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Crystal Growth: Principles of Crystallization01:25

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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.
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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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Updated: May 21, 2025

Optimizing the Growth of Endothiapepsin Crystals for Serial Crystallography Experiments
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Can Coarse-Grained Molecular Dynamics Simulations Predict Pharmaceutical Crystal Growth?

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
Summary
This summary is machine-generated.

Coarse-grained molecular dynamics simulations can predict pharmaceutical crystal growth. A finer-grained MARTINI approach with Particle Swarm Optimization (PSO) showed superior accuracy for carbamazepine facet growth prediction.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Crystallography

Background:

  • Predicting crystal growth is crucial for pharmaceutical development.
  • Coarse-grained (CG) molecular dynamics (MD) offers a computationally efficient alternative to atomistic simulations.
  • Evaluating CG methods for predicting relative crystal facet growth rates is essential.

Purpose of the Study:

  • To assess the ability of two CG MD strategies to predict pharmaceutical crystal facet growth rates.
  • To compare CG simulation results with established theories like Bravais-Friedel-Donnay-Harker (BFDH) and attachment energy (AE).

Main Methods:

  • Applied two CG strategies: MARTINI-level mapping with Particle Swarm Optimization (PSO) and a coarser-grained Iterative Boltzmann Inversion (IBI) method.
  • Used CG force fields to simulate crystal growth from the melt for phenytoin and carbamazepine.
  • Compared simulation predictions with BFDH and AE theories.

Main Results:

  • Both CG strategies successfully stabilized crystal structures and predicted growth from the melt using modest computational resources.
  • The finer-grained PSO-generated force field (FF) using MARTINI mapping demonstrated superior accuracy in predicting facet growth rates and crystal morphology for carbamazepine.
  • While most methods performed well for phenytoin, the IBI-generated model showed limitations.

Conclusions:

  • CG MD simulations are viable tools for predicting pharmaceutical crystal growth.
  • The choice of CG strategy significantly impacts prediction accuracy, with finer-grained approaches like MARTINI-PSO showing better performance for complex morphologies.