News Picture Generic

Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation

September 24, 2024
Featured Article

Organic Chemistry

Organic cages can possess complex, functionalised internal cavities that make them promising candidates for synthetic enzyme mimics. Conformationally flexible but chemically robust structures are needed for adaptable guest binding and catalysis, but these rapidly exchanging systems are difficult to resolve in solution. Here, we use inexpensive calculations and high-throughput crystallisation experiments to identify accessible cage conformations for a recently reported organic cage by ‘locking’ them in the solid state. The conformers identified exhibit a range of distances between the carboxylic acid groups in the internal cavity, suggesting adaptability towards binding a wide array of target guest molecules. The complexity of the observed crystal structures goes beyond what is possible with state-of-the-art crystal structure prediction.

For details

Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation

C. E. Shields a, T. Fellowes a, A. G. Slater a, A. I. Cooper a, K. G. Andrews b, F. T. Szczypiński a

a. University of Liverpool
b. Durham University

DOI: https://10.26434/chemrxiv-2024-6cwvw

For more information about the used Chemspeed solutions:

FLEX ISYNTH

ISYNTH

Contact us to learn more about this exciting publication:

https://www.chemspeed.com/contact-us/

Other Recent News

Discover more news articles you might be interested in

Read more about Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography
News Picture 1 1 V2
Featured
Jan
6

Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography

The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances.

© Chemspeed Technologies 2026