Fabian Buchwald, Tobias Girschick, Eibe Frank, and Stefan Kramer (2010)
Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships
In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp. 1268-1273, AAAI Press.
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, in particular class probability estimators. Our experiments show that a kernel estimator based on class probability estimates from a random forest classifier performs as good or better as Gaussian Processes regression, while taking only a fraction of the time for training. Therefore, generic machine-learning based methods for conditional density estimation may be a good and fast option for quantifying opentox321uncertainty in QSAR modeling.