Constructing Optimum Blood Brain Barrier QSAR Models Using a Combination of 4D-Molecular Similarity Measures and Cluster Analysis
A new method, using a combination of 4D-molecular similarity measures and cluster analysis to construct optimum QSAR models, is applied to a data set of 150 chemically diverse compounds to build optimum blood-brain barrier (BBB) penetration models. The complete data set is divided into subsets based...
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Published in | Journal of Chemical Information and Computer Sciences Vol. 44; no. 6; pp. 2083 - 2098 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Chemical Society
01.11.2004
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Subjects | |
Online Access | Get full text |
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Summary: | A new method, using a combination of 4D-molecular similarity measures and cluster analysis to construct optimum QSAR models, is applied to a data set of 150 chemically diverse compounds to build optimum blood-brain barrier (BBB) penetration models. The complete data set is divided into subsets based on 4D-molecular similarity measures using cluster analysis. The compounds in each cluster subset are further divided into a training set and a test set. Predictive QASAR models are constructed for each cluster subset using the corresponding training sets. These QSAR models best predict test set compounds which are assigned to the same cluster subset, based on the 4D-molecular similarity measures, from which the models are derived. The results suggest that the specific properties governing blood-brain barrier permeability may vary across chemically diverse compounds. Partitioning compounds into chemically similar classes is essential to constructing predictive blood-brain barrier penetration models embedding the corresponding key physiochemical properties of a given chemical class. |
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Bibliography: | ark:/67375/TPS-93C652SJ-S istex:E17D8C293E821504CB5BEF7E1106A41E429D24D8 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0095-2338 1549-960X 1520-5142 |
DOI: | 10.1021/ci0498057 |