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FRED Pose Prediction and Virtual Screening Accuracy
Abstract
Results of a previous docking study are reanalyzed and extended to include results from the docking program FRED and a detailed statistical analysis of both structure reproduction and virtual screening results. FRED is run both in a traditional docking mode and in a hybrid mode that makes use of the structure of a bound ligand in addition to the protein structure to screen molecules. This analysis shows that most docking programs are effective overall but highly inconsistent, tending to do well on one system and poorly on the next. Comparing methods, the difference in mean performance on DUD is found to be statistically significant (95% confidence) 61% of the time when using a global enrichment metric (AUC). Early enrichment metrics are found to have relatively poor statistical power, with 0.5% early enrichment only able to distinguish methods to 95% confidence 14% of the time.
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Cites
Benchmarking sets for molecular docking.
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Metadata
- Authors
- McGann, M.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-02-16
- Volume
- 51
- Issue
- 3
- Pages
- 578-596
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2061583504
- Cited by
- 764
- References
- 1
- FWCI
- 29.60
- Type
- journal article
- Language
- en
- Open access
- closed
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