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Conformal prediction to define applicability domain: A case study on predicting ER and AR binding
Swedish Toxicology Sciences Research Center, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.ORCID iD: 0000-0003-3107-331X
Department of Chemistry, Umeå University, Umeå, Sweden.
Department of Chemistry, Umeå University, Umeå, Sweden.
2016 (English)In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 27, no 4, p. 303-316Article in journal (Refereed) Published
Abstract [en]

A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.

Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 27, no 4, p. 303-316
Keywords [en]
Conformal prediction, oestrogen receptor, androgen receptor, random forest, signature descriptors
National Category
Computer and Information Sciences Bioinformatics (Computational Biology) Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:oru:diva-83049DOI: 10.1080/1062936X.2016.1172665ISI: 000375443100001PubMedID: 27088868Scopus ID: 2-s2.0-84963808622OAI: oai:DiVA.org:oru-83049DiVA, id: diva2:1439336
Funder
Swedish Research Council, K2010-79X-21367-01-3Available from: 2016-06-20 Created: 2020-06-12 Last updated: 2020-07-17Bibliographically approved

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Norinder, Ulf

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