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Cited by 773

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

Integrating Genetic Approaches into the Discovery of Anticancer Drugs

Hartwell, Leland H., Szankasi, Philippe, Roberts, Christopher J., Murray, Andrew W., Friend, Stephen H.·1997·Science

Abstract

The discovery of anticancer drugs is now driven by the numerous molecular alterations identified in tumor cells over the past decade. To exploit these alterations, it is necessary to understand how they define a molecular context that allows increased sensitivity to particular compounds. Traditional genetic approaches together with the new wealth of genomic information for both human and model organisms open up strategies by which drugs can be profiled for their ability to selectively kill cells in a molecular context that matches those found in tumors. Similarly, it may be possible to identify and validate new targets for drugs that would selectively kill tumor cells with a particular molecular context. This article outlines some of the ways that yeast genetics can be used to streamline anticancer drug discovery.

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Crossref · 10.1126/science.278.5340.1064

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Metadata

Authors
Hartwell, L. H., Szankasi, P., Roberts, C. J., Murray, A. W., & Friend, S. H.
Venue
Science
Published
1997-11-07
Volume
278
Issue
5340
Pages
1064-1068
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2124156743
Cited by
773
References
54
FWCI
17.22
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Integrating Genetic Approaches into the Discovery of Anticancer Drugs. https://app.cochat.ai/papers/10.1126/science.278.5340.1064
The paper
Hartwell, L. H., Szankasi, P., Roberts, C. J., Murray, A. W., & Friend, S. H. (1997). Integrating Genetic Approaches into the Discovery of Anticancer Drugs. Science. https://doi.org/10.1126/science.278.5340.1064

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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.