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Cited by 224

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Structure-Based Design: Potent Inhibitors of Human Brain Memapsin 2 (β-Secretase)

Open access
Ghosh, Arun K., Bilcer, Geoffrey, Harwood, Cynthia, Kawahama, Reiko, Shin, Dongwoo, Hussain, Khaja Azhar, Hong, Lin, Loy, Jeffrey A., Nguyen, Chan, Koelsch, Gerald, Ermolieff, Jacques, Tang, Jordan·2001·Journal of Medicinal Chemistry

Abstract

Memapsin 2 (beta-secretase) is one of two proteases that cleave the beta-amyloid precursor protein (APP) to produce the 40-42 residue amyloid-beta peptide (Abeta) in the human brain, a key event in the progression of Alzheimer's disease. On the basis of the X-ray crystal structure of our lead inhibitor (2, OM99-2 with eight residues) bound to memapsin, we have reduced the molecular weight and designed potent memapsin inhibitors. Structure-based design and preliminary structure-activity studies have been presented.

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Metadata

Authors
Ghosh, A. K., Bilcer, G., Harwood, C., Kawahama, R., Shin, D., Hussain, K. A., Hong, L., Loy, J. A., Nguyen, C., Koelsch, G., Ermolieff, J., & Tang, J.
Venue
Journal of Medicinal Chemistry
Published
2001-08-01
Volume
44
Issue
18
Pages
2865-2868
ISSN
  • 0022-2623 (ISSN-L)
  • 1520-4804
OpenAlex
W2016584130
Cited by
224
References
1
FWCI
10.50
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Structure-Based Design: Potent Inhibitors of Human Brain Memapsin 2 (β-Secretase). https://app.cochat.ai/papers/10.1021/jm0101803
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Ghosh, A. K., Bilcer, G., Harwood, C., Kawahama, R., Shin, D., Hussain, K. A., Hong, L., Loy, J. A., Nguyen, C., Koelsch, G., Ermolieff, J., & Tang, J. (2001). Structure-Based Design: Potent Inhibitors of Human Brain Memapsin 2 (β-Secretase). Journal of Medicinal Chemistry. https://doi.org/10.1021/jm0101803

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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.