Fast conditional density estimation for quantitative structure-activity relationships

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dc.contributor.authorBuchwald, Fabian
dc.contributor.authorGirschick, Tobias
dc.contributor.authorKramer, Stefan
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Atlanta, USAen_NZ
dc.date.accessioned2010-09-09T22:07:53Z
dc.date.available2010-09-09T22:07:53Z
dc.date.issued2010
dc.description.abstractMany 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.en_NZ
dc.description.urihttp://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1819
dc.format.mimetypeapplication/pdf
dc.identifier.citationBuchwald, 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.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/4561
dc.language.isoen
dc.publisherAssociation for the Advancement of Artificial Intelligence
dc.relation.isPartOfProc 24th AAAI Conference on Artificial Intelligenceen_NZ
dc.rightsThis 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.
dc.sourceAAAI-10en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectQSAR modellingen_NZ
dc.subjectMachine learning
dc.titleFast conditional density estimation for quantitative structure-activity relationshipsen_NZ
dc.typeConference Contributionen_NZ
pubs.begin-page1268en_NZ
pubs.elements-id19862
pubs.end-page1273en_NZ
pubs.finish-date2010-07-15en_NZ
pubs.place-of-publicationUSAen_NZ
pubs.start-date2010-07-11en_NZ
pubs.volume3en_NZ
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