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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.