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dc.contributor.authorMayr, Fabian
dc.contributor.authorMöller, Gabriele
dc.contributor.authorGarscha, Ulrike
dc.contributor.authorFischer, Jana
dc.contributor.authorRodríguez Castaño, Patricia
dc.contributor.authorInderbinen, Silvia G
dc.contributor.authorTemml, Veronika
dc.contributor.authorWaltenberger, Birgit
dc.contributor.authorSchwaiger, Stefan
dc.contributor.authorHartmann, Rolf W
dc.contributor.authorGege, Christian
dc.contributor.authorMartens, Stefan
dc.contributor.authorOdermatt, Alex
dc.contributor.authorPandey, Amit V
dc.contributor.authorWerz, Oliver
dc.contributor.authorAdamski, Jerzy
dc.contributor.authorStuppner, Hermann
dc.contributor.authorSchuster, Daniela
dc.date.accessioned2020-11-06T10:53:51Z
dc.date.available2020-11-06T10:53:51Z
dc.date.issued2020-09-26
dc.identifier.citationInt J Mol Sci. 2020 Sep 26;21(19):7102. doi: 10.3390/ijms21197102.en_US
dc.identifier.pmid32993084
dc.identifier.doi10.3390/ijms21197102
dc.identifier.urihttp://hdl.handle.net/10033/622559
dc.description.abstractNatural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectSEAen_US
dc.subjectSuperPreden_US
dc.subjectSwissTargetPredictionen_US
dc.subjectdihydrochalconesen_US
dc.subjectin silico target predictionen_US
dc.subjectpolypharmacologyen_US
dc.subjectvirtual screeningen_US
dc.titleFinding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction.en_US
dc.typeArticleen_US
dc.identifier.eissn1422-0067
dc.contributor.departmentHIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany.en_US
dc.identifier.journalInternational journal of molecular sciencesen_US
dc.source.volume21
dc.source.issue19
refterms.dateFOA2020-11-06T10:53:52Z
dc.source.journaltitleInternational journal of molecular sciences
dc.source.countrySwitzerland


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International