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dc.contributor.authorMathai, Neann Sarah
dc.contributor.authorKirchmair, Johannes
dc.date.accessioned2021-02-25T12:09:27Z
dc.date.available2021-02-25T12:09:27Z
dc.date.created2020-06-04T11:09:48Z
dc.date.issued2020-05
dc.identifier.issn1422-0067
dc.identifier.urihttps://hdl.handle.net/11250/2730409
dc.description.abstractComputational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleSimilarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scopeen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright by the authorsen_US
dc.source.articlenumber3585
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.3390/ijms21103585
dc.identifier.cristin1813800
dc.source.journalInternational Journal of Molecular Sciencesen_US
dc.identifier.citationInternational Journal of Molecular Sciences. 2020, 21 (10), 3585.
dc.source.volume21en_US
dc.source.issue10en_US


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