On the information content of 2D and 3D descriptors for QSAR
To gain better understanding on the information content of two-dimensional (2D) vs. three-dimensional (3D) descriptor systems, we analyzed principal component analysis scores derived from 87 2D descriptors and 798 3D (ALMOND) variables on a set of 5998 compounds of medicinal chemistry interest. The information overlap between ALMOND and 2D-based descriptors, as modeled by the fraction of explained variance (r²) and by seven-groups cross-validation (q²) in a two PLS components model was 40%. Individual component analysis indicates that the first and second principal components from the 2D-descriptors are related to the first and third dimensions from the ALMOND PCA model. The first ALMOND component is explained (61%) by size-related descriptors, whereas the third component is marginally explained (25%) by hydrophobicity-related descriptors. Surprisingly, 2D-based hydrogen-bonding descriptors did not contribute significantly in this analysis. These results do not a priori justify the choice of one methodology over the other, when performing QSAR studies.
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| Format: | Digital revista |
| Language: | English |
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Sociedade Brasileira de Química
2002
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532002000600013 |
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