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Cited by 4,495

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success

Lovering, Frank, Bikker, Jack, Humblet, Christine·2009·Journal of Medicinal Chemistry
DOI 10.1021/jm901241e
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Abstract

The medicinal chemistry community has become increasingly aware of the value of tracking calculated physical properties such as molecular weight, topological polar surface area, rotatable bonds, and hydrogen bond donors and acceptors. We hypothesized that the shift to high-throughput synthetic practices over the past decade may be another factor that may predispose molecules to fail by steering discovery efforts toward achiral, aromatic compounds. We have proposed two simple and interpretable measures of the complexity of molecules prepared as potential drug candidates. The first is carbon bond saturation as defined by fraction sp(3) (Fsp(3)) where Fsp(3) = (number of sp(3) hybridized carbons/total carbon count). The second is simply whether a chiral carbon exists in the molecule. We demonstrate that both complexity (as measured by Fsp(3)) and the presence of chiral centers correlate with success as compounds transition from discovery, through clinical testing, to drugs. In an attempt to explain these observations, we further demonstrate that saturation correlates with solubility, an experimental physical property important to success in the drug discovery setting.

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Metadata

Authors
Lovering, F., Bikker, J., & Humblet, C.
Venue
Journal of Medicinal Chemistry
Publisher
American Chemical Society (ACS)
Published
2009-10-14
Volume
52
Issue
21
Pages
6752-6756
ISSN
  • 0022-2623 (ISSN-L)
  • 1520-4804
DOI
10.1021/jm901241e
OpenAlex
W2074274985
Cited by
4,495
References
32
FWCI
54.72
Type
journal article
Language
en
Open access
closed

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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.