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Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

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Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
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Recrystallization: Solid–Solution Equilibria01:10

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Recrystallization is a purification technique used to separate impurities from solid compounds. In this technique, no chemical reactions occur. Instead, it exploits physical properties only, specifically, the solubility differences between the desired compound and impurities, either at a single temperature or at different temperatures, and under other selected conditions. The solid-solution equilibrium (solubility equilibrium) of each component in the solution represents a binary phase...
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Optimizing the Growth of Endothiapepsin Crystals for Serial Crystallography Experiments
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粗粒度分子动力学模拟可以预测制药水晶的生长吗?

Linghao Shi1, Futianyi Wang2, Taraknath Mandal3

  • 1Macromolecular Science and Engineering Program, University of Michigan, Ann Arbor, Michigan 48109, United States.

Journal of chemical theory and computation
|March 17, 2025
PubMed
概括

粗粒度分子动力学模拟可以预测制药水晶的生长. 使用微粒群优化 (PSO) 的更细粒度的MARTINI方法显示了对卡巴马西平面增长预测的卓越准确性.

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科学领域:

  • 计算化学是一种计算化学.
  • 材料科学 是一种材料科学.
  • 晶体学 晶体学是指结晶学.

背景情况:

  • 预测晶体生长对于制药开发至关重要.
  • 粗粒度 (CG) 分子动力学 (MD) 为原子模拟提供了一个计算效率高的替代方案.
  • 评估CG方法来预测相对晶体面增长率是必不可少的.

研究的目的:

  • 评估两种CG MD策略在预测制药晶体面增长率方面的能力.
  • 为了将CG模拟结果与诸如Bravais-Friedel-Donnay-Harker (BFDH) 和附着能量 (AE) 等既有理论进行比较.

主要方法:

  • 应用了两种CG策略:使用粒子群优化 (PSO) 的MARTINI级映射和粗粒度的代博尔兹曼倒置 (IBI) 方法.
  • 使用CG力场来模拟化中的晶体生长,用于化和碳胺.
  • 将模拟预测与BFDH和AE理论进行比较.

主要成果:

  • 这两种CG策略都成功地稳定了晶体结构,并使用适度的计算资源预测了化的增长.
  • 使用MARTINI映射的细粒度PSO产生的力场 (FF) 在预测卡巴马西平的面生长率和晶体形态方面表现出卓越的准确性.
  • 虽然大多数方法对氨酸的表现都很好,但IBI生成的模型显示出局限性.

结论:

  • CG MD模拟是预测制药晶体生长的可行工具.
  • 选择CG策略显著影响预测准确性,像MARTINI-PSO这样的细粒度方法对复杂形态表现更好.