The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment
Abstract
It has been reported recently that consensus scoring, which combines multiple scoring functions in binding affinity estimation, leads to higher hit-rates in virtual library screening studies. This method seems quite independent to the target receptor, the docking program, or even the scoring functions under investigation. Here we present an idealized computer experiment to explore how consensus scoring works. A hypothetical set of 5000 compounds is used to represent a chemical library under screening. The binding affinities of all its member compounds are assigned by mimicking a real situation. Based on the assumption that the error of a scoring function is a random number in a normal distribution, the predicted binding affinities were generated by adding such a random number to the "observed" binding affinities. The relationship between the hit-rates and the number of scoring functions employed in scoring was then investigated. The performance of several typical ranking strategies for a consensus scoring procedure was also explored. Our results demonstrate that consensus scoring outperforms any single scoring for a simple statistical reason: the mean value of repeated samplings tends to be closer to the true value. Our results also suggest that a moderate number of scoring functions, three or four, are sufficient for the purpose of consensus scoring. As for the ranking strategy, both the rank-by-number and the rank-by-rank strategy work more effectively than the rank-by-vote strategy.
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Cites
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Cited by
Similarity-based virtual screening using 2D fingerprints
Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849
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
Self-citationKey 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
Evaluation of Docking Performance: Comparative Data on Docking Algorithms
Kontoyianni, M., McClellan, L. M., & Sokol, G. S. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0302997 · Cited by 600
Huang, S.-Y., Grinter, S. Z., & Zou, X. (2010) · Physical Chemistry Chemical Physics · doi:10.1039/c0cp00151a · Cited by 508
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331
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Consensus scoring criteria for improving enrichment in virtual screening.
Yang, J.-M., Chen, Y.-F., Shen, T.-W., Kristal, B. S., & Hsu, D. F. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050034w · Cited by 241
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107
Systematic exploitation of multiple receptor conformations for virtual ligand screening.
Bottegoni, G., Rocchia, W., Rueda, M., Abagyan, R., & Cavalli, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018845 · Cited by 96
GFscore: a general nonlinear consensus scoring function for high-throughput docking.
Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., & Morelli, X. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0600758 · Cited by 51
Metadata
- Authors
- Wang, R., & Wang, S.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-07-17
- Volume
- 41
- Issue
- 5
- Pages
- 1422-1426
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2030686695
- Cited by
- 311
- References
- 3
- FWCI
- 16.89
- Type
- journal article
- Language
- en
- Open access
- closed
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