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Conformal prediction of HDAC inhibitors
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
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Number of Authors: 52019 (English)In: SAR and QSAR in environmental research (Print), ISSN 1062-936X, E-ISSN 1029-046X, Vol. 30, no 4, p. 265-277Article in journal (Refereed) Published
Abstract [en]

The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.

Place, publisher, year, edition, pages
2019. Vol. 30, no 4, p. 265-277
Keywords [en]
conformal prediction, epigenetic, HDAC, QSAR, RDKit descriptors, machine learning
National Category
Chemical Sciences Computer and Information Sciences Earth and Related Environmental Sciences Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-169052DOI: 10.1080/1062936X.2019.1591503ISI: 000465266700003PubMedID: 31012353OAI: oai:DiVA.org:su-169052DiVA, id: diva2:1318416
Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-05-27Bibliographically approved

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