Open Access

Fabian Mayr, Gabriele Möller, Ulrike Garscha, Jana Fischer, Patricia Rodríguez Castaño, Silvia G. Inderbinen, Veronika Temml, Birgit Waltenberger, Stefan Schwaiger, Rolf W. Hartmann, Christian Gege, Stefan Martens, Alex Odermatt, Amit V. Pandey, Oliver Werz, Jerzy Adamski, Hermann Stuppner, Daniela Schuster

• Natural 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 Int. J. Mol. Sci. 2020, 21, 7102; doi:10.3390/ijms21197102 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2020, 21, 7102 2 of 18 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.