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Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments
Abstract
A systematic virtual screening study on 11 pharmaceutically relevant targets has been conducted to investigate the interrelation between 8 two-dimensional (2D) fingerprinting methods, 13 atom-typing schemes, 13 bit scaling rules, and 12 similarity metrics using the new cheminformatics package Canvas. In total, 157 872 virtual screens were performed to assess the ability of each combination of parameters to identify actives in a database screen. In general, fingerprint methods, such as MOLPRINT2D, Radial, and Dendritic that encode information about local environment beyond simple linear paths outperformed other fingerprint methods. Atom-typing schemes with more specific information, such as Daylight, Mol2, and Carhart were generally superior to more generic atom-typing schemes. Enrichment factors across all targets were improved considerably with the best settings, although no single set of parameters performed optimally on all targets. The size of the addressable bit space for the fingerprints was also explored, and it was found to have a substantial impact on enrichments. Small bit spaces, such as 1024, resulted in many collisions and in a significant degradation in enrichments compared to larger bit spaces that avoid collisions.
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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
A Fast Flexible Docking Method using an Incremental Construction Algorithm
Rarey, M., Kramer, B., Lengauer, T., & Klebe, G. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0477 · Cited by 2,827
A geometric approach to macromolecule-ligand interactions
Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982) · Journal of Molecular Biology · doi:10.1016/0022-2836(82)90153-x · Cited by 2,278
Morgan, H. L. (1965) · Journal of Chemical Documentation · doi:10.1021/c160017a018 · Cited by 1,692
SMILES. 2. Algorithm for generation of unique SMILES notation
Weininger, D., Weininger, A., & Weininger, J. L. (1989) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00062a008 · Cited by 1,286
Atom pairs as molecular features in structure-activity studies: definition and applications
Carhart, R. E., Smith, D. H., & Venkataraghavan, R. (1985) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00046a002 · Cited by 1,066
Walters, W. P., Stahl, M. T., & Murcko, M. A. (1998) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01163-x · Cited by 991
Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
Truchon, J.-F., & Bayly, C. I. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600426e · Cited by 874
Similarity-based virtual screening using 2D fingerprints
Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849
Pharmacophore perception, development, and use in drug design
Güner, O. F. (2000) · Medical Entomology and Zoology · Cited by 689
Brown, R. D., & Martin, Y. C. (1996) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9501047 · Cited by 625
McGann, M. R., Almond, H. R., Nicholls, A., Grant, J. A., & Brown, F. K. (2003) · Biopolymers · doi:10.1002/bip.10207 · Cited by 491
Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484
Molecular Structure Description: The Electrotopological State
Kier, L. B., & Hall, L. H. (1999) · Medical Entomology and Zoology · Cited by 467
Comparison of Topological, Shape, and Docking Methods in Virtual Screening
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) · Journal of Chemical Information and Modeling · doi:10.1021/ci700052x · Cited by 417
The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding
Brown, R. D., & Martin, Y. C. (1997) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci960373c · Cited by 378
Nilakantan, R., Bauman, N., Dixon, J. S., & Venkataraghavan, R. (1987) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00054a008 · Cited by 366
On the Properties of Bit String-Based Measures of Chemical Similarity
Flower, D. R. (1998) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci970437z · Cited by 345
Feature trees: A new molecular similarity measure based on tree matching
Rarey, M., & Dixon, J. S. (1998) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008068904628 · Cited by 328
Ultrafast shape recognition to search compound databases for similar molecular shapes
Ballester, P. J., & Richards, W. G. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20681 · Cited by 327
Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0498719 · Cited by 322
Rogers, D., Brown, R. D., & Hahn, M. (2005) · SLAS Discovery · doi:10.1177/1087057105281365 · Cited by 296
Miller, M. D., Kearsley, S. K., Underwood, D. J., & Sheridan, R. P. (1994) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00119865 · Cited by 281
Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., & Schuffenhauer, A. (2004) · Organic & Biomolecular Chemistry · doi:10.1039/b409865j · Cited by 260
Matter, H. (1997) · Journal of Medicinal Chemistry · doi:10.1021/jm960352+ · Cited by 251
Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034207y · Cited by 246
Virtual Screening in Drug Discovery
(2005) · CRC Press eBooks · doi:10.1201/9781420028775 · Cited by 235
Chen, X., & Reynolds, C. H. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025531g · Cited by 191
Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design
McGregor, M. J., & Muskal, S. M. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980159j · Cited by 189
Virtual Screening for Bioactive Molecules
(2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613083 · Cited by 167
Three-Dimensional Pharmacophoric Pattern Searching
Gund, P. (1977) · Progress in molecular and subcellular biology · doi:10.1007/978-3-642-66626-1_4 · Cited by 156
Prospective Identification of Biologically Active Structures by Topomer Shape Similarity Searching
CRAMER, R. D. I., Poss, M. A., Hermsmeier, M. A., Caulfield, T. J., Kowala, M. C., & Valentine, M. T. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990159q · Cited by 104
One-Dimensional Molecular Representations and Similarity Calculations: Methodology and Validation
Dixon, S. L., & Merz, K. M. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm010137f · Cited by 100
Self-citationClark, D. E., Jones, G., Willett, P., Kenny, P. W., & Glen, R. C. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00017a026 · Cited by 91
Dixon, S. L., & Koehler, R. T. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm980708c · Cited by 71
Self-citationLingos, Finite State Machines, and Fast Similarity Searching
Grant, J., Haigh, J. A., Pickup, B. T., Nicholls, A. O., & Sayle, R. A. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci6002152 · Cited by 53
Baldi, P., Benz, R. W., Hirschberg, D. S., & Swamidass, S. J. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700200n · Cited by 51
Similarity Metrics and Descriptor Spaces – Which Combinations to Choose?
Glen, R. C., & Adams, S. (2006) · QSAR & Combinatorial Science · doi:10.1002/qsar.200610097 · Cited by 50
A Novel Shape-Feature Based Approach to Virtual Library Screening
Putta, S., Lemmen, C., Beroza, P., & Greene, J. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0255026 · Cited by 42
Güner, O. F., Henry, D. R., & Pearlman, R. S. (1992) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00005a017 · Cited by 38
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Paricharak, S., Cortés-Ciriano, I., IJzerman, A. P., Malliavin, T. E., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0063-9 · Cited by 38
Baselious, F., Hilscher, S., Robaa, D., Barinka, C., Schutkowski, M., & Sippl, W. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25021358 · Cited by 32
Metadata
- Authors
- Sastry, M., Lowrie, J. F., Dixon, S. L., & Sherman, W.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2010-05-07
- Volume
- 50
- Issue
- 5
- Pages
- 771-784
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W1974880836
- Cited by
- 332
- References
- 40
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- closed
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