A Whole Output Strategy for Polymorph Screening:  Combining Crystal Structure Prediction, Graph Set Analysis, and Targeted Crystallization Experiments in the Case of Diflunisal

2003 ◽  
Vol 3 (2) ◽  
pp. 151-158 ◽  
Author(s):  
Wendy I. Cross ◽  
Nicholas Blagden ◽  
Roger J. Davey ◽  
Robin G. Pritchard ◽  
Marcus A. Neumann ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
Crystallization Experiments ◽  
Graph Set ◽  
Graph Set Analysis ◽  
Polymorph Screening

scholarly journals CRYSTAL STRUCTURE PREDICTION IN THE CONTEXT OF PHARMACEUTICAL POLYMORPH SCREENING AND PUTATIVE POLYMORPHS OF CIPROFLOXACIN

2017 ◽  
Vol 9 (4) ◽  
pp. 1
Author(s):  
Pawanpreet Singh ◽  
Renu Chadha
Keyword(s):  
Crystal Structure ◽  
Pharmaceutical Industry ◽  
Structure Prediction ◽  
Active Pharmaceutical Ingredient ◽  
Crystal Form ◽  
Crystal Structure Prediction ◽  
Drug Polymorphism ◽  
Computational Aspects ◽  
Polymorph Screening

Molecular simulation is increasingly used by medicinal chemists in the process and product development. Reliable computational predictions are of great value not only for the design of an active pharmaceutical ingredient with novel properties but also for the avoidance of an undesirable change of form in the late stages of development of an industrially important molecule. In the pharmaceutical industry, drug polymorphism can be a critical problem and is the subject of various regulatory considerations. This contribution tried to review the fuzzy frontiers between the chemical structure of the molecule and its crystal energy landscape with a particular focus on the crystal structure prediction (csp) methodology to complement polymorph screening. A detailed application of csp in the pharmaceutical industry is illustrated on ciprofloxacin; describing its putative polymorphs. This approach successfully identifies the known crystal form within this class, as well as a large number of other low-energy structures. The performance of the approach is discussed in terms of both the quality of the results and computational aspects. csp methods are now being used as part of the interdisciplinary range of studies to establish the range of solid forms of a molecule. Moreover, further methodological improvements aimed at increasing the accuracy of the predictions and at broadening the range of molecules i.e. cocrystals, salts and solvates.

scholarly journals Interpreting computed crystal energy landscapes for pharmaceutical molecules

2014 ◽  
Vol 70 (a1) ◽  
pp. C1615-C1615
Author(s):  
Sarah Price
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Pyridinecarboxylic Acid ◽  
Solid Form ◽  
Pharmaceutical Molecules ◽  
Polymorph Screening

Crystal Structure Prediction (CSP) algorithms aim to generate the thermodynamically feasible crystal structures of a molecule from the chemical diagram, ranking their relative stability by a necessarily approximate estimate of the crystal energy. Such calculations are becoming feasible for molecules of a size and flexibility of small molecule pharmaceuticals. Contrasting the crystal energy landscape, the computer generated structures that are thermodynamically plausible as polymorphs, with the results of experimental polymorph screening, shows that CSP studies are not limited to being a search for the most thermodynamically stable crystal structure but can play a valuable role in understanding polymorphism and the potential complexity of crystallisation behaviour.[1] This presentation will illustrate the use of CSP as a complement to industrial-type solid form screening activities. Examples will include olanzapine, [2] tazofelone, two closely related 5-HT2a agonists and 6-[(5-chloro-2-([(4-chloro-2-fluorophenyl)methyl]oxy)phenyl)methyl]-2-pyridinecarboxylic acid (GSK269984B).[3] This illustrates the use of the crystal energy landscape to understand disorder, help structurally characterise metastable polymorphs and suggest whether there are additional polymorphs to be targeted. Since crystal energy landscapes usually include a wider range of crystal structures than known polymorphs, it raises the scientific question as to what determines which structures can be observed as metastable polymorphs. Thus both scientific as well as technological challenges need to be overcome before we can predict polymorphs.

Crystal Structure Prediction via Deep Learning

2018 ◽  
Vol 140 (32) ◽  
pp. 10158-10168 ◽  
Author(s):  
Kevin Ryan ◽  
Jeff Lengyel ◽  
Michael Shatruk
Keyword(s):  
Crystal Structure ◽  
Deep Learning ◽  
Structure Prediction ◽  
Crystal Structure Prediction

scholarly journals New high-pressure phases of Fe7N3 and Fe7C3 stable at Earth's core conditions: evidences for carbon–nitrogen isomorphism in Fe-compounds

RSC Advances ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 3577-3581 ◽  
Author(s):  
Nursultan Sagatov ◽  
Pavel N. Gavryushkin ◽  
Talgat M. Inerbaev ◽  
Konstantin D. Litasov
Keyword(s):  
Crystal Structure ◽  
High Pressure ◽  
Ab Initio ◽  
Structure Prediction ◽  
Harmonic Approximation ◽  
Crystal Structure Prediction ◽  
Earth’S Core ◽  
Earth's Core ◽  
Core Conditions

We carried out ab initio calculations on the crystal structure prediction and determination of P–T diagrams within the quasi-harmonic approximation for Fe7N3 and Fe7C3.

scholarly journals Contact map based crystal structure prediction using global optimization

CrystEngComm ◽  
2021 ◽  
Author(s):  
Jianjun Hu ◽  
Wenhui Yang ◽  
Rongzhi Dong ◽  
Yuxin Li ◽  
Xiang Li ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Global Optimization ◽  
Crystal Engineering ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
New Materials ◽  
Contact Map

Crystal structure prediction is now playing an increasingly important role in the discovery of new materials or crystal engineering.

Rational design and crystal structure prediction of ring-fused double-PDI compounds as n-channel organic semiconductors: a DFT study

2021 ◽  
Author(s):  
Suryakanti Debata ◽  
Smruti R. Sahoo ◽  
Rudranarayan Khatua ◽  
Sridhar Sahu
Keyword(s):  
Crystal Structure ◽  
Charge Transport ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Rational Design ◽  
Molecular Design ◽  
Design Strategy ◽  
Dft Study ◽  
Crystal Structure Prediction ◽  
Model Compounds

In this study, we present an effective molecular design strategy to develop the n-type charge transport characteristics in organic semiconductors, using ring-fused double perylene diimides (DPDIs) as the model compounds.

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