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Cited by 820

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

From carbohydrate leads to glycomimetic drugs

Open access
Ernst, Beat, Magnani, John L.·2009·Nature Reviews Drug Discovery

Abstract

Carbohydrates are the most abundant natural products. Besides their role in metabolism and as structural building blocks, they are fundamental constituents of every cell surface, where they are involved in vital cellular recognition processes. Carbohydrates are a relatively untapped source of new drugs and therefore offer exciting new therapeutic opportunities. Advances in the functional understanding of carbohydrate–protein interactions have enabled the development of a new class of small-molecule drugs, known as glycomimetics. These compounds mimic the bioactive function of carbohydrates and address the drawbacks of carbohydrate leads, namely their low activity and insufficient drug-like properties. Here, we examine examples of approved carbohydrate-derived drugs, discuss the potential of carbohydrate-binding proteins as new drug targets (focusing on the lectin families) and consider ways to overcome the challenges of developing this unique class of novel therapeutics.

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Metadata

Authors
Ernst, B., & Magnani, J. L.
Venue
Nature Reviews Drug Discovery
Published
2009-07-24
Volume
8
Issue
8
Pages
661-677
ISSN
  • 1474-1776 (ISSN-L)
  • 1474-1784
OpenAlex
W2149170824
Cited by
820
References
7
FWCI
1.40
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: From carbohydrate leads to glycomimetic drugs. https://app.cochat.ai/papers/10.1038/nrd2852
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Ernst, B., & Magnani, J. L. (2009). From carbohydrate leads to glycomimetic drugs. Nature Reviews Drug Discovery. https://doi.org/10.1038/nrd2852

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.