Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.
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.
Kubinyi
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Main Laboratory, BASF AG, D-67056 Ludwigshafen, Germany. hugo.kubinyi@msm.basf-ag.de
Hamprecht
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Mietzner
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D015195:Drug Design; D016018:Least-Squares Analysis; D008968:Molecular Conformation; D013256:Steroids / Q000737:chemistry / Q000378:metabolism; D013329:Structure-Activity Relationship; D014156:Transcortin / Q000378:metabolism
D013256:Steroids; D014156:Transcortin