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BREED: Generating Novel Inhibitors through Hybridization of Known Ligands. Application to CDK2, P38, and HIV Protease
Open accessAbstract
In this work we describe BREED, a method for the generation of novel inhibitors from structures of known ligands bound to a common target. The method is essentially an automation of the common medicinal chemistry practice of joining fragments of two known ligands to generate a new inhibitor. The ligand-bound target structures are overlaid, all overlapping bonds in all pairs of ligands are found, and the fragments on each side of each matching bond are swapped to generate the new molecules. Since the method is automated, it can be applied recursively to generate all possible combinations of known ligands. In an application of this method to HIV protease inhibitors and protein kinase inhibitors, hundreds of new molecular structures were generated. These included known inhibitor scaffolds not included in the initial set, entirely novel scaffolds, and novel substituents on known scaffolds. The method is fast, and since all of the ligand functional groups are known to bind the target in the precise position and orientation present in the novel ligand, the success rate of this method should be superior to more traditional de novo design techniques. In an era of increasingly high-throughput structural biology, such methods for high-throughput utilization of structural information will become increasingly valuable.
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Metadata
- Authors
- Pierce, A. C., Rao, G., & Bemis, G. W.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-04-20
- Volume
- 47
- Issue
- 11
- Pages
- 2768-2775
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2050458179
- Cited by
- 185
- References
- 0
- FWCI
- 5.78
- Type
- journal article
- Language
- en
- Open access
- green
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- Pierce, A. C., Rao, G., & Bemis, G. W. (2004). BREED: Generating Novel Inhibitors through Hybridization of Known Ligands. Application to CDK2, P38, and HIV Protease. Journal of Medicinal Chemistry. https://doi.org/10.1021/jm030543u
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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