The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise
Open accessAbstract
We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure-Activity Resource) 2010 data set, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands.
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Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
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
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
A semiempirical free energy force field with charge‐based desolvation
Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20634 · Cited by 2,246
Comparative evaluation of 11 scoring functions for molecular docking.
Wang, R., Lu, Y., & Wang, S. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0203783 · Cited by 849
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698
Binding MOAD (Mother Of All Databases)
Hu, L., Benson, M. L., Smith, R. D., Lerner, M. G., & Carlson, H. A. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20512 · Cited by 328
Cited by
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Protein–Ligand Scoring with Convolutional Neural Networks
Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 774
Self-citationTsubaki, M., Tomii, K., & Sese, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty535 · Cited by 688
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva-Jr, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 641
An open-source drug discovery platform enables ultra-large virtual screens
Gorgulla, C., Boeszoermenyi, A., Wang, Z.-F., Fischer, P. D., Coote, P. W., Padmanabha Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2117-z · Cited by 641
Metadata
- Authors
- Koes, D. R., Baumgartner, M. P., & Camacho, C. J.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-02-12
- Volume
- 53
- Issue
- 8
- Pages
- 1893-1904
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2028629022
- Cited by
- 1,136
- References
- 8
- FWCI
- 12.39
- Type
- journal article
- Language
- en
- Open access
- green
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