A5014678969- Crystallography and molecular interactions
- Crystallization and Solubility Studies
- Computational Drug Discovery Methods
- X-ray Diffraction in Crystallography
- Analytical Chemistry and Chromatography
- Protein Structure and Dynamics
- Mass Spectrometry Techniques and Applications
- Nuclear Materials and Properties
- Advanced Chemical Physics Studies
- Catalysis and Oxidation Reactions
- Zeolite Catalysis and Synthesis
- Phase Equilibria and Thermodynamics
- Microstructure and Mechanical Properties of Steels
- High Temperature Alloys and Creep
- Enzyme Structure and Function
- Machine Learning in Materials Science
- Chemistry and Chemical Engineering
Cambridge Crystallographic Data Centre
2023-2024
University College London
2020-2024
Thomas Young Centre
2020-2022
Eli Lilly (United States)
2020
A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems varying complexity: a silicon and iodine-containing molecule, copper coordination complex, near-rigid cocrystal, polymorphic small agrochemical, highly flexible drug candidate, morpholine salt. In this first two parts focusing on generation methods, many (CSP) methods performed well for but agrochemical compound, successfully reproducing...
A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking structures order stability. exercise involved standardized sets seeded from a range generation methods. Participants 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived empirical data or quantum chemical calculations, and various...
A workflow for the digital design of crystallization processes starting from chemical structure active pharmaceutical ingredient (API) is a multistep, multidisciplinary process. simple version would be to first predict API crystal and, it, corresponding properties solubility, morphology, and growth rates, assuming that nucleation controlled by seeding, then use these parameters This usually an oversimplification as most APIs are polymorphic, stable alone may not have required development...
Crystal structure prediction methods are prone to overestimate the number of potential polymorphs organic molecules. In this work, we aim reduce overprediction by systematically applying molecular dynamics simulations and biased sampling cluster subsets structures that can easily interconvert at finite temperature pressure. Following approach, rationally predicted putative in crystal (CSP)-generated energy landscapes. This uses an unsupervised clustering approach analyze independent...
A workflow for the digital design of crystallization processes starting from chemical structure active pharmaceutical ingredient (API) is a multi-step, multi-disciplinary process. simple version would be to first predict API crystal and it corresponding properties solubility, morphology, growth rates, assume that nucleation controlled by seeding, then use these parameters This usually an over-simplification as most APIs are polymorphic, stable alone may not have required development into...
Reduction of a large dataset computationally predicted structures ibuprofen by employing molecular dynamics and biased simulations at finite temperature pressure.
<p>Crystal structure prediction methods are prone to overestimate the number of potential polymorphs organic molecules. In this work, we aim reduce overprediction by systematically applying molecular dynamics simulations and biased sampling cluster subsets structures that can easily interconvert at finite temperature pressure. Following approach, rationally predicted putative in CSP-generated crystal energy landscapes. This uses an unsupervised clustering approach analyze independent...
The control of the crystal form is a central issue in pharmaceutical industry. identification putative polymorphs through Crystal Structure Prediction (CSP) methods based on lattice energy calculations, which are known to significantly over-predict number plausible structures. A valuable tool reduce overprediction employ physics-based, dynamic simulations coalesce minima separated by small barriers into smaller more stable geometries once thermal effects introduced. Molecular dynamics and...
The molecular structures of the first and second generation sulflowers, sulflower persulfurated coronene (PSC), are remarkably similar: carbon ring decorated with sulfur atoms, without any additional moiety.
Crystal structure prediction methods are prone to overestimate the number of potential polymorphs organic molecules. In this work, we aim reduce overprediction by systematically applying molecular dynamics simulations and biased sampling cluster subsets structures that can easily interconvert at finite temperature pressure. Following approach, rationally predicted putative in CSP-generated crystal energy landscapes. This uses an unsupervised clustering approach analyze independent...
The molecular structures of the first and second generation Sulflowers, Sulflower Persulfurated Coronene (PSC), are remarkably similar: carbon ring decorated with sulfur atoms, without any additional moiety. However, their crystallisability is starkly different, Sulfower easily forming well-characterised crystals, but PSC only resulting in amorphous forms, despite extensive experimental efforts. Here this phenomenon investigated using Crystal Structure Prediction (CSP) methods to generate...
Since 1999 the Crystal Structure Prediction (CSP) blind tests, a community initiative currently coordinated by Cambridge Crystallographic Data Centre (CCDC), have provided state-of-the-art CSP methods with an opportunity to validate and benchmark methodologies against unpublished data, subsequent publications capturing developments made over years providing readers overview of available indevelopment [1][2][3][4][5][6].The 7 th test saw participation from 129 researchers belonging 28 groups...
Crystal structure prediction methods are prone to overestimate the number of potential polymorphs organic molecules. In this work, we aim reduce overprediction by systematically applying molecular dynamics simulations and biased sampling cluster subsets structures that can easily interconvert at finite temperature pressure. Following approach, rationally predicted putative in CSP-generated crystal energy landscapes. This uses an unsupervised clustering approach analyze independent...
<p>The control of the crystal form is a central issue in pharmaceutical industry. The identification putative polymorphs through Crystal Structure Prediction (CSP) methods based on lattice energy calculations, which are known to significantly over-predict number plausible structures. A valuable tool reduce overprediction employ physics-based, dynamic simulations coalesce minima separated by small barriers into smaller more stable geometries once thermal effects introduced. Molecular...