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Cited by 450

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

Targeting proteins with metal complexes

Meggers, Eric·2009·Chemical Communications

Abstract

Unique properties of metal complexes, such as structural diversity, adjustable ligand exchange kinetics, fine-tuned redox activities, and distinct spectroscopic signatures, make them exciting scaffolds not only for binding to nucleic acids but increasingly also to proteins as non-traditional targets. This feature article discusses recent trends in this field. These include the use of chemically inert metal complexes as structural scaffolds for the design of enzyme inhibitors, new strategies for inducing selective coordination chemistry at the protein binding site, recent advances in the development of catalytic enzyme inhibitors, and the design of metal complexes that can inject electrons or holes into redox enzymes. A common theme in many of the discussed examples is that binding selectivity is at least in part achieved through weak interactions between the ligand sphere and the protein binding site. These examples hint to an exciting future in which "organic-like" molecular recognition principles are combined with properties that are unique to metals and thus promise to yield compounds with novel and unprecedented properties.

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Metadata

Authors
Meggers, E.
Venue
Chemical Communications
Published
2009
Issue
9
Pages
1001
ISSN
  • 1359-7345 (ISSN-L)
  • 1364-548X
OpenAlex
W2072961231
Cited by
450
References
57
FWCI
15.66
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Targeting proteins with metal complexes. https://app.cochat.ai/papers/10.1039/b813568a
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Meggers, E. (2009). Targeting proteins with metal complexes. Chemical Communications. https://doi.org/10.1039/b813568a

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.