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Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty
Department of Chemistry, University of Cambridge, Cambridge, England; IOTA Pharmaceut, Cambridge, England.
Department of Chemistry, University of Cambridge, Cambridge, England.
Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.ORCID iD: 0000-0003-3107-331X
Department of Chemistry, University of Cambridge, Cambridge, England.
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2018 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 58, no 5, p. 1132-1140Article in journal (Refereed) Published
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
Washington DC: American Chemical Society (ACS), 2018. Vol. 58, no 5, p. 1132-1140
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-83050DOI: 10.1021/acs.jcim.8b00054ISI: 000433634900021PubMedID: 29701973Scopus ID: 2-s2.0-85046534087OAI: oai:DiVA.org:oru-83050DiVA, id: diva2:1439338
Funder
Swedish Research Council FormasSwedish Foundation for Strategic Research Knut and Alice Wallenberg FoundationSwedish Research Council
Note

Ytterligare forskningsfinansiärer:

European Union's Framework Programme For Research and Innovation Horizon 2020 under the Marie Curie Sklodowska-Curi, Grant Number: 703543

ExCAPE project - European Unions Horizon 2020 Research and Innovation programm, Grant Number: 671555

Swedish Knowledge Foundation through the project Data Analytics for Research and Development, Grant Number: 20150185

Available from: 2018-05-02 Created: 2020-06-12 Last updated: 2024-01-16Bibliographically approved

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Norinder, UlfSpjuth, Ola

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