On Outliers and Activity CliffsWhy QSAR Often Disappoints
Abstract
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTOn Outliers and Activity CliffsWhy QSAR Often DisappointsGerald M. MaggioraView Author Information University of Arizona Tucson, ArizonaCite this: J. Chem. Inf. Model. 2006, 46, 4, 1535Publication Date (Web):July 24, 2006Publication History Published online24 July 2006Published inissue 1 July 2006https://pubs.acs.org/doi/10.1021/ci060117shttps://doi.org/10.1021/ci060117seditorialACS PublicationsCopyright © 2006 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views7967Altmetric-Citations561LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (13 KB) Get e-AlertscloseSUBJECTS:Assays,Bioinformatics and computational biology,Molecular modeling,Molecules,Structure activity relationship Get e-Alerts
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The existence of activity cliffs in compound data sets is a major factor limiting QSAR predictions, often much greater than intrinsic limitations of modeling.
“The existence of activity cliffs in compound data sets is a major factor limiting QSAR predictions, often much greater than intrinsic limitations of modeling.22”
From QSAR without borders (2020) · cites this paper as [22]
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- Authors
- Maggiora, G. M.
- Affiliations
- University of Arizona (US)
- Corresponding author
- Maggiora, Gerald M.
- ORCID
- Maggiora, Gerald M.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-07-01
- Volume
- 46
- Issue
- 4
- Pages
- 1535-1535
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2039609876
- Cited by
- 728
- References
- 0
- Citations by year
- 2026: 38 · 2025: 26 · 2024: 42 · 2023: 39 · 2022: 34 · 2021: 39 · 2020: 38 · 2019: 35 · 2018: 37 · 2017: 30 · 2016: 39 · 2015: 35 · 2014: 38 · 2013: 51 · 2012: 53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- outliers, activity cliffs, QSAR, structure-activity relationship, drug discovery, molecular modeling, bioassay data
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