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Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules

We accelerate a key step in crystal structure prediction (CSP) using machine learning and report its robustness on a wide array of pharmaceutical molecules. The speedup achieved by our scheme allows for a scale-up in both the number of candidate drug molecules studied and the level of theory employe...

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Bibliographic Details
Main Authors: Anelli, Andrea (Author) , Dietrich, Hanno (Author) , Ectors, Philipp (Author) , Stowasser, Frank (Author) , Bereau, Tristan (Author) , Neumann, Marcus (Author) , Ende, Joost van den (Author)
Format: Article (Journal)
Language:English
Published: 04 Oct 2024
In: CrystEngComm
Year: 2024, Volume: 26, Issue: 41, Pages: 5845-5849
ISSN:1466-8033
DOI:10.1039/D4CE00752B
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1039/D4CE00752B
Verlag, kostenfrei, Volltext: https://pubs.rsc.org/en/content/articlelanding/2024/ce/d4ce00752b
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Author Notes:Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann and Joost van den Ende
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Summary:We accelerate a key step in crystal structure prediction (CSP) using machine learning and report its robustness on a wide array of pharmaceutical molecules. The speedup achieved by our scheme allows for a scale-up in both the number of candidate drug molecules studied and the level of theory employed in their treatment, paving the way for tackling more complex crystal energy landscapes.
Item Description:Gesehen am 10.04.2025
Physical Description:Online Resource
ISSN:1466-8033
DOI:10.1039/D4CE00752B

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