Abstract
PoseBusters assesses molecular poses using steric and energetic criteria. We find that classical protein-ligand docking tools currently still outperform deep learning-based methods.
Keywords
Docking (animal)Computer scienceArtificial intelligenceAutoDockForce field (fiction)Python (programming language)Steric effectsMachine learningAlgorithmChemistryStereochemistry
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Publication Info
- Year
- 2023
- Type
- article
- Volume
- 15
- Issue
- 9
- Pages
- 3130-3139
- Citations
- 204
- Access
- Closed
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Cite This
Martin Buttenschoen,
Garrett M. Morris,
Charlotte M. Deane
(2023).
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.
Chemical Science
, 15
(9)
, 3130-3139.
https://doi.org/10.1039/d3sc04185a
Identifiers
- DOI
- 10.1039/d3sc04185a