The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Docking and scoring with ICM: the benchmarking results and strategies for improvement
Open accessAbstract
Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC = 82.2 and ROC(2%) = 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.
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Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
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
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Self-citationMoitessier, N., Englebienne, P., Lee, D., Lawandi, J., & Corbeil, C. R. (2008) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707515 · Cited by 514
Cited by
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412
Gentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F., Stern, A., & Cherkasov, A. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397
Boosting docking-based virtual screening with deep learning.
Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395
Adaptation of the human aryl hydrocarbon receptor to sense microbiota-derived indoles
Hubbard, T. D., Murray, I. A., Bisson, W. H., Lahoti, T. S., Gowda, K., Amin, S. G., Patterson, A. D., & Perdew, G. H. (2015) · Scientific Reports · doi:10.1038/srep12689 · Cited by 377
Schneider, N., Lange, G., Hindle, S., Klein, R., & Rarey, M. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9626-2 · Cited by 306
Metadata
- Authors
- Neves, M. A. C., Totrov, M., & Abagyan, R.
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2012-05-09
- Volume
- 26
- Issue
- 6
- Pages
- 675-686
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- OpenAlex
- W1988744515
- Cited by
- 371
- References
- 4
- FWCI
- 22.40
- Type
- journal article
- Language
- en
- Open access
- green
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