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Conformal Regression for QSAR Modelling - Quantifying Prediction Uncertainty.
Cambridge University.
SweTox.
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2018 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the outputted prediction intervals to create as efficient (i.e. narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges and the different approaches were evaluated on 29 publicly available datasets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals. This approach afforded an average prediction range of 1.65 pIC50 units at the 80 % confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

Place, publisher, year, edition, pages
2018.
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:uu:diva-350011DOI: 10.1021/acs.jcim.8b00054PubMedID: 29701973OAI: oai:DiVA.org:uu-350011DiVA, id: diva2:1203242
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Swedish Research Council FormasSwedish Foundation for Strategic Research Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-05-02

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Spjuth, Ola

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Department of Pharmaceutical BiosciencesScience for Life Laboratory, SciLifeLab
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