFC提高了FEP-RBFE的可靠性.