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
|
| MeSH Number(s) |
G02.111.830.500 G07.690.773.997.500
|
| 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
|
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 this website 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,
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| Year | Major Topic | Minor Topic | Total |
|---|
| 2006 | 1 | 0 | 1 |
| 2007 | 1 | 2 | 3 |
| 2009 | 0 | 1 | 1 |
| 2015 | 1 | 0 | 1 |
| 2017 | 0 | 1 | 1 |
| 2021 | 0 | 2 | 2 |
| 2022 | 0 | 1 | 1 |
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Below are the most recent publications written about "Quantitative Structure-Activity Relationship" by people in Profiles.
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Singh Y, Jaswal S, Singh S, Verma SK, Thareja S. Dual aromatase-steroid sulfatase inhibitors (DASI's) for the treatment of breast cancer: a structure guided ligand based designing approach. J Biomol Struct Dyn. 2023 12; 41(20):10604-10626.
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Banjare L, Singh Y, Verma SK, Singh AK, Kumar P, Kumar S, Jain AK, Thareja S. Multifaceted 3D-QSAR analysis for the identification of pharmacophoric features of biphenyl analogues as aromatase inhibitors. J Biomol Struct Dyn. 2023 03; 41(4):1322-1341.
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Mandavi S, Verma SK, Banjare L, Dubey A, Bhatt R, Thareja S, Jain AK. A Comprehension into Target Binding and Spatial Fingerprints of Noscapinoid Analogues as Inhibitors of Tubulin. Med Chem. 2021; 17(6):611-622.
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Verma SK, Thareja S. Structure based comprehensive modelling, spatial fingerprints mapping and ADME screening of curcumin analogues as novel ALR2 inhibitors. PLoS One. 2017; 12(4):e0175318.
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Thareja S, Rajpoot T, Verma SK. Generation of comparative pharmacophoric model for steroidal 5a-reductase I and II inhibitors: A 3D-QSAR study on 6-azasteroids. Steroids. 2015 Mar; 95:96-103.
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Jorissen RN, Reddy GS, Ali A, Altman MD, Chellappan S, Anjum SG, Tidor B, Schiffer CA, Rana TM, Gilson MK. Additivity in the analysis and design of HIV protease inhibitors. J Med Chem. 2009 Feb 12; 52(3):737-54.
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Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, Li ZR, Cao ZW, Ji ZL, Chen YZ. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Model. 2008 Jun; 26(8):1276-86.
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Li H, Yap CW, Ung CY, Xue Y, Li ZR, Han LY, Lin HH, Chen YZ. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J Pharm Sci. 2007 Nov; 96(11):2838-60.
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Lin HH, Han LY, Yap CW, Xue Y, Liu XH, Zhu F, Chen YZ. Prediction of factor Xa inhibitors by machine learning methods. J Mol Graph Model. 2007 Sep; 26(2):505-18.
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Wright AD, de Nys R, Angerhofer CK, Pezzuto JM, Gurrath M. Biological activities and 3D QSAR studies of a series of Delisea pulchra (cf. fimbriata) derived natural products. J Nat Prod. 2006 Aug; 69(8):1180-7.