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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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Bibliographische Detailangaben
Hauptverfasser: Anelli, Andrea (VerfasserIn) , Dietrich, Hanno (VerfasserIn) , Ectors, Philipp (VerfasserIn) , Stowasser, Frank (VerfasserIn) , Bereau, Tristan (VerfasserIn) , Neumann, Marcus (VerfasserIn) , Ende, Joost van den (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 04 Oct 2024
In: CrystEngComm
Year: 2024, Jahrgang: 26, Heft: 41, Pages: 5845-5849
ISSN:1466-8033
DOI:10.1039/D4CE00752B
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1039/D4CE00752B
Verlag, kostenfrei, Volltext: https://pubs.rsc.org/en/content/articlelanding/2024/ce/d4ce00752b
Volltext
Verfasserangaben:Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann and Joost van den Ende
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 10.04.2025
Beschreibung:Online Resource
ISSN:1466-8033
DOI:10.1039/D4CE00752B

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