Three-Dimensional Quantitative Similarity-Activity Relationships (3D QSiAR) from SEAL Similarity Matrices. Kubinyi, H., Hamprecht, F., & Mietzner, T. J.~Med.~Chem., 41(14):2553=-2564, 1998. ![Three-Dimensional Quantitative Similarity-Activity Relationships (3D QSiAR) from SEAL Similarity Matrices [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract: The program SEAL is suited to describe the electrostatic, steric, hydrophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calculated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similarity can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity matrix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set, the test set predictivities (r2pred = 0.53-0.84) of different PLS models, based on a weighted sum of molecular properties, are superior to published results of CoMFA and CoMSIA studies (r2pred = 0.31-0.40), regardless of whether a common alignment or individual, pairwise alignments of all molecules are used in the calculation of the similarity matrices. Training and test set selections have a significant influence on the external predictivities of the models. Although the SEAL similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantitative similarity-activity analyses (3D QSiAR) is proposed for approaches which correlate 3D structure-derived similarity matrices with biological activities.
@article{Kubinyi:1998aa,
Abstract = {Abstract: The program SEAL is suited to describe the electrostatic, steric, hydrophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calculated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similarity can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity matrix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set, the test set predictivities (r2pred = 0.53-0.84) of different PLS models, based on a weighted sum of molecular properties, are superior to published results of CoMFA and CoMSIA studies (r2pred = 0.31-0.40), regardless of whether a common alignment or individual, pairwise alignments of all molecules are used in the calculation of the similarity matrices. Training and test set selections have a significant influence on the external predictivities of the models. Although the SEAL similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantitative similarity-activity analyses (3D QSiAR) is proposed for approaches which correlate 3D structure-derived similarity matrices with biological activities.},
Author = {Kubinyi, H. and Hamprecht, F.A. and Mietzner, T.},
Date-Added = {2008-04-23 14:56:04 -0400},
Date-Modified = {2008-04-23 14:57:40 -0400},
Doi = {10.1021/jm970732a},
Journal = {J.~Med.~Chem.},
Keywords = {qsar; alignment; similarity; paradox},
Local-Url = {file://localhost/Users/rguha/Documents/articles/jm970732a.pdf},
Number = {14},
Pages = {2553=-2564},
Title = {Three-Dimensional Quantitative Similarity-Activity Relationships ({3D QSiAR}) from {SEAL} Similarity Matrices},
Url = {http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/jm970732a},
Volume = {41},
Year = {1998},
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