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Cocrystals in the Cambridge Structural Database: a network approach
Author(s) -
Devogelaer Jan-Joris,
Meekes Hugo,
Vlieg Elias,
de Gelder René
Publication year - 2019
Publication title -
acta crystallographica section b
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.604
H-Index - 33
ISSN - 2052-5206
DOI - 10.1107/s2052520619004694
Subject(s) - cocrystal , computer science , network science , database , chemistry , organic chemistry , molecule , complex network , world wide web , hydrogen bond
To obtain a better understanding of which coformers to combine for the successful formation of a cocrystal, techniques from data mining and network science are used to analyze the data contained in the Cambridge Structural Database (CSD). A network of coformers is constructed based on cocrystal entries present in the CSD and its properties are analyzed. From this network, clusters of coformers with a similar tendency to form cocrystals are extracted. The popularity of the coformers in the CSD is unevenly distributed: a small group of coformers is responsible for most of the cocrystals, hence resulting in an inherently biased data set. The coformers in the network are found to behave primarily in a bipartite manner, demonstrating the importance of combining complementary coformers for successful cocrystallization. Based on our analysis, it is demonstrated that the CSD coformer network is a promising source of information for knowledge‐based cocrystal prediction.