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Cited by 267

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Structure-based inhibitor design by using protein models for the development of antiparasitic agents.

Open access
Ring, C S, Sun, E, McKerrow, J H, Lee, G K, Rosenthal, P J, Kuntz, I D, Cohen, F E·1993·Proceedings of the National Academy of Sciences

Abstract

The lack of an experimentally determined structure of a target protein frequently limits the application of structure-based drug design methods. In an effort to overcome this limitation, we have investigated the use of computer model-built structures for the identification of previously unknown inhibitors of enzymes from two major protease families, serine and cysteine proteases. We have successfully used our model-built structures to identify computationally and to confirm experimentally the activity of nonpeptidic inhibitors directed against important enzymes in the schistosome [2-(4-methoxybenzoyl)-1-naphthoic acid, Ki = 3 microM] and malaria (oxalic bis[(2-hydroxy-1-naphthylmethylene)hydrazide], IC50 = 6 microM) parasite life cycles.

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Crossref · 10.1073/pnas.90.8.3583

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Metadata

Authors
Ring, C. S., Sun, E., McKerrow, J. H., Lee, G. K., Rosenthal, P. J., Kuntz, I. D., & Cohen, F. E.
Venue
Proceedings of the National Academy of Sciences
Published
1993-04-15
Volume
90
Issue
8
Pages
3583-3587
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1993418211
Cited by
267
References
2
FWCI
27.10
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Structure-based inhibitor design by using protein models for the development of antiparasitic agents. https://app.cochat.ai/papers/10.1073/pnas.90.8.3583
The paper
Ring, C. S., Sun, E., McKerrow, J. H., Lee, G. K., Rosenthal, P. J., Kuntz, I. D., & Cohen, F. E. (1993). Structure-based inhibitor design by using protein models for the development of antiparasitic agents. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.90.8.3583

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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.