The DOI matches its Crossref record.
Discovering High-Affinity Ligands for Proteins
Abstract
D rug discovery is a difficult and time-consuming process that involves the identification of potent therapeutic agents with good bioavailability, metabolic stability, and low toxicity. An important first step in this process is the identification of molecules that bind with high affinity and
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Citations
Cited by
Cohen, Y., Avram, L., & Frish, L. (2005) · Angewandte Chemie International Edition · doi:10.1002/anie.200300637 · Cited by 1,122
Druggability Indices for Protein Targets Derived from NMR-Based Screening Data
Hajduk, P. J., Huth, J. R., & Fesik, S. W. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049131r · Cited by 522
Self-citationCovalent modification as a strategy to block protein–protein interactions with small-molecule drugs
Way, J. (2000) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(99)00049-6 · Cited by 55
Metadata
- Authors
- Hajduk, P. J., Meadows, R. P., & Fesik, S. W.
- Venue
- Science
- Published
- 1997-10-17
- Volume
- 278
- Issue
- 5337
- Pages
- 497-499
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1554612821
- Cited by
- 236
- References
- 0
- FWCI
- 23.80
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Discovering High-Affinity Ligands for Proteins. https://app.cochat.ai/papers/10.1126/science.278.5337.497
- The paper
- Hajduk, P. J., Meadows, R. P., & Fesik, S. W. (1997). Discovering High-Affinity Ligands for Proteins. Science. https://doi.org/10.1126/science.278.5337.497
Data sources
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.
