 Quantitative Structure-Activity Relationship
"Quantitative Structure-Activity Relationship" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
A quantitative prediction of the biological, ecotoxicological or pharmaceutical activity of a molecule. It is based upon structure and activity information gathered from a series of similar compounds.
| Descriptor ID |
D021281
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| MeSH Number(s) |
G02.111.830.500 G07.690.830.500
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| Concept/Terms |
Quantitative Structure-Activity Relationship- Quantitative Structure-Activity Relationship
- Quantitative Structure Activity Relationship
- Quantitative Structure-Activity Relationships
- Relationship, Quantitative Structure-Activity
- Relationships, Quantitative Structure-Activity
- Structure-Activity Relationship, Quantitative
- Structure-Activity Relationships, Quantitative
- Structure Activity Relationship, Quantitative
- QSAR
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Below are MeSH descriptors whose meaning is more general than "Quantitative Structure-Activity Relationship".
Below are MeSH descriptors whose meaning is more specific than "Quantitative Structure-Activity Relationship".
This graph shows the total number of publications written about "Quantitative Structure-Activity Relationship" by people in UAMS Profiles by year, and whether "Quantitative Structure-Activity Relationship" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2017 | 2 | 0 | 2 | | 2015 | 0 | 2 | 2 | | 2014 | 1 | 0 | 1 | | 2013 | 1 | 0 | 1 | | 2012 | 0 | 1 | 1 | | 2009 | 1 | 0 | 1 | | 2008 | 3 | 0 | 3 | | 2007 | 0 | 1 | 1 | | 2006 | 0 | 2 | 2 | | 2005 | 1 | 1 | 2 | | 2004 | 0 | 1 | 1 | | 2002 | 0 | 1 | 1 | | 2001 | 0 | 2 | 2 | | 2000 | 1 | 0 | 1 |
To return to the timeline, click here.
Below are the most recent publications written about "Quantitative Structure-Activity Relationship" by people in Profiles over the past ten years.
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Uchida T, Wakasugi M, Kitamura T, Yamamoto T, Asakura M, Fujiwara R, Itoh T, Fujii H, Hirono S. Exploration of DPP-IV inhibitors with a novel scaffold by multistep in silico screening. J Mol Graph Model. 2018 01; 79:254-263.
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Slavov S, Stoyanova-Slavova I, Li S, Zhao J, Huang R, Xia M, Beger R. Why are most phospholipidosis inducers also hERG blockers? Arch Toxicol. 2017 Dec; 91(12):3885-3895.
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Stoyanova-Slavova IB, Slavov SH, Buzatu DA, Beger RD, Wilkes JG. 3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification. J Mol Graph Model. 2017 03; 72:246-255.
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Hidayat AN, Aki-Yalcin E, Beksac M, Tian E, Usmani SZ, Ertan-Bolelli T, Yalcin I. Insight into human protease activated receptor-1 as anticancer target by molecular modelling. SAR QSAR Environ Res. 2015; 26(10):795-807.
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