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AAAI
2010

Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships

11 years 1 months ago
Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships
Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive prediction intervals of activities. In this paper, we experimentally evaluate and compare three methods for conditional density estimation for their suitability in QSAR modeling. In contrast to traditional methods for conditional density estimation, they are based on generic machine learning schemes, more specifically, class probability estimators. Our experiments show that a kernel estimator based on class probability estimates from a random forest classifier is highly competitive with Gaussian process regression, while taking only a fraction of the time for training. Therefore, generic machine-lea...
Fabian Buchwald, Tobias Girschick, Eibe Frank, Ste
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where AAAI
Authors Fabian Buchwald, Tobias Girschick, Eibe Frank, Stefan Kramer
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