The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Fragment-Based Drug Design: How Big Is Too Big?
Abstract
Much has been discussed about the proper physicochemical properties (e.g., molecular weight, hydrophobicity, etc.) that should be considered when utilizing fragment leads in drug design. However, little has been reported as to what emphasis, if any, should be placed on the potency of the resulting fragment leads. In this report, a retrospective analysis of 18 highly optimized inhibitors is described in which the compounds were systematically deconstructed until the minimal binding elements could be identified. An analysis of the potency changes that were observed as the leads were reduced in size indicate that a nearly linear relationship exists between molecular weight and binding affinity over the entire range of sizes and potencies represented in the dataset. On the basis of these observations, prediction maps can be constructed that enable critical and quantitative assessments of the process of lead identification and optimization. These data place well-defined limits on the ideal size and potency of fragment leads that are being considered for use in fragment-based drug design.
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Cites
Docking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,150
Drug-like properties and the causes of poor solubility and poor permeability
Lipinski, C. A. (2000) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/s1056-8719(00)00107-6 · Cited by 3,734
An inhibitor of Bcl-2 family proteins induces regression of solid tumours
Oltersdorf, T., Elmore, S. W., Shoemaker, A. R., Armstrong, R. C., Augeri, D. J., Belli, B. A., Bruncko, M., Deckwerth, T. L., Dinges, J., Hajduk, P. J., et al. (2005) · Nature · doi:10.1038/nature03579 · Cited by 3,416
Self-citationDiscovering High-Affinity Ligands for Proteins: SAR by NMR
Shuker, S. B., Hajduk, P. J., Meadows, R. P., & Fesik, S. W. (1996) · Science · doi:10.1126/science.274.5292.1531 · Cited by 2,041
Self-citationLigand efficiency: a useful metric for lead selection
Hopkins, A. L., Groom, C. R., & Alex, A. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03069-7 · Cited by 1,846
NMR Spectroscopy Techniques for Screening and Identifying Ligand Binding to Protein Receptors
Meyer, B., & Peters, T. (2003) · Angewandte Chemie International Edition · doi:10.1002/anie.200390233 · Cited by 1,039
The maximal affinity of ligands
Kuntz, I. D., Chen, K., Sharp, K. A., & Kollman, P. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.9997 · Cited by 849
A simple physical model for binding energy hot spots in protein–protein complexes
Kortemme, T., & Baker, D. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202485799 · Cited by 804
Rees, D. C., Congreve, Murray, C. W., & Carr, R. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1467 · Cited by 798
Is There a Difference between Leads and Drugs? A Historical Perspective
Oprea, T. I., Davis, A. M., Teague, S. J., & Leeson, P. D. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010366a · Cited by 769
Unraveling hot spots in binding interfaces: progress and challenges
DeLano, W. L. (2002) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(02)00283-x · Cited by 767
Erlanson, D. A., McDowell, R. S., & O'Brien, T. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm040031v · Cited by 642
Ligand efficiency indices as guideposts for drug discovery
Abad-Zapatero, C., & Metz, J. T. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(05)03386-6 · Cited by 527
Druggability Indices for Protein Targets Derived from NMR-Based Screening Data
Hajduk, P. J., Huth, J. R., & Fesik, S. W. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049131r · Cited by 522
Self-citationFragment-Based Lead Discovery Using X-ray Crystallography
Hartshorn, M. J., Murray, C. W., Cleasby, A., Frederickson, M., Tickle, I. J., & Jhoti, H. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm0495778 · Cited by 422
NMR screening techniques in drug discovery and drug design
Stockman, B. J., & Dalvit, C. (2002) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/s0079-6565(02)00049-3 · Cited by 227
Baurin, N., Aboul‐ela, F., Barril, X., Davis, B. J., Drysdale, M., Dymock, B. W., Finch, H., Fromont, C., Richardson, C. M., Simmonite, H., et al. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049806z · Cited by 135
Structure-based screening of low-affinity compounds
Carr, R. A. E., & Jhoti, H. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02245-6 · Cited by 112
Fragonomics: fragment-based drug discovery
Zartler, E. R., & Shapiro, M. J. (2005) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2005.05.002 · Cited by 104
Solution Nuclear Magnetic Resonance Spectroscopy Techniques for Probing Intermolecular Interactions
Pellecchia, M. (2005) · Chemistry & Biology · doi:10.1016/j.chembiol.2005.08.013 · Cited by 86
Magnitude of the Hydrophobic Effect at Central versus Peripheral Sites in Protein-Protein Interfaces
Li, Y., Huang, Y., Swaminathan, C. P., Smith‐Gill, S. J., & Mariuzza, R. A. (2005) · Structure · doi:10.1016/j.str.2004.12.012 · Cited by 65
Library design for NMR-based screening
Lepre, C. A. (2001) · Drug Discovery Today · doi:10.1016/s1359-6446(00)01616-0 · Cited by 62
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Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success
Lovering, F., Bikker, J., & Humblet, C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,495
The resurgence of covalent drugs
Singh, J., Petter, R. C., Baillie, T. A., & Whitty, A. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3410 · Cited by 1,873
The Moderately Efficient Enzyme: Evolutionary and Physicochemical Trends Shaping Enzyme Parameters
Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., & Milo, R. (2011) · Biochemistry · doi:10.1021/bi2002289 · Cited by 1,050
Probing the links between in vitro potency, ADMET and physicochemical parameters
Gleeson, M. P., Hersey, A., Montanari, D., & Overington, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3367 · Cited by 503
Perspectives on NMR in drug discovery: a technique comes of age
Pellecchia, M., Bertini, I., Cowburn, D., Dalvit, C., Giralt, E., Jahnke, W., James, T. L., Homans, S. W., Kessler, H., Luchinat, C., et al. (2008) · Nature Reviews Drug Discovery · doi:10.1038/nrd2606 · Cited by 414
Metadata
- Authors
- Hajduk, P. J.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-11-09
- Volume
- 49
- Issue
- 24
- Pages
- 6972-6976
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2299973047
- Cited by
- 265
- References
- 22
- FWCI
- 22.89
- Type
- journal article
- Language
- en
- Open access
- closed
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