Quantitative structure-activity relationships and the unnamed science
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ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTQuantitative structure-activity relationships and the unnamed scienceCorwin HanschCite this: Acc. Chem. Res. 1993, 26, 4, 147–153Publication Date (Print):April 1, 1993Publication History Published online1 May 2002Published inissue 1 April 1993https://pubs.acs.org/doi/10.1021/ar00028a003https://doi.org/10.1021/ar00028a003research-articleACS PublicationsRequest reuse permissionsArticle Views615Altmetric-Citations148LEARN 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 Other access optionsGet e-Alertsclose Get e-Alerts
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Carbeck, J. D., Colton, I. J., Gao, J., & Whitesides, G. M. (1998) · Accounts of Chemical Research · doi:10.1021/ar970255q · Cited by 44 · see citing works
Comparative QSAR: On the Toxicology of the Phenolic OH Moiety
Garg, R., Kurup, A., & Hansch, C. H. (2001) · Critical Reviews in Toxicology · doi:10.1080/20014091111686 · Cited by 43 · see citing works
Self-citationQuantum chemical descriptors for linear solvation energy relationships
Lowrey, A. H., Cramer, C. J., Urban, J. J., & Famini, G. R. (1995) · Computers & Chemistry · doi:10.1016/0097-8485(94)00058-m · Cited by 42 · see citing works
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The use of artificial intelligence systems for predicting toxicity
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Computer-Aided Selection of Potential Antihypertensive Compounds with Dual Mechanism of Action
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Recanatini, M., Cavalli, A., & Hansch, C. H. (1997) · Chemico-Biological Interactions · doi:10.1016/s0009-2797(97)00047-1 · Cited by 39 · see citing works
Self-citationStępnik, K. E., & Kukuła‐Koch, W. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072534 · Cited by 37 · see citing works
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Janicka, M., Sztanke, M., & Sztanke, K. (2024) · Molecules · doi:10.3390/molecules29020287 · Cited by 35 · see citing works
QSAR treatment of multiple toxicities: the mutagenicity and cytotoxicity of quinolines
Smith, C. J., Hansch, C. H., & Morton, M. J. (1997) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/s0027-5107(97)00118-8 · Cited by 33 · see citing works
Self-citationJain, P., & Kumar, A. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp06611e · Cited by 32 · see citing works
In Silico Methods for Toxicity Prediction
Combes, R. D. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-3055-1_7 · Cited by 31 · see citing works
Some observations on classical QSAR
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Searching for allosteric effects via QSARs
Hansch, C. H., Garg, R., & Kurup, A. (2001) · Bioorganic & Medicinal Chemistry · doi:10.1016/s0968-0896(00)00248-0 · Cited by 30 · see citing works
Self-citationMolecular structure descriptors in the computer-aided design of biologically active compounds
Raevsky, O. A. (1999) · Russian Chemical Reviews · doi:10.1070/rc1999v068n06abeh000425 · Cited by 29 · see citing works
Edward, J. T., Chubb, F. L., & Sangster, J. M. (1997) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/y97-174 · Cited by 29 · see citing works
Qsar of P450 Oxidation: On the Value of ComparingkcatandKmwithKcat/Km
Gao, H., & Hansch, C. H. (1996) · Drug Metabolism Reviews · doi:10.3109/03602539608994017 · Cited by 29 · see citing works
Self-citationComparative molecular field analysis: CoMFA
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C-QSAR: a database of 18,000 QSARs and associated biological and physical data
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In silico evaluation of multispecies toxicity of natural compounds
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Quantitative Approaches to Structure–Activity Relationships
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3D QSAR Modeling in Drug Design
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Chemical–biological interactions in human
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Anwendung und Grenzen kristallographischer Daten im strukturbezogenen Liganden‐ und Wirkstoff‐Design
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Machine Learning to Predict Toxicity of Compounds
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Structure-activity models of chemical carcinogens: state of the art, and new directions.
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- Authors
- Hansch, C.
- Corresponding author
- Hansch, Corwin
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 1993-04-01
- Volume
- 26
- Issue
- 4
- Pages
- 147-153
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W1976406451
- Cited by
- 172
- References
- 50
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 5 · 2018: 4 · 2017: 5 · 2016: 3 · 2015: 2 · 2014: 1 · 2013: 3 · 2012: 5
- FWCI
- 20.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Accounts of Chemical Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- QSAR, cheminformatics, molecular descriptors, drug design
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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