Mayr, FabianFabianMayrMöller, GabrieleGabrieleMöllerGarscha, UlrikeUlrikeGarschaFischer, JanaJanaFischerRodríguez Castaño, PatriciaPatriciaRodríguez CastañoInderbinen, Silvia G.Silvia G.InderbinenTemml, VeronikaVeronikaTemmlWaltenberger, BirgitBirgitWaltenbergerSchwaiger, StefanStefanSchwaigerHartmann, Rolf W.Rolf W.HartmannGege, ChristianChristianGegeMartens, StefanStefanMartensOdermatt, AlexAlexOdermattPandey, Amit VikramAmit VikramPandey0000-0001-8331-5902Werz, OliverOliverWerzAdamski, JerzyJerzyAdamskiStuppner, HermannHermannStuppnerSchuster, DanielaDanielaSchuster2024-10-052024-10-052020-09-26https://boris-portal.unibe.ch/handle/20.500.12422/55902Natural 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.en600 - Technology::610 - Medicine & health500 - Science::570 - Life sciences; biologyarticle10.48350/1503933299308410.3390/ijms21197102