Multivariate QSAR
In this work, the chemometric techniques most frequently used in QSAR (quantitative structure-activity relationships) studies are reviewed. They are introduced in chronological order, beginning with Hansch analysis and the exploratory data analysis methods of principal components and hierarchical cl...
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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Química
2002-01-01
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| Series: | Journal of the Brazilian Chemical Society |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532002000600004 |
| _version_ | 1828841139476627456 |
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| author | Ferreira Márcia M. C. |
| author_facet | Ferreira Márcia M. C. |
| author_sort | Ferreira Márcia M. C. |
| collection | DOAJ |
| description | In this work, the chemometric techniques most frequently used in QSAR (quantitative structure-activity relationships) studies are reviewed. They are introduced in chronological order, beginning with Hansch analysis and the exploratory data analysis methods of principal components and hierarchical clustering (PCA and HCA). Principal component regression and partial least squares regression methods (PCR and PLS) are discussed, followed by the pattern recognition methods (KNN and SIMCA). Different applications are presented to illustrate these chemometric techniques. The methodology used for regression in 3D-QSAR is presented (unfolding PLS). Finally, the higher order method called Multilinear PLS, already used in analytical chemistry but not yet explored by the QSAR community, is introduced. This method maintains the multiway structure of the data and has several advantages over bilinear PLS including speed in calculation, simplicity and stability, since the number of parameters to be estimated can be greatly reduced. |
| first_indexed | 2024-12-12T19:57:06Z |
| format | Article |
| id | doaj.art-3d3ea39614f4462e801b832f4508b3d0 |
| institution | Directory Open Access Journal |
| issn | 0103-5053 |
| language | English |
| last_indexed | 2024-12-12T19:57:06Z |
| publishDate | 2002-01-01 |
| publisher | Sociedade Brasileira de Química |
| record_format | Article |
| series | Journal of the Brazilian Chemical Society |
| spelling | doaj.art-3d3ea39614f4462e801b832f4508b3d02022-12-22T00:13:50ZengSociedade Brasileira de QuímicaJournal of the Brazilian Chemical Society0103-50532002-01-01136742753Multivariate QSARFerreira Márcia M. C.In this work, the chemometric techniques most frequently used in QSAR (quantitative structure-activity relationships) studies are reviewed. They are introduced in chronological order, beginning with Hansch analysis and the exploratory data analysis methods of principal components and hierarchical clustering (PCA and HCA). Principal component regression and partial least squares regression methods (PCR and PLS) are discussed, followed by the pattern recognition methods (KNN and SIMCA). Different applications are presented to illustrate these chemometric techniques. The methodology used for regression in 3D-QSAR is presented (unfolding PLS). Finally, the higher order method called Multilinear PLS, already used in analytical chemistry but not yet explored by the QSAR community, is introduced. This method maintains the multiway structure of the data and has several advantages over bilinear PLS including speed in calculation, simplicity and stability, since the number of parameters to be estimated can be greatly reduced.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532002000600004chemometricsprincipal component analysispartial least squaresSIMCAKNN |
| spellingShingle | Ferreira Márcia M. C. Multivariate QSAR Journal of the Brazilian Chemical Society chemometrics principal component analysis partial least squares SIMCA KNN |
| title | Multivariate QSAR |
| title_full | Multivariate QSAR |
| title_fullStr | Multivariate QSAR |
| title_full_unstemmed | Multivariate QSAR |
| title_short | Multivariate QSAR |
| title_sort | multivariate qsar |
| topic | chemometrics principal component analysis partial least squares SIMCA KNN |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532002000600004 |
| work_keys_str_mv | AT ferreiramarciamc multivariateqsar |