Nonparametric Regression Applied to Quantitative Structure-Activity
Relationships
P. Constans and J. Hirst. Nonparametric Regression Applied to Quantitative
Structure-Activity Relationships. Journal of Chemical Information and Computer
Sciences, 40, 452-459 (2000).
Abstract
Several nonparametric regressors have been applied to modeling quantitative
structure-activity relationship (QSAR) data. The simplest regressor, the
Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors,
the local linear and the shifted Nadaraya-Watson, were implemented within additive
models -a computationally more expedient approach, better suited for low-density
designs. Performances were benchmarked against the nonlinear method of smoothing
splines. A linear reference point was provided by multilinear regression (MLR).
Variable selection was explored using systematic combinations of different variables
and combinations of principal components. For the data set examined, 47 inhibitors
of dopamine ß-hydroxylase, the additive nonparametric regressors have greater
predictive accuracy (as measured by the mean absolute error of the predictions or
the Pearson correlation in cross-validation trails) than MLR. The use of principal
components did not improve the performance of the nonparametric regressors over use
of the original descriptors, since the original descriptors are not strongly
correlated. It remains to be seen if the nonparametric regressors can be
successfully coupled with better variable selection and dimensionality reduction in
the context of high-dimensional QSARs.
Keywords
Molecular similarity-matrices, artificial neural networks, dopamine
ß-hydroxylase, receptor surface models, dihydrofolate-reductase, drug design,
variable selection, GA strategy, 3D QSAR, inhibitors.
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 Nonparametric Regression
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