The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Comparison of Topological, Shape, and Docking Methods in Virtual Screening
Abstract
Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D ligand similarity (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active and decoy compound sets were assembled from both the MDDR and the Merck compound databases. Averaged over multiple targets, ligand-based methods outperformed docking algorithms. This was true for 3D ligand-based methods only when chemical typing was included. Using mean enrichment factor as a performance metric, Glide appears to be the best docking method among the three with FRED a close second. Results for all virtual screening methods are database dependent and can vary greatly for particular targets.
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Cites
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057
Benchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318
Do Structurally Similar Molecules Have Similar Biological Activity?
Martin, Y. C., Kofron, J. L., & Traphagen, L. M. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020155c · Cited by 806
Comparative evaluation of eight docking tools for docking and virtual screening accuracy
Kellenberger, E., Rodrigo, J., Muller, P., & Rognan, D. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20149 · Cited by 577
Why do we need so many chemical similarity search methods?
Sheridan, R. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02411-x · Cited by 412
Self-citation
Cited by
Chen, Y.-C. (2015) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2014.12.001 · Cited by 611
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514
Recognizing Pitfalls in Virtual Screening: A Critical Review
Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, K., & Agrafiotis, D. K. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200528d · Cited by 438
Sastry, M., Lowrie, J. F., Dixon, S. L., & Sherman, W. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100062n · Cited by 332
Metadata
- Authors
- McGaughey, G. B., Sheridan, R. P., Bayly, C. I., Culberson, J. C., Kreatsoulas, C., Lindsley, S., Maiorov, V., Truchon, J.-F., & Cornell, W. D.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-06-26
- Volume
- 47
- Issue
- 4
- Pages
- 1504-1519
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2125792789
- Cited by
- 417
- References
- 5
- FWCI
- 35.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- McGaughey, G. B., Sheridan, R. P., Bayly, C. I., Culberson, J. C., Kreatsoulas, C., Lindsley, S., Maiorov, V., Truchon, J.-F., & Cornell, W. D. (2007). Comparison of Topological, Shape, and Docking Methods in Virtual Screening. Journal of Chemical Information and Modeling. https://doi.org/10.1021/ci700052x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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