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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| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
04 Oct 2024
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| 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 
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| Verfasserangaben: | Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann and Joost van den Ende |
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| 520 | |a 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. | ||
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