Co‐crystal Prediction by Artificial Neural Networks **
Abstract: A significant amount of attention has been given to the design and synthesis of co‐crystals by both industry and academia because of its potential to change a molecule's physicochemical properties. Yet, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co‐crystals, hampering the efficient exploration of the target's solid‐state landscape. This paper reports on the application of a data‐driven co‐crystal prediction method based on two types of artificial neural network models and co‐crystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a co‐crystal is likely to form. By combining the output of multiple models of both types, our approach shows to have excellent performance on the proposed co‐crystal training and validation sets, and has an estimated accuracy of 80 % for molecules for which previous co‐crystallization data is unavailable.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Co‐crystal Prediction by Artificial Neural Networks ** ; volume:59 ; number:48 ; year:2020 ; pages:21711-21718 ; extent:8
Angewandte Chemie / International edition. International edition ; 59, Heft 48 (2020), 21711-21718 (gesamt 8)
- Creator
- DOI
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10.1002/anie.202009467
- URN
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urn:nbn:de:101:1-2022061411220479900663
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Devogelaer, Jan‐Joris
- Meekes, Hugo
- Tinnemans, Paul
- Vlieg, Elias
- de Gelder, René
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