Interaction Analysis using the Cambridge Structural Database - rapid access to intermolecular hydrogen-bond...

Area of Science:

• Crystallography
• Materials Science
• Computational Chemistry

Background:

• Coformer screening is crucial for developing new crystalline materials.
• Identifying effective coformers often relies on extensive experimental screening.
• Data-driven approaches can accelerate the discovery of novel coformers.

Purpose of the Study:

• To present a novel methodology for virtual coformer screening.
• To leverage hydrogen-bonding data for predicting coformer suitability.
• To provide a rapid and data-driven tool for coformer selection.

Main Methods:

• Utilized hydrogen-bonding interaction data from the Cambridge Structural Database (CSD).
• Developed a method to identify commonly interacting functional groups from a target molecule.
• Implemented the methodology as a script within the Mercury software.

Main Results:

• Successfully identified functional groups commonly involved in hydrogen bonding.
• Suggested appropriate coformers based on identified functional group interactions.
• Demonstrated a quick and data-driven approach to coformer selection.

Conclusions:

• The presented methodology offers an efficient virtual screening tool for coformer discovery.
• Leveraging CSD data enables a more informed and rapid selection of potential coformers.
• The Mercury script provides a practical implementation for researchers.