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Cocrystals in the Cambridge Structural Database: a network approach.

Jan Joris Devogelaer1, Hugo Meekes1, Elias Vlieg1

  • 1Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, The Netherlands.

Acta Crystallographica Section B, Structural Science, Crystal Engineering and Materials
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PubMed
Summary
This summary is machine-generated.

Analyzing the Cambridge Structural Database (CSD) reveals coformer networks. This network analysis helps predict successful cocrystal formation by identifying complementary coformer pairs.

Keywords:
Cambridge Structural Databasecocrystal predictioncocrystallizationdata miningknowledge-based approachnetworks

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Area of Science:

  • Crystallography
  • Materials Science
  • Data Science

Background:

  • Cocrystal formation is crucial for drug development and materials science.
  • Predicting successful cocrystal formation requires understanding coformer interactions.
  • The Cambridge Structural Database (CSD) contains extensive cocrystal data.

Purpose of the Study:

  • To analyze the Cambridge Structural Database (CSD) using data mining and network science.
  • To understand which coformers are suitable for successful cocrystal formation.
  • To develop a knowledge-based approach for cocrystal prediction.

Main Methods:

  • Construction of a coformer network based on CSD entries.
  • Analysis of network properties to identify coformer clusters.
  • Extraction of clusters representing similar cocrystallization tendencies.

Main Results:

  • Coformer popularity in the CSD is unevenly distributed, creating a biased dataset.
  • The coformer network exhibits primarily bipartite behavior.
  • Complementary coformer combinations are essential for successful cocrystallization.

Conclusions:

  • The CSD coformer network is a valuable resource for cocrystal prediction.
  • Network analysis provides insights into coformer selection strategies.
  • This approach facilitates knowledge-based cocrystal design.