The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Chemical Similarity Searching
Abstract
This paper reviews the use of similarity searching in chemical databases. It begins by introducing the concept of similarity searching, differentiating it from the more common substructure searching, and then discusses the current generation of fragment-based measures that are used for searching chemical structure databases. The next sections focus upon two of the principal characteristics of a similarity measure: the coefficient that is used to quantify the degree of structural resemblance between pairs of molecules and the structural representations that are used to characterize molecules that are being compared in a similarity calculation. New types of similarity measure are then compared with current approaches, and examples are given of several applications that are related to similarity searching.
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Citations
Cites
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
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Self-citationDo Structurally Similar Molecules Have Similar Biological Activity?
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Metadata
- Authors
- Willett, P., Barnard, J. M., & Downs, G. M.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1998-07-21
- Volume
- 38
- Issue
- 6
- Pages
- 983-996
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2096729078
- Cited by
- 1,765
- References
- 2
- FWCI
- 34.65
- Type
- journal article
- Language
- en
- Open access
- closed
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