Buchwald, F., Girschick, T., Kramer, S. & Frank, E. (2010). Fast conditional density estimation for quantitative structure-activity relationships. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, Georgia, USA, July 11-15, 2010.
Permanent Research Commons link: http://hdl.handle.net/10289/4561
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-learning based methods for conditional density estimation may be a good and fast option for quantifying uncertainty in QSAR modeling.
Association for the Advancement of Artificial Intelligence
This article has been published in the Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. ©2010 Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.