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Cited by 262

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Inhibition of protein–protein interactions using designed molecules

J. Wilson, Andrew·2009·Chemical Society Reviews

Abstract

Although many cellular processes depend upon enzymatic reactions, protein-protein interactions (PPIs) mediate a large number of important regulatory pathways and thus play a central role in disease development. In order to understand and selectively inhibit cellular signalling pathways, there is a pressing need for small molecules that target PPIs, particularly in the context of pharmaceutical development. This tutorial review will introduce the relevance of PPIs to chemical biology and highlight the key challenges in designing inhibitors. Some of the successes using conventional approaches to the identification of small-molecule PPI inhibitors will be highlighted, and also the reasons why these approaches have not always proven successful. Several general approaches tailored to particular protein topologies are emerging for the design of scaffolds that inhibit PPIs-these will form the major content of this review. Finally a summary of the challenges to be faced in developing inhibitors of PPIs into drug leads and how these challenges may differ from those encountered with enzyme-like targets will be given.

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Metadata

Authors
J. Wilson, A.
Venue
Chemical Society Reviews
Published
2009
Volume
38
Issue
12
Pages
3289
ISSN
  • 0306-0012 (ISSN-L)
  • 1460-4744
OpenAlex
W2105287154
Cited by
262
References
52
FWCI
1.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Inhibition of protein–protein interactions using designed molecules. https://app.cochat.ai/papers/10.1039/b807197g
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J. Wilson, A. (2009). Inhibition of protein–protein interactions using designed molecules. Chemical Society Reviews. https://doi.org/10.1039/b807197g

Data sources

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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.