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Cited by 280

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Novel Chemical Space Exploration via Natural Products

Open access
Rosén, Josefin, Gottfries, Johan, Muresan, Sorel, Backlund, Anders, Oprea, Tudor I.·2009·Journal of Medicinal Chemistry

Abstract

Natural products (NPs) are a rich source of novel compound classes and new drugs. In the present study we have used the chemical space navigation tool ChemGPS-NP to evaluate the chemical space occupancy by NPs and bioactive medicinal chemistry compounds from the database WOMBAT. The two sets differ notably in coverage of chemical space, and tangible leadlike NPs were found to cover regions of chemical space that lack representation in WOMBAT. Property based similarity calculations were performed to identify NP neighbors of approved drugs. Several of the NPs revealed by this method were confirmed to exhibit the same activity as their drug neighbors. The identification of leads from a NP starting point may prove a useful strategy for drug discovery in the search for novel leads with unique properties.

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Crossref · 10.1021/jm801514w

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Metadata

Authors
Rosén, J., Gottfries, J., Muresan, S., Backlund, A., & Oprea, T. I.
Venue
Journal of Medicinal Chemistry
Published
2009-03-06
Volume
52
Issue
7
Pages
1953-1962
ISSN
  • 0022-2623 (ISSN-L)
  • 1520-4804
OpenAlex
W1990555250
Cited by
280
References
38
FWCI
19.64
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Novel Chemical Space Exploration via Natural Products. https://app.cochat.ai/papers/10.1021/jm801514w
The paper
Rosén, J., Gottfries, J., Muresan, S., Backlund, A., & Oprea, T. I. (2009). Novel Chemical Space Exploration via Natural Products. Journal of Medicinal Chemistry. https://doi.org/10.1021/jm801514w

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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.